By Jordan Kretchmer, Senior Partner at Outlander VC
Robotics does not have a model problem nearly as much as it has an integration problem.
Yes, models are getting better. Vision-language models are improving. Teleoperation is improving. Haptics are improving. Dexterous manipulation is improving. Simulation is improving. Training pipelines are improving. Foundation models for robotics are improving. But almost every major piece of the stack is still being built as its own island.
That is why the industry keeps producing impressive technical breakthroughs without corresponding deployment scale. The components are getting better individually, but the system as a whole is still fragmented.
Robotics needs its missing protocol layer.
It needs an MCP for the physical world: a shared standard that lets perception, control, teleoperation, haptics, training, data, simulation, foundation models, and real machines all interoperate through a common interface. Until that exists, robotics will continue to advance in silos, and the cost of stitching everything together will keep slowing the entire industry down.
Right now, most robotics systems are assembled like custom projects, not interoperable products.
One company builds a tele-op layer. Another builds dexterous hands. Another builds foundation models. Another builds control software. Another builds haptic interfaces. Another builds simulation tooling. Another builds training infrastructure. Another builds robot arms, mobile bases, humanoids, drones, or underwater vehicles.
Each layer may be world-class. But the interfaces between them are usually bespoke.
A tele-op system has its own control API.
A hardware platform has its own command structure.
A manipulation stack has its own task representation.
A VLM has its own wrapper around perception and planning.
A dataset pipeline has its own logging schema.
A simulator has its own object definitions.
A human operator console has its own notion of intervention and override.
That means almost every serious deployment still requires custom glue code across the entire stack. This is a tax on the industry. And it is getting larger as the stack gets more powerful. The problem is no longer that we do not have enough interesting robotics technologies. The problem is that they do not compose.
The industry does not need one winning robot architecture. It needs a shared language. An MCP for the physical world would be a protocol layer that standardizes how robotic systems describe and exchange:
This is not glamorous, which is exactly why it matters.
Infrastructure layers are often less visible than the products they enable, but they are what allow ecosystems to form. In software, standards and protocols turned isolated systems into platforms. Robotics is approaching the point where it needs the same thing.
Without that layer, every robotics company is forced to behave like a vertically integrated island, even when the best future for the industry is modular.
This issue becomes far more urgent as robotics moves from tightly controlled industrial workflows into semi-structured and unstructured environments.
A robot in a fixed production cell can get away with a lot of custom engineering. The environment is constrained. Variability is low. The edge cases are limited. That is not the future most people are trying to build toward.
The real frontier is in environments like:
These environments require multiple modes of control and intelligence to work together in the same system:
The more open-ended the environment, the more important the integration layer becomes. A robot that cannot fluidly move between those modes is not really robust. It is just a demo that works when conditions are favorable.
The most obvious place to see the need for a shared protocol is manipulation. Manipulation is where the hardest problems pile on top of each other: perception, contact, uncertainty, dexterity, force control, recovery, human intervention, and training.
Take a warehouse picking robot.
Today, a real-world deployment might include:
Every integration point is fragile. Every handoff is custom. Every logging pipeline is inconsistent. Every training dataset requires cleanup and translation before it can be reused.
Now imagine a shared protocol layer in between.
The perception system publishes objects, poses, affordances, and uncertainty.
The robot publishes kinematics, end-effector state, control modes, force limits, fault states, and reachable workspace.
The task planner publishes goals, subtasks, constraints, and escalation logic.
The tele-op system can subscribe to the same state and take over only the part of the task that needs human help.
The training pipeline logs all of it in a standard format.
The simulator replays the same episode using the same definitions.
The foundation model reasons over the same task graph and state representation.
That is what a real interoperability layer would unlock: not just compatibility, but compounding.
A robot should be able to describe itself in a standardized way:
{
“robot_id”: “mm_warehouse_12”,
“platform_type”: “mobile_manipulator”,
“locomotion”: [“wheeled”],
“manipulators”: [
{
“arm_id”: “right_arm”,
“dof”: 7,
“payload_kg”: 10,
“reach_m”: 1.2
}
],
“end_effectors”: [
{
“type”: “parallel_gripper”,
“max_force_n”: 35,
“tactile_sensing”: true
}
],
“sensors”: [
“rgb_camera”,
“depth_camera”,
“wrist_force_torque”,
“joint_encoders”
],
“control_modes”: [
“joint_space”,
“cartesian_pose”,
“impedance”,
“shared_teleop”
]
}
A task should be able to arrive in a standard structure:
{
“task_id”: “pick_task_10027”,
“type”: “pick_and_place”,
“object”: {
“class”: “polybag”,
“sku”: “SKU-8821”,
“pose_estimate”: [0.31, 0.22, 0.14, 0.0, 1.57, 0.0],
“pose_confidence”: 0.74
},
“source”: “bin_B4”,
“target”: “tote_Z9”,
“constraints”: {
“max_grip_force_n”: 12,
“avoid_crushing”: true,
“time_limit_s”: 18
},
“fallback”: {
“teleop_allowed”: true,
“escalate_after_failures”: 2
}
}
A human handoff should be structured, not improvised:
{
“handoff”: {
“from”: “autonomy”,
“to”: “remote_operator”,
“scope”: [“wrist_rotation”, “grip_force”],
“keep_autonomous”: [“base_stability”, “collision_avoidance”],
“reason”: “low_confidence_final_grasp_alignment”,
“max_duration_s”: 10
}
}
A training episode should be logged in a reusable way:
{
“episode_id”: “ep_544002”,
“task_type”: “bin_pick”,
“observations”: {
“vision”: “uri://frames/ep_544002”,
“robot_state”: “uri://state/ep_544002”,
“force_torque”: “uri://ft/ep_544002”,
“tactile”: “uri://tactile/ep_544002”
},
“actions”: {
“policy_commands”: “uri://actions/ep_544002”,
“human_override_segments”: [
{
“start_ms”: 8210,
“end_ms”: 11520,
“operator_id”: “operator_4”
}
]
},
“outcome”: {
“success”: true,
“completion_time_ms”: 16310,
“recovery_used”: true
}
}
None of this is exotic. That is the point. It is basic infrastructure that should already exist in a standardized form.
Haptics is one of the clearest examples of a capability that should be part of the shared protocol layer.
Today, haptics is often treated as an accessory to teleoperation. But in a mature robotics stack, haptics should be much more than that.
