Q2 2026 · Call for Founders
14 bets we're making right now.
Each quarter, our investment team goes deep on the areas where we think the next generation of B2B companies will be built. These are the spaces we're actively researching, debating, and looking to fund. If you're building here — or thinking about it — we want to meet you.
Pulse of the market
Employer healthcare costs in 2026 are expected to see their largest increase in 15 years, with employer sponsored health insurance spending projected to surpass $1.7 trillion. Insurance distribution is roughly 20 years behind other industries in technology adoption. Despite recent activity in AI native commercial brokerages (Harper, Gyde, Casey) and benefits platforms (Angle Health, Nava Benefits), the core brokerage function — plan selection, carrier negotiation, renewal strategy, compliance — remains largely human driven.
We'd love to meet founders thinking about
- →SMB health insurance brokerage: an AI-native broker fiduciarily aligned with the employer rather than the carrier, modeling self-funded scenarios in real time, surfacing claims data, and handling TPA selection and stop-loss placement.
- →Self-funded / alternative funding: SMBs are embracing captives, cash prices, and AI to bypass traditional fully-insured plans. A brokerage that can model and place these structures with AI would be differentiated.
- →Benefits navigation and advocacy: 95% of employers are improving open enrollment with AI-driven recommendation engines. A brokerage that owns the employee relationship — not just the employer — could build deep retention.
Pulse of the market
The cloud giants spent two decades convincing enterprises to move everything off-prem. AI is reversing that for a significant share of compute. Data sovereignty laws, classified workloads, latency requirements, regulatory constraints, and the simple math that owning GPUs is cheaper than renting them after 18 months are all pushing AI workloads back on-premises. But enterprises don't want to go back to managing their own infrastructure — they want the cloud experience running on hardware they control. Meanwhile, many inference workloads don't need a data center at all: a $2,000 edge device running quantized open source models can serve inference that would cost $50K+ annually in cloud API calls. The enterprise AI infrastructure market is projected at $300B+ by 2030, and the companies that own the on-prem and edge layers capture the massive middle ground between public cloud and bare metal DIY.
We'd love to meet founders thinking about
- →The managed on-prem AI platform: a turnkey appliance that grows into a complete managed AI platform matching the developer experience of AWS/GCP/Azure but running entirely within the customer's physical control. The winners are software companies that happen to ship on hardware, not hardware incumbents.
- →Distributed edge Inference-as-a-Service: deploying and managing a fleet of inference nodes at customer locations, handling the operational complexity, and pricing it as a simple per-inference fee that dramatically undercuts cloud pricing.
- →The global inference network: thousands of inference nodes at the edge of every network, running optimized models on heterogeneous hardware, dynamically routing requests to the nearest capable node. The inference CDN is a larger market than the content CDN.
Pulse of the market
AI is creating an entirely new cost structure that enterprise finance teams have no tooling to manage. Compute is now a major variable OpEx line that fluctuates with workload, pricing model, and vendor. AI agents are performing work previously billed as labor, with no payroll or tax framework to account for it. Enterprises are acquiring GPU infrastructure with no standardized approach to depreciation or valuation. And model failures and agent errors are creating real liability exposure with no viable insurance products. This is where cloud cost management was in 2012 before Apptio and CloudHealth built what became a $4B+ category.
We'd love to meet founders thinking about
- →Compute cost forecasting for finance: FP&A tools built specifically for AI compute as variable OpEx, including token attribution, workload-level cost allocation, and forecast modeling for usage- and outcome-based pricing.
- →On-prem AI asset management: depreciation scheduling, capital allocation, insurance valuation, and collateral assessment for GPU rack assets the CFO is now inheriting.
- →AI labor accounting and attribution: tracking, reporting, and optimizing cost attribution between human and AI teams ahead of regulatory and tax frameworks.
- →AI enterprise risk and insurance: model failure, agent errors, and hallucination-driven decisions are real liability categories with no viable insurance products yet.
Pulse of the market
AI has fundamentally changed the economics of molecular discovery. What used to take years of directed evolution and tens of millions of dollars can now be accomplished in weeks using generative protein models, autonomous enzyme engineering platforms, and cell-free screening systems. New computational methods can design enzymes for industrial chemical reactions from scratch in a one-shot process, producing biocatalysts stable enough for harsh manufacturing environments. The $6 trillion chemical industry still runs on petroleum-derived processes, and enzyme-based alternatives can deliver 50–70% energy savings and 40–60% water reduction. Cost parity remains the central challenge, and the companies that win will close the gap between "works in the lab" and "works at industrial scale and price."
We'd love to meet founders thinking about
- →Process economics validation: taking an AI-designed enzyme or molecule from provisional patent to demonstrated industrial viability — proving yield, titer, rate, and cost of goods at the scale that makes the next round of funding obvious.
- →Enzyme-as-a-Process for specific industrial replacements: selling the validated enzyme and the production process together into high-value verticals where cost parity is within reach — specialty chemicals, pharmaceutical intermediates, food ingredients, textile processing, bio-based polymers.
- →AI-guided retrobiosynthesis optimized for economics: platforms that design biosynthetic pathways for industrial cost structure from the start, incorporating feedstock cost, energy, yield, and capex into the design loop.
- →Patent strategy infrastructure for AI-discovered molecules: tools that help founders build defensible IP estates around AI-generated molecules — human inventorship documentation, continuation-in-part strategy, and freedom-to-operate analysis.
- →Scale-up and fermentation infrastructure: cell-free biomanufacturing platforms, continuous fermentation systems, and CDMOs purpose-built for bridging computational design to pilot-scale production.
- →Enzymatic recycling and circular economy applications: enzymes that depolymerize plastics, textiles, and industrial waste back into virgin-quality feedstocks.