It should be:
Imagine a remote operator guiding a robot through a delicate cable insertion task, a valve turn, a surgical motion, or a damaged-parts extraction. The operator’s force adjustments, hesitation, compensation, and contact patterns should not disappear into a closed loop. They should become structured data the whole system can use.
That is how tele-op becomes a path to autonomy rather than a parallel dead-end stack.
The industry often frames teleoperation as something autonomy will eventually replace. That is too simplistic.
Tele-op plays at least four durable roles in serious robotic deployments:
The strongest robotic systems will not choose between tele-op and autonomy. They will integrate both cleanly.
A shared protocol would let a tele-op system work across different robot platforms without requiring a rebuild every time. It would let remote operators use consistent abstractions for state, control authority, intervention, and safety constraints. It would let operator actions flow into standardized datasets that improve autonomy over time.
That is a much more powerful model than treating tele-op as just a labor layer attached to a brittle robot.
A lot of robotics commentary assumes that better models will naturally absorb the integration problem. They will not.
Foundation models and VLMs are useful only to the extent that they can interact with the physical world through reliable abstractions.
A model may understand that a tool is partially occluded, that a handle is probably graspable, or that a failed insertion likely requires a rotation before retrying. But unless the robot stack exposes standardized primitives around world state, action space, control modes, and uncertainty, the model is still trapped inside a custom wrapper for each deployment. That prevents real portability.
A shared protocol would let models do things like:
That is how model progress actually becomes deployment progress.
Although manipulation is the clearest starting point, this protocol layer matters across the full spectrum of robotic systems.
In drones, it would unify mission planning, autonomy, payload control, operator intervention, and perception outputs.
In autonomous ground systems, it would unify navigation, sensor fusion, tele-op fallback, and mission-level commands.
In maritime robotics, it would connect autonomy, sparse human supervision, degraded communications, and platform-agnostic mission control.
In industrial robotics, it would make higher-mix manufacturing more modular and less dependent on one-off integration.
In agriculture, it would connect mobility, sensing, actuation, and human supervision across many crop and task types.
In surgical or assistive robotics, it would unify haptics, supervision, safety logic, and precision manipulation interfaces.
The embodiments differ. The integration problem is the same.
A serious MCP layer for robotics should standardize the core primitives of interaction:
Capability discovery: What can this machine sense, reach, manipulate, carry, or tolerate safely?
World representation: What objects, humans, obstacles, surfaces, and affordances exist in the scene, and with what uncertainty?
Task specification: What is the goal, what are the subtasks, what constraints matter, and what counts as success?
Action abstraction: What actions can be requested at a high level, and how do they map to control modes?
Human intervention: How does the system request help, hand off control partially or fully, log intervention, and resume autonomy?
Feedback streams: How are vision, tactile, force, audio, and telemetry represented so they can be consumed across tools?
Safety semantics: What does degraded mode mean, when is intervention required, and how are confidence thresholds expressed?
Training and replay: How are trajectories, demonstrations, corrections, outcomes, and contexts logged for reuse?
Simulation portability: How do real and simulated episodes share common task and state representations?
That is the level where the industry needs convergence.
The best version of this protocol does not merely connect software modules. It becomes the common operating grammar of embodied intelligence. It allows a robot to learn from a human correction in one environment and reuse that lesson elsewhere.
It allows a foundation model to reason over a task in a platform-agnostic way.
It allows tele-op to become a scalable bridge to autonomy rather than a dead-end service layer.
It allows tactile, visual, and semantic information to be fused in reusable formats.
It allows heterogeneous machines (arms, drones, humanoids, AMRs, underwater vehicles, surgical systems, field robots) to participate in a broader ecosystem instead of living inside closed vertical stacks.
That is what turns robotics from a collection of bespoke systems into a true platform economy.
This is not just a technical standards conversation. It is an economic one.
A real interoperability layer would reduce the cost of deploying robotics by lowering the amount of custom integration required at every site and every workflow. It would reduce vendor lock-in. It would make it easier for customers to adopt best-of-breed systems instead of betting everything on one vertically integrated provider.
It would also make data more valuable. Right now, a huge amount of robotic training data is trapped inside proprietary schemas and deployment-specific stacks. Standardization would make more of that data reusable across tasks, sites, and even embodiments.
It would also accelerate the rate at which model improvements can propagate into the field. A better policy, planner, or reasoning system becomes more valuable when it can plug into many robotic environments instead of one.
That is how ecosystems scale.
The biggest long-term winners in robotics may not just be the companies with the best arm, the best hand, the best model, or the best tele-op interface. They may be the companies that help define the grammar that lets the rest of the industry interoperate.
Because robotics is reaching the stage where the bottleneck is no longer just “can this one thing work?” It is increasingly “can many things work together reliably enough to deploy at scale?”
That is an infrastructure question. And infrastructure questions tend to determine who compounds.
The robotics industry still tends to treat integration as downstream plumbing, something to solve after the exciting breakthroughs are built. That is backwards. Integration is not the boring part. It is the part that determines whether breakthroughs remain isolated or become systemic.
The next major leap in robotics may not come from a new model architecture or a novel end effector alone. It may come from the shared protocol layer that allows all of those advances to connect. That is the missing standard.
An MCP for the physical world.
Not because robotics needs less innovation, but because it needs a way for innovation to compound across the stack.
Are you building this? Apply now.
Original source here.
AUSTIN, Texas – Bravo Ordnance and NODA AI have established a strategic partnership focused on integrating advanced autonomous orchestration capabilities with scalable, mission-ready lethality solutions designed for the evolving demands of modern warfare.
The partnership brings together Bravo Ordnance’s rapidly deployable payload and munition systems with NODA’s platform- and hardware-agnostic orchestration technology, enabling autonomous systems to operate with greater adaptability and mission effectiveness across contested environments.
As unmanned systems continue to redefine the battlefield, the ability to rapidly integrate autonomous platforms, payloads, and tactical decision-making has become increasingly critical. Through this collaboration, Bravo and NODA aim to accelerate the operational capability of autonomous systems by combining scalable lethality with real-time horizontal autonomy.
NODA serves as the independent reasoning layer enabling operational and tactical employment of multi-domain autonomous systems from any manufacturer as a unified force. Assessed and selected within Department of War programs, NODA’s horizontal autonomy accelerates decision advantage by deploying foundational tactics and algorithms across mixed fleets. The solution uniquely integrates dozens of OEM platform autonomy technologies into a coordinated tapestry of combined effects—without requiring modifications to existing hardware or software.
Bravo Ordnance, recently selected as a winner of the Drone Dominance Program Lethality Prize Challenge, continues to develop adaptable and rapidly deployable payload systems designed for modern drone warfare and scalable autonomous operations.
“Our worldview of autonomy orchestration is much different than others. This partnership will illuminate our unique approach—the ability to tailor tactics and effects down to custom warheads,” said Philong Duong, CEO of NODA AI. “Effective JTACs of yesteryear knew the difference between N and K series Hellfires, and now we bring that similar dynamic calculus to the next fight.”
“This partnership represents the next phase of autonomous warfare capability,” said Devan Plantamura, CEO and Cofounder of Bravo Ordnance. “The future battlefield will depend on speed of iteration, manufacturing, adaptability, and coordinated lethal autonomous systems operating at scale. Integrating Bravo’s mission-ready warheads with NODA’s orchestration capability helps autonomous systems solve problems they cannot solve alone.”
The collaboration will focus on interoperability, scalable deployment, and supporting next-generation autonomous mission sets across defense applications.
ABOUT NODA AI
NODA is the independent reasoning layer that enables fully autonomous operations. It orchestrates the missions of any autonomous platform, from any manufacturer, across every domain simultaneously with tactics developed to employ high-performance autonomous platforms trained against real adversary capabilities. NODA’s orchestrator translates commander’s intent into coordinated action across the most capable vendor-agnostic autonomous platform network in defense.
Founded by Marine Corps veterans and AI practitioners, NODA integrates dozens of government and OEM autonomous systems from subsurface to space, with no onboard hardware and no manufacturer modifications required.
For more information, visit: https://www.nodaintelligence.ai
ABOUT BRAVO ORDNANCE
Bravo Ordnance develops advanced payload and lethality solutions designed for modern systems and rapidly evolving battlefield requirements. Focused on scalable manufacturing, modular integration, and operational adaptability, Bravo Ordnance builds mission-ready systems engineered for the speed and flexibility demanded by modern warfare.
For more information, visit: https://warhead.co
Original source linked here.
PROVIDENCE, R.I., May 12, 2026 /PRNewswire/ — Havoc, the all-domain collaborative autonomy company, today announced a $100 million Series A funding round, bringing the company’s total capital raised to ~$200 million since 2024. The round included participation from new investors CCM Capital Markets, Clear Street LLC, Cobalt Capital, Boardman Bay Capital Management, Meet Perry, Mute Ventures, Soren Ventures, SAIC, and JA Green. Existing investors included Outlander VC, Scout VC, B Capital, Lockheed Martin, Taiwania Capital, UP.Partners, and The Veteran Fund, alongside participation from Vanderbilt University’s endowment.
Defense technology is entering a new era where national security priorities are demanding unified, all-domain autonomy. The blueprint for building drones, boats, and vehicles exists. What is missing is the ability for thousands of autonomous assets to work together in a way that is coordinated, collaborative, scalable, and resilient. Havoc’s software-defined hardware approach is unlike anything on the market today, purpose-built to enable autonomous systems across sea, air, and land to operate together as a unified force.
“We built Havoc around a simple belief: the future of national security depends on collaborative autonomy that works in the real world, not in controlled demos or years from now. In less than two years, we’ve already built one of the most mature collaborative autonomy software stacks in the industry, operating across more than 100 air, surface, and ground platforms,” said Paul Lwin, CEO of Havoc. “Our autonomous platforms and command-and-control systems have already demonstrated that they provide warfighters meaningful capability in the exact environments where future conflicts will occur: contested, distributed, and communications-degraded environments. With this funding, we will accelerate deployment across every domain and prove that a single warfighter can task, monitor, and supervise thousands of heterogeneous autonomous systems working together as one force.”
Havoc Fast Facts
The Havoc Difference
Havoc’s software-defined hardware approach enables one-to-many, with a single operator supervising thousands of autonomous assets working together. The Havoc model provides immediate affordable mass by partnering with commercial manufacturers with existing excess capacity.
Havoc delivers real-time decision-making at the edge through all-domain collaborative autonomy, enabling persistent autonomous tasking. Autonomy at the edge fuses sensing, planning, and control, enabling heterogeneous assets to self-organize and execute complex missions with minimal supervision. The Havoc stack is modular and works with any platform or sensor to support autonomous navigation, dynamic path planning, and collision avoidance.
An intuitive, mission focused user interface enables a single operator to command, monitor, and re-task autonomous assets at the scale of thousands, reducing cognitive load and collapsing the distance between operator intent and execution.
“Havoc has done what very few companies in this space have managed,” said Will Graves, Chief Investment Officer at Boardman Bay Capital Management. “They’ve built a truly scalable collaborative autonomy platform that works across all domains, and the demand signal from the U.S. military speaks for itself. This is exactly the category of hard-tech, defense-critical infrastructure we’re eager to support.”
“Havoc is building foundational infrastructure for how autonomous systems will coordinate and act across every domain,” said Dan Abrams, Managing Partner at Cobalt Capital. “That’s a generational platform opportunity, and exactly the kind of category-defining company Cobalt looks to back. We’re proud to be partners and are incredibly excited about what the future holds for Havoc.”
About Havoc
Havoc is the leader in all-domain collaborative autonomy. Its software-defined hardware approach powers military and commercial-grade autonomous systems across sea, air, and land to sense, decide, and act together in complex and contested environments. Havoc connects assets, enabling them to share information, adapt in real time, and continue operating even when communications are disrupted or denied. Havoc optimizes mission performance and minimizes human risk. Learn more at havocai.com.
Original source here.
Autonomous laser defense company launches domestic production of high-power laser sources
SAN FRANCISCO–(BUSINESS WIRE)–Aurelius Systems, the autonomous laser defense company behind the Archimedes counter-UAS system, today announced Aurelius Manufacturing, a new division that will build high-power fiber laser source modules in the United States.
The U.S. defense laser supply chain has a gap. Demand for high-power fiber lasers is growing across military and industrial applications. A small number of established domestic manufacturers produce laser sources, but the market is shifting. Chinese laser companies have taken majority share in Asia Pacific, a region that accounts for nearly half the global fiber laser market, and are expanding into the U.S. through new automation products and service networks. For defense programs that need ITAR-compliant components from a supplier they can trace end to end, the pool of qualified domestic options is small and getting smaller relative to demand.
Aurelius set out to build Archimedes, its autonomous counter-UAS system, to give American forces scalable defense against drone threats. In doing so, the team found that domestic production of high-power fiber lasers has been shrinking for over a decade, with most remaining suppliers no longer American-owned. Aurelius Manufacturing is the company’s response: a U.S. production line for the same fiber laser source modules and components that sit at the heart of any directed-energy system, and that American manufacturers have had to import for years.
The launch comes as the Pentagon pushes to field laser weapons at scale within 36 months, backed by $250 million in directed energy R&D funding from the One Big Beautiful Bill. The Department of Defense’s fiscal year 2027 budget requested more than quadruples that figure, proposing over $2 billion in directed energy RDT&E. The Army’s Enduring High Energy Laser program is moving toward its first production contract, with plans to acquire up to 24 systems. Navy leadership has called for lasers on every ship in the surface fleet. As these programs move from prototyping into production, the number of domestic suppliers building defense-grade laser sources has not kept pace. Lead times from qualified vendors are long, and the industrial base needs more capacity.
Aurelius Manufacturing’s first product is a compact, rack-integrated fiber laser source module rated at multi-kilowatt output. Units will be available from prototype quantities through full-rate production, with configurations tailored to directed-energy and industrial manufacturing applications.
Aurelius’s laser sources are designed to be ITAR-compliant with full domestic traceability and no dependency on foreign allocation schedules. For industrial customers running laser welding, metal cutting, surface treatment, or additive manufacturing lines, domestic production will mean shorter lead times and direct access to the engineers building the hardware.
“It’s clear the domestic production of high-power lasers in the US is significantly lower than necessary to support both our directed energy and defense needs. Laser system production has been continually offshored outside of our lands. In order to support our customers, the directed energy industry at large and the growing material processing industry in the US, we’ll be vertically integrating and producing lasers here in the homeland,” said Michael Laframboise, CEO of Aurelius Systems.
Production capacity reservations for Q1 2027 are open. Customers can reach Aurelius at aureliusmanufacturing.com.
About Aurelius Systems
Aurelius Systems is a San Francisco-based defense technology company building autonomous laser systems. Its first product, Archimedes, is a counter-drone system designed to defeat Group 1 and 2 UAS threats. Through Aurelius Manufacturing, the company is building domestic production of high-power fiber laser sources for defense and industrial customers. All products are designed and built in the United States. For more information, visit aureliussystems.com.
In December 2024, as we sat in a multi-partner meeting with Phil Duong (co-founder/CEO at NODA), he told us he would “run through walls” to make this vision a reality. At the time, he had no revenue, no institutional backing, and only an early prototype.
We’d go on to be NODA’s first VC backer. Today, NODA AI announced a $25M Series A led by Bessemer Venture Partners, with Booz Allen Ventures, Draper Associates, Bloomberg Beta, and Alumni Ventures joining. We doubled down alongside Crosslink.
Here’s why we backed Phil and Dave at pre-seed, and why the thesis is playing out faster than anyone predicted.
The problem they understood better than anyone
The modern battlefield is full of autonomous systems that don’t talk to each other. Drones, USVs, UUVs, satellites, and unmanned ground vehicles sit inside their own vendor ecosystems, and commanders are left to coordinate mixed fleets by hand.
Today that means a few hundred autonomous assets in a single theater. Five years from now it’s tens of thousands. A decade out, hundreds of thousands to millions of unmanned systems operating across air, land, sea, subsea, and space simultaneously. No human commander, no strike cell, no battle staff can orchestrate that by hand. The force that wins is the one whose machines coordinate themselves.
NODA is building that coordination layer. A vendor-agnostic orchestration platform that sits on top of existing hardware and lets warfighters design, deploy, and adapt coordinated plays across an entire mixed fleet in near real time. As fleets scale from hundreds to millions, the orchestration layer is the constraint. NODA is building it now.
Founders, not metrics
Phil and Dave aren’t founders who stumbled into defense. Both served in the Marine Corps as Naval Aviators and Joint Terminal Attack Controller Instructors, the exact role that puts you at the seam of multi-domain coordination under fire. They left the service, went to C3.ai, where they helped build from zero to $100M, and won the largest contract in the company’s history, before leaving to found NODA.
When we met them, there was no traction story. There was a prototype, a team, and a level of problem clarity that’s rare at any stage and almost unheard of at pre-seed. They knew which acronyms mattered and which were noise. They knew which integrations were politically possible and which weren’t. They knew what a JTAC actually needs in the loop and what a Group 3 UAV actually does when its link drops.
What sealed it wasn’t the pitch. It was what we learned about the months before the pitch. Phil’s original funding source had walked away. Most founders pause at that point. Phil convinced his engineers to keep building (without pay), recruited top researchers out of MIT Lincoln Labs into the same deal, and put his own savings in to make payroll math work. By the time he sat across from us, through sheer force of will, the prototype for an incredibly complex problem was running.
That’s the founder-market fit we look for at Outlander. Operator depth, product conviction, and the kind of persistence that doesn’t flinch when the room is empty.
What they’ve accomplished
Since the pre-seed, NODA has built one of the largest integrated ecosystems of defense autonomous systems in the world. 30+ OEM integrations spanning subsurface to space. Partners include Huntington Ingalls and Viasat. They’ve been selected over incumbents to lead the orchestration layer on a multi-domain collaborative autonomy program for the Department of War, and they hold contracts with the UK Ministry of Defence. The platform is in the field. The pipeline keeps growing.
Where it goes from here
Defense is the proving ground. The architecture points further. The platform is vendor-agnostic by design and the reasoning engine is built to optimize across any heterogeneous fleet. Same problem shape shows up in autonomous vehicle fleets, industrial robotics, logistics, infrastructure inspection. Too many independent systems, not enough coordination.
Outlander founder/managing partner, Paige Craig, put it simply in the announcement: “NODA AI has been the fastest growing company in our portfolio and is onto something big.”
Congrats to the whole team at NODA AI: A $25M Series A is a meaningful step. Knowing this team, it’s one of many.
This week, Elon Musk merged SpaceX and xAI into an entity valued at $1.25 trillion – the world’s most valuable private company – with plans to build data centers in space.
It’s the kind of move that forces you to recalibrate what “big” means. And it caps off a 2025 that was anything but quiet:
If 2025 was wild, 2026 is already shaping up to be even more interesting.
Here’s what our investing partners are watching:
The robot economy arrives. In this next generation, robots will be more common than humans. From fighting wars to folding clothes and delivering meals. This growth will require advanced manufacturing, materials, energy, and sensors.
There’s also a massive picks & shovels opportunity to build platforms that service these bits – comms and systems to coordinate effort between and among these groups, security, auditing, tracking, insurance, and much more. The companies building the infrastructure layer for the robot economy will define the next decade.
Commerce gets more contextual. The future of commerce will not be won through keywords and impressions, but through influence, context, and trust. Generative AI, creators, and new media are reshaping how consumers discover and decide, and the companies building for this shift will define the next generation of commerce infrastructure.
Barometer, an AI-powered brand suitability platform for advertising, is a perfect example – enabling brands to align with suitable content at the content-specific level rather than buying blind.
The grid becomes the bottleneck. The real bottleneck in electrification isn’t power generation – it’s the grid itself. Demand from AI data centers, EVs, and electrified industry is ramping faster than new transmission can be built. The problem is that utilities don’t actually have great visibility into their infrastructure. Inspections are infrequent, data is patchy, and that forces conservative operating assumptions.
That’s where companies like Nomadic Drones come in. Their drones land directly on power lines, self-charge, and continuously monitor grid health, giving utilities a live view instead of quarterly snapshots. The payoff is higher utilization of existing lines, smarter maintenance, and less pressure to rush into expensive new builds. As electricity becomes a strategic input for the AI economy, tools that help the grid see and use itself better matter more than ever.
Designers start eating frontend engineering. I spent a lot of 2025 investing in hardware and it’s an area I am still incredibly excited about in 2026. But when I look at the direction AI is pushing software, here’s my take: front-end engineering as we know it might not exist in five years. We’re heading toward a world where designers & product leaders directly own the frontend, with AI handling the translation from design to production code. And I don’t believe that prompt engineering alone is the future, especially as design/user experiences become a bigger part of defensibility.
Tools like Wonder (they just launched their private alpha) are building for this future. Unlike Figma or other current AI prototyping tools, Wonder’s mission is to connect directly to your codebase, understand your design system, and generate beautiful interfaces that can ship to production the same day. AI is going to completely rewrite entire job functions and 2026 is going to mark a major shift for the frontend.
Teleoperation takes center stage. Forget full autonomy. In 2026, humanoid robots go mainstream through teleoperation. The companies trying to solve autonomy first are burning capital on impossible timelines. The smart money is on hybrid models. Human intelligence drives robots remotely, building toward autonomy as a byproduct of real-world deployment, with unit economics that make sense from day one.
Our portfolio company Starlife is building that future right now. (And yes, they’re hiring!) They deploy low-cost humanoids-as-a-service, each piloted in real-time by a human operator, solving customers’ critical labor shortages today while generating physical data at an unprecedented scale.
The PCP Is dead. Long live the AI triage layer. Hot take: Primary care physicians as the first point of contact will work towards being irrelevant.
The combination of continuous biometric monitoring, AI-driven symptom assessment, and specialist-matching algorithms will route patients directly to the right care endpoint. We’re watching for founders building the infrastructure layer here – not consumer apps, but the pipes that health systems will adopt to survive margin compression. The future of healthcare routing won’t look like a doctor’s office. It’ll look like logistics optimization.
Are you a pre-seed or seed stage founder rewriting an industry? We should talk. www.outlander.vc
Disclaimer: This post contains forward-looking opinions of Outlander VC team members and does not constitute investment advice or an offer to sell or solicitation to purchase any securities. References to portfolio companies should not be interpreted as recommendations or endorsements. Past performance is not indicative of future results. All investments involve risk, including possible loss of principal.
In 2026, startups are building and deploying faster than ever. Founders move from idea to MVP in weeks by leveraging cloud-native infrastructure, open-source frameworks, and AI-assisted development. Small technical teams now engage real customers and validate demand far earlier in a company’s lifecycle.
This acceleration has changed what it means to raise pre-seed capital. With a compressed “0 to 1” timeline, early decisions matter more, and the choice of a pre-seed partner can shape a company’s trajectory. It’s not enough to be writing checks; founders look for VC partners who bring judgement, pattern recognition, and domain expertise.
At Outlander VC, we’ve been investing in idea-phase founders for over 15 years. Along the way, we’ve worked alongside talented pre-seed VCs–actual pre-seed VCs–who are backing founders at the earliest stage and partnering for the long haul.
Here’s a list of 26 pre-seed VCs that you should know in 2026.
Outlander VC is laser-focused on the one factor that drives startup success–the founder. After more than 15 years of investing, our team has backed over eighteen unicorns as a lead or early investor, including Scale, Lyft, Gusto, Wish, HavocAI, and more.
Stage: Pre-seed, Seed
Check Size: $500K – $2.5M
Focus: Generalist; Software, AI/ML, Robotics & Autonomy, Industrial Tech, Defense
Website: https://outlander.vc/
Manufacturing and industrial automation–focused investors backing AI-driven production systems and smart factory technologies, with a specialization in vertical SaaS platforms that apply AI to industrial use cases.
Stage: Pre-Seed, Seed
Check Size: $250K–$400K
Focus: AI-Driven Production, Vertical SaaS, Smart Factory Tech
Notable Investments: Melrose, Aurelius, Pet’s Table
Website: https://640oxford.com/
Apply: Contact form on their website
Highly active, residency-focused accelerator with a growing US presence in New York City, San Francisco, and Austin.
Stage: Pre-seed, Inception
Check Size: <$500K
Focus: Generalist
Highlighted Investments: Airalo, Lovable
Website: https://www.antler.co/
Early-stage venture capital firm that backs pre-seed and seed companies—especially product-led founders and “technical storytellers”—across work, play, and future-facing tech in the U.S., with quick, transparent decision making.
Stage: Pre-Seed, Seed
Check Size: $250K
Focus: SaaS, Hardware, Applied AI
Highlighted Investments: Maneva, Statusphere
Website: https://www.behindgeniusventures.com/
A hybrid venture capital firm and startup studio that has been building and investing in startups since 2008, with a knack for backing transformative internet infrastructure (e.g. Hugging Face). Currently accepting applications for AI camp.
Stage: Pre-Seed, Seed
Check Size: $250K–$750K
Focus: AI/ML, Web3, Decentralization
Highlighted Investments: Airbnb, Kickstarter, Tumblr
Website: https://www.betaworks.com/
Inception-stage investors focused on accelerating technology adoption in the backbone industries of the economy, with a strong orientation toward Pacific Northwest founders while remaining broadly US and Canada agnostic.
Stage: Pre-Seed, Seed
Check Size: $250K-1M
Focus: SaaS, Fintech, Real Economy
Highlighted Investments: Myriad, Empirium
Website: https://www.breakwater.vc/
Early-stage investor specializing in deep tech and frontier tech pre-seed deals, averaging one deal per week.
Stage: Pre-Seed
Check Size: $500K
Focus: Deep Tech
Highlighted Investments: Coinbase, Volley
Website: https://www.boost.vc/
Operator-led venture fund that pairs capital with operational support, investing in companies that reshape how people live and business thrive.
Stage: Pre-Seed and Seed
Check Size: $500K
Focus: Consumer Tech, Health Tech, Connected Hardware & Robotics
Highlighted Investments: Peloton, Ro
Website: https://www.cadeventure.com/
Inception-stage investors who pair early conviction with a 1000+ member community of founders and operators through the Comma Collective as a core part of their value-add.
Stage: Pre-Seed and Seed
Check Size: $250-500K
Focus: SaaS, AI/ML, Dev Tools, Fintech, Digital Health
Highlighted Investments: Convoke
Website: https://comma.vc/
Inception-stage firm partnering with exceptional students solving real-world problems.
Stage: Pre-Seed
Check Size: $50K–$250K
Focus: Impact (e.g. Climate, Accessible Healthcare, Economic Mobility)
Highlighted Investments: General Galactic, Mobius Industries
Website: https://www.critical.vc/
Industry-and-geography-agnostic investors, renowned for a founder-friendly 6-day decision process. Investing out of Fund III with over $200M in committed capital.
Stage: Pre-Seed, Seed
Check Size: $200K–$1M
Focus: AI/ML, All Technology Sectors, Data-Driven Companies
Highlighted Investments: STRATxAI, GigEasy, Paratus Health
Website: https://www.goaheadvc.com/
A diversity-focused pre-seed fund backing underrepresented founders, with increasing activity in AI/ML and a reputation for founder-friendly processes and fast decision-making.
Stage: Pre-Seed, Seed
Check Size: $50K-$750K (Sweet Spot $150K)
Focus: AI/ML, FinTech, Healthcare
Highlighted Investments: Webflow, NerdWallet, Branch
Website: https://www.hustlefund.vc/
Ultra-selective fund known for early investments in transformative companies like OpenAI, Slack, and Airbnb. They focus on enterprise applications and AI/ML solutions, requiring warm introductions to gain an audience.
Stage: Pre-Seed
Check Size: $100K-$750K (Sweet Spot $400K)
Focus: AI/ML, Healthcare, SaaS, Enterprise Applications
Highlighted Investments: OpenAI, Slack, Lyft
Website: https://www.k9ventures.com/
Early-stage venture firm based in Chicago, investing solely in tech startups headquartered in the Midwest.
Stage: Pre-Seed, Seed
Check Size: $250K-$1M
Focus: AI/ML, HealthTech, FinTech, Vertical SaaS
Highlighted Investments: Astronomer, Arrellio, Prediction Guard
Website: https://www.m25vc.com/
Early stage investors backing Gen Z founders as early as inception.
Stage: Pre-Seed and Seed
Check Size: $100-150K
Focus: Generalist, AI/ML, Data
Highlighted Investments: Mercor
Website: https://www.neweraventures.com/
High-frequency investors backing students in US universities.
Stage: Pre-Seed, Seed
Check Size: $50K
Focus: Generalist
Highlighted Investments: Hardshell, Vala
Website: https://notyet.vc/
Pre-seed fund investing in software and AI/ML, focused on helping technical founders develop as B2B sales leaders.
Stage: Pre-Seed
Check Size: $250K–$500K
Focus: SaaS, Enterprise, AI/ML
Highlighted Investments: EcoMap, ResultID
Website: https://www.paxmv.vc/
Apply: Pitch Us form
Pre-seed and seed investors backing community-building companies in software industries.
Stage: Pre-Seed
Check Size: $100-250K
Focus: SaaS, Enterprise, Consumer, AI/ML
Highlighted Investments: Clay, Superpower
Website: https://www.parkrangerscap.com/
High-volume pre-seed fund known for backing underrepresented founders, leading first institutional rounds across B2B and B2C software with a strong emphasis on diverse founding teams.
Stage: Pre-Seed, Seed
Check Size: $250K–$500K
Focus: B2B Software, B2C Software, Connected Hardware
Highlighted Investments: The Athletic, Bobbie, Carrot Fertility, Modern Health, Clearco
Website: https://precursorvc.com/
Specialists in emerging technology; invests with “scout checks” at earliest stages and leads early-stage rounds.
Stage: Pre-Seed and Seed
Check Size: Up to $1M
Focus: Software, AI/ML, Consumer, Fin Tech, Blockchain
Highlighted Investments: Exowatt, Momentum Labs
Website: https://www.protagonist.co/
Founder-friendly VC that prioritizes the resilience of entrepreneurs over pedigree.
Stage: Pre-Seed, Seed
Check Size: $250K–$500K
Focus: Generalist
Highlighted Investments: dScribeAI, Trially
Website: https://redbud.vc/
Generalist fund investing in transformative and overlooked founders–not the trendy.
Stage: Pre-Seed and Seed
Check Size: $250-500K
Focus: Generalist
Notable Investments: Arden, Milkify
Website: https://www.southloop.vc
Early-stage investors backing emerging categories with decades-long horizons.
Stage: Pre-Seed and Seed
Check Size: $250-500K
Focus: Fintech, Vertical Software, Applied AI
Notable Investments: Beehiiv, Windsor
Website: https://www.spicecapital.xyz/
Investing in the earliest of pre-seed startups and unafraid of rolling up their sleeves. Untapped looks for founders outside of typical networks.
Stage: Pre-Seed
Check Size: $100K–$250K
Focus: AI/ML, Emerging Tech
Notable Investments: General Intelligence Company, Woz
Website: https://untapped.vc/
Early-stage investors backing ideas making physical industries more profitable, efficient, and resilient.
Stage: Pre-Seed, Seed
Check Size: $100K–$750K
Focus: Energy, Mobility, Manufacturing
Notable Investments: Actual, Futureproof
Website: https://www.virtaventures.co/
Wischoff Ventures invests in relentless early stage, high growth technology companies.
Stage: Pre-Seed, Seed
Check Size: $500K–$1M
Focus: Legacy Industries
Notable Investments: Checkmate
Website: https://www.wischoff.com
On November 7th, Secretary of War Pete Hegseth announced sweeping changes to how the Department of War buys technology. The centerpiece: rebranding the Defense Acquisition System (DAS) as the Warfighting Acquisition System (WAS), with an explicit mandate to push authority downward and move faster. The old system was built around compliance and process. The new system treats acquisition as a warfighting function and prioritizes speed to fielded capability. As the memo puts it: “Speed to capability delivery is now our organizing principle.”
The reforms also introduce Portfolio Acquisition Executives (replacing the old PEO structure), incentive-based compensation tied to delivery timelines, and a stated preference for commercial solutions. For founders, the signal is clear: the Department wants to buy from you faster.
So in your next strategy meeting, you’re probably asking: “Should I focus on winning OTAs now instead of SBIRs?”
Here’s the bottom line.
OTAs are a powerful tool. They move faster than traditional contracts and can lead to sticky production agreements. But SBIRs remain the best entry point for most startups because they help founders learn how to sell into the Department—not just pitch to it. They force you to nail down a user, a requirement, and a mission. And they make it far easier to graduate into larger contracting instruments later.
Think of SBIR as the beachhead. OTA is the ramp. The goal is not to stack non-dilutive grants forever. The goal is to reach procurement money—and eventually O&M funds—as fast as possible. That’s where real scale and enduring revenue live.
An OTA (Other Transaction Authority) is a non-Federal Acquisition Regulation (FAR)-based contracting instrument. In plain terms, it allows the government to buy, prototype, and scale technology without the full weight of traditional acquisition rules.
OTAs matter because they can be awarded faster, allow more commercial-style deal structures, bridge prototype to production, and convert real demand into real dollars. They sit in that critical middle zone between pilot and full procurement, giving your early champion more leverage to pull you through the system.
Under the new WAS framework, OTAs are likely to become even more prominent as the Department leans into speed and flexibility.
But there’s a catch.
Sometimes a unit or program office will use an OTA to buy a single prototype for an upcoming exercise. On paper, that looks like a win. But if it’s your first engagement with the Department, you’ve landed in the deep end without scaffolding—no built-in transition support, no structured portfolio shepherding.
OTAs can absolutely work as a first touch point, especially if you have a government champion with real operational urgency. But SBIR offices have something OTAs don’t: a mandate to transition what they fund. They bring resources, contracting support, portfolio leads, and workflows designed to help companies mature inside the DoW ecosystem. Even if you enter through an OTA, you should leverage SBIR and innovation networks to connect with requirements owners, align to the right Program Elements (PEs), and build a transition plan that leads to actual procurement.
OTAs aren’t bad. You just usually need the support structure around them to turn a prototype sale into something enduring.
SBIR forces discipline. It requires you to define the specific operational pain, the exact end user, and how your capability maps to mission execution. It also gives you support infrastructure, contracting help, and transition-focused resources.
In short, SBIR teaches you how to sell inside the Department—not just pitch outside of it.
It de-risks and matures your product before it reaches the warfighter, and it gives you credibility with every other buyer and contracting pathway you’ll encounter.
SBIR is how you build the muscle memory of how Warfighting Acquisition actually works.
Here’s something founders often overlook: the customer you’re talking to needs access to a Program Element (PE) with money aligned to your maturity level and use case. A PE is essentially a budget line that defines what Congress authorized that money to be spent on. If your champion doesn’t have the right PE, they literally cannot pay you—even if they want to.
This is why SBIR is so powerful early. It bypasses PE constraints because it’s congressionally carved-out RDT&E (Research, Development, Test & Evaluation) funding.
It’s also why founders often experience the Department as “interested” but not buying. They’re talking to officers and GS civilians who genuinely want the capability, but their PE is the wrong color of money at the wrong time of year.
If you’ve never sold to the government, SBIR is the cleanest on-ramp.
If you already have validated demand and operational pull, OTAs can accelerate your path to production and procurement.
Here’s a simple framework for running your Warfighting Acquisition OODA Loop:
SBIR is the ignition. OTA is the accelerant.
Use both.
Why Outlander VC?
We back pre-seed founders building for national security – and help them navigate their first DoW contracts. If you’re looking to raise outside capital, we’d love to chat. Apply here.
For the last decade, startups were told to “stay lean.” Don’t touch hardware. Be a pure software play. Today, that mindset is increasingly obsolete.
At Outlander VC, we believe that many consequential companies of the next decade won’t be built in the cloud alone. They’ll be built in factories.
The foundational AI boom (and horizon commoditization) has delivered easy-to-access powerful models. You can build a wrapper or deploy an API overnight. The result? A flood of lookalike startups chasing ephemeral distribution moats.
But when you embed intelligence into physical systems – into drones, vehicles, vessels, robots – you shift the playing field. Software is getting easier and easier to copy.
Physics isn’t so simple.
Hardware introduces real constraints: supply chains, manufacturing, motion, autonomy, edge sensing, and more. In today’s era, what used to be considered a hindrance can now be an advantage. Mastering how to navigate those constraints provides a layer of defensibility that just doesn’t exist in the software world anymore.
Over the last ten years, it was considered suicide by many to build a hardware company without $50M in venture funding. We were ahead of the curve then, backing companies like Coco and Skyways in years where robotics investments made up less than today’s estimated 10% of venture dollars. And we see a massive potential transformation ahead.
Today, founders can build prototypes quickly with off-the-shelf components or leveraging 3D printing for rapid iterations. With generative design and additive optimization tools like MecAgent or Vixiv, bringing your imagination to life in the physical world is happening faster and faster too.
The bottom line is this: we’re not anti-software; in fact we’re still extremely bullish on certain vertical and horizontal AI plays so continue to make active investments in what we believe will be transformative technologies in the app layer. But we do believe that some of the biggest problems in the world for the next 10 to 20 years and beyond will be solved by founders who ship things, not just code.
And to help the founders out there who share this vision of the future world, we’ve put together a list of 25 investors cutting checks at the earliest stages into industrial-focused tech startups.
These are the firms that, like us at Outlander VC, are backing ambitious founders at the very beginning, funding ideas that are reimagining how the physical world is built, moved, powered, and automated.
1. Outlander VC
Stage: Pre-seed, Seed
Check Size: $500K–$2.5M
Focus: AI for the Physical World, Robotics, Space, and more (Generalist fund)
Notable Investments: ScaleAI, Coco, REGENT
Website: outlander.vc
2. Activate Capital Partners
Stage: Pre-seed, Seed
Check Size: $250K–$1M
Focus: Factory automation, climate industrials, industrial IoT
Website: activatecap.com
3. Alchemist Accelerator
Stage: Pre-seed
Check Size: $150K–$500K
Focus: B2B industrial tech, robotics, manufacturing
Website: alchemistaccelerator.com
4. Alumni Ventures
Stage: Pre-seed–Series A
Check Size: $100K–$3M
Focus: AI, robotics, deep tech
Website: av.vc
5. BlueBear Capital
Stage: Pre-seed, Seed
Check Size: $500K–$2M
Focus: Robotics, energy infrastructure, autonomy
Website: bluebearcap.com
6. Boost VC
Stage: Pre-seed
Check Size: $500K
Focus: Frontier tech, aerospace, robotics
Website: boost.vc
7. Brickyard
Stage: Pre-seed, Seed
Check Size: $300K–$500K
Focus: Robotics, automation, technical teams
Website: justlaybrick.com
8. Construct Capital
Stage: Pre-seed, Seed
Check Size: $2M–$4M
Focus: Robotics infrastructure, industrial SaaS, logistics
Website: constructcap.com
9. Contrarian Thinking Capital
Stage: Pre-seed, Seed
Check Size: $150K–$250K
Focus: Robotics, logistics, modernizing industrial workflows
Website: contrarianthinkingcapital.com
10. Cybernetix Ventures
Stage: Pre-seed, Seed
Check Size: $300K–$1.5M
Focus: Robotics, industrial autonomy, manufacturing automation
Website: cybernetix.vc
11. Detroit Venture Partners (DVP)
Stage: Pre-seed, Seed
Check Size: $100K–$500K
Focus: Industrial automation, manufacturing tech, mobility
Website: detroitventurepartners.com
12. DO Venture Partners
Stage: Pre-seed, Seed
Check Size: $300K–$1.2M
Focus: Robotics, automation, deep industrial software
Website: doventurepartners.com
13. Embark Ventures
Stage: Pre-seed, Seed
Check Size: $300K–$1M
Focus: Robotics, autonomy, technical founders
Website: embark.vc
14. Fifty Years
Stage: Pre-seed, Seed
Check Size: $250K–$500K
Focus: Hardtech, industrial systems, robotics, climate hardware
Website: fiftyyears.com
15. Future Ventures
Stage: Pre-seed, Seed
Check Size: $500K–$2M
Focus: Tough tech, industrial robotics, climate + hard tech
Website: future.ventures
16. GoAhead Ventures
Stage: Pre-seed, Seed
Check Size: $100K–$500K
Focus: Deep engineering teams, robotics, autonomy
Website: goaheadvc.com
17. Heroic Ventures
Stage: Pre-seed
Check Size: $200K–$1M
Focus: Robotics, autonomy, defense-aligned hard tech
Website: heroicvc.com
18. Ironspring Ventures
Stage: Pre-seed, Seed
Check Size: $300K–$1.5M
Focus: Manufacturing innovation, construction, supply chain
Website: ironspring.com
19. Mana Ventures
Stage: Pre-seed
Check Size: $250K–$750K
Focus: Climate industrials, materials, space, hardtech
Website: manaventures.com
20. Pathbreaker Ventures
Stage: Pre-seed, Seed
Check Size: $250K–$600K
Focus: Robotics, AI, deep tech
Website: pathbreakervc.com
21. Precursor Ventures
Stage: Pre-seed
Check Size: $250K–$750K
Focus: Founder-first investing, deep tech, industrial-adjacent hardware
Website: precursorvc.com
22. Razor’s Edge Ventures
Stage: Pre-seed, Seed
Check Size: $250K–$1M
Focus: Autonomy, sensing, industrial AI, dual-use hardware
Website: razorsedge.vc
23. Right Side Capital Management
Stage: Pre-seed
Check Size: $200K–$500K
Focus: High-volume pre-seed, robotics and deep tech
Website: rightsidecapital.com
24. RockYard Ventures
Stage: Pre-seed, Seed
Check Size: $100K–$500K
Focus: Construction, manufacturing, supply chain
Website: rockyardventures.com
25. The Engine
Stage: Pre-seed, Seed
Check Size: $250K–$2M
Focus: Tough tech, industrial platforms, robotics, energy systems
Website: engine.xyz
How Space Startups can Start Working with the Government
Guest: SpaceWERX Director Arthur Grijalva
Host(s): AJ Smith, Donavan Moss
In this conversation, we sat down with Arthur Grijalva, Director of SpaceWERX, to talk about how space startups can successfully work with the U.S. government.
If you have not heard of SpaceWERX, they are the innovation arm of the U.S. Space Force. Each year, they invest over $460M in startups tackling the most urgent challenges in space. Their mission is to bring game-changing commercial capabilities into national security. If you are building for space and want your technology to move from prototype to government procurement, understanding how SpaceWERX operates is a critical first step.
Watch the full interview below and read on for five takeaways from our conversation with Arthur.
SpaceWERX takes early risks, backing ideas as early as the white-paper stage. They might seed your concept with about $75K to prove feasibility or fund $1.25M–$2.25M to build a prototype through a Phase II. Larger opportunities come later through Strategic Funding Increase (STRATFI) awards, which combine SpaceWERX, program office, and private capital into efforts that can reach $60M+. At every step, they focus on whether your tech can scale into an operational capability for the Space Force.
Arthur breaks dual-use into “little C” (mostly defense-focused with private capital interest) and “big C” (commercially ubiquitous, like GPS). Both can work. The point is to avoid relying entirely on the government as your only customer. A credible commercial path (even if it’s just on your future roadmap) gives you staying power.
One of the biggest mistakes SpaceWERX sees is poor timing; companies showing up only when they need funding. Instead, engage early. Attend events, join programs, and build relationships before you ask for money. If they already know you and your tech, it is far easier to get pulled into funded opportunities.
If you can help the Space Force with space superiority, orchestration of proliferated satellite constellations, or AI/ML for space operations, you are solving high-priority problems right now. These are areas where funding and partnerships are actively being built.
SpaceWERX designs every award with a transition partner in mind, but only a fraction of projects make it across the finish line. Founders who keep transition pathways front and center from the beginning have a much better chance of success.
This is just the start. In future episodes, we will be speaking with other stakeholders across the DoD to go deeper on how to navigate defense procurement, secure funding, and scale inside this ecosystem.
If you are at the beginning of your journey and building technology with potential to serve both defense and commercial markets, apply for funding at www.outlander.vc.
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