Pulse of the market
The US is facing a structural labor crisis that no amount of recruiting can solve. The country is losing 400K+ skilled tradespeople per year to retirement with no replacement pipeline, while manufacturing carries 500,000+ unfilled positions. At the same time, AI is displacing white-collar workers who need new career paths. The conventional response — "train more workers" — misses the point. The real opportunity is using AI to fundamentally redefine what it means to do these jobs, decouple decades of institutional knowledge from the physical act of doing the work, and build new pathways for people entering the trades from non-traditional backgrounds.
We'd love to meet founders thinking about
- →AI-augmented skilled trades platforms: AR and spatial computing tools that overlay expert guidance onto physical work environments in real time, turning a two-year apprentice into someone who can execute complex jobs with AI assistance.
- →Institutional knowledge capture: platforms that record and structure skilled trade workflows from observation — video, sensor data, tool usage — into reusable procedural knowledge. A live skills graph where every tap, voice query, and completed task trains future models.
- →The manufacturing workforce platform: AI can automate ~80% of an operator's cognitive work. Cleanly decoupling cognitive from physical compresses ramp time from months to weeks and expands the eligible labor pool.
- →AI-native skilled trades services firms: full-stack service businesses that use AI to compress the expertise required for HVAC, plumbing, electrical, and other trades.
- →The white-collar to trades transition pipeline: vocational retraining that compresses 2–4 year training into 6–18 months, purpose-built for career changers rather than 18-year-olds.
Pulse of the market
Insurance is a $6T+ global industry built on actuarial tables derived from decades of human-operated physical systems. AI is rewriting the risk profile of virtually every insurable physical asset, but the data to price this new risk doesn't exist. Liability frameworks for human/AI shared decision making haven't been written, and incumbent carriers can't make the transition because their organizational structure, regulatory framework, and capital model are built around the old paradigm. An autonomous warehouse has a completely different fire risk profile than a human-operated one. When an AI system miscalculates a structural load or misjudges a drone flight path, existing insurance products, regulatory frameworks, and legal precedents have no answer for who is responsible.
We'd love to meet founders thinking about
- →Liability allocation for human/AI shared outcomes: engines that determine responsibility when human operators and AI systems jointly control physical outcomes in construction, logistics, manufacturing, and healthcare.
- →Underwriting infrastructure for autonomous systems: insurers need to underwrite the behavior of the AI operator, not just the asset — and the rating variables, models, and data pipelines don't exist yet.
- →Black box recording and audit infrastructure for AI-assisted operations that creates defensible records for courts and regulators.
- →Risk data platforms for AI-operated physical assets: instrumenting facilities, factories, fleets, and buildings to generate the datasets insurers need to price new coverage.
- →The AI-native insurance carrier: vertically integrated and built from scratch around continuous physical-world data, dynamic risk pricing, automated loss prevention, and instant sensor-based claims settlement.
An open invitation
See a gap you want to fill?
Forum invests at the earliest stages — pre-idea, pre-market, or pre-revenue. If you're building in any of these areas already, or have industry experience and are ready to build, we want to hear from you.
Pulse of the market
When chip vendors rushed to bring intelligence on device starting around 2015, each took a different architectural approach with no shared standard to build toward. Every NPU vendor built proprietary toolchains, and none are compatible. A developer who wants an AI feature to run across Qualcomm, Apple, Intel, and MediaTek hardware isn't writing one codebase — they're maintaining several, each requiring its own optimization every time something changes. No neutral runtime exists to manage what happens when multiple AI workloads compete for the same chip on the same device. Vendor lock-in is a massive headwind on edge AI advancement, and every platform shift tells us the company that removes it captures outsized value.
We'd love to meet founders thinking about
- →Edge runtime and compiler abstraction: a neutral runtime and compiler that lets developers write once and execute optimally across any edge silicon — the layer sitting between AI applications and the diverse NPU hardware underneath.
- →Intelligent inference routing: profiling each workload's actual requirements and matching it to the most cost-effective silicon automatically across NVIDIA, AMD, Intel, Qualcomm, and custom ASICs.
- →Unified operational planes: a management layer that treats edge and on-prem hardware as a single resource pool with cost attribution that surfaces exactly where the performance premium isn't worth the price premium.
Pulse of the market
AI is transforming every dimension of how physical assets are maintained — from what breaks and why, to who fixes it and how the work gets paid for. As AI becomes the operator of physical systems, failure modes change entirely: software/hardware interaction failures, sensor drift, model degradation in physical environments. Predictive maintenance has underdelivered for a decade because "your machine will probably fail in 30 days" isn't actionable. The real shift is prescriptive, going from predicting failure to orchestrating the intervention. Meanwhile, the US has a $2.6T infrastructure maintenance backlog, military depots can't hire enough technicians, and $500B+ in hazardous government work isn't getting done.
We'd love to meet founders thinking about
- →Maintenance for AI-controlled physical systems: when the failure mode is the AI itself — model drift, sensor calibration decay, software updates interacting with physical systems in unexpected ways — the diagnostics and service contracts look nothing like traditional maintenance agreements.
- →Prescriptive maintenance that closes the loop: systems that generate the specific intervention, order the parts, schedule the technician, and verify the repair.
- →Uptime-as-a-service business models: platforms where the AI takes financial responsibility for equipment availability and earns a premium for guaranteed performance.
- →Autonomous infrastructure maintenance: networked fleets of AI-controlled robots for pipe inspection, road maintenance, bridge inspection, and power line monitoring — subscribed to by municipalities like a utility.
- →Autonomous and AI-augmented military depot maintenance: robotics for repetitive inspection and repair, AI for diagnostics and planning, and AR for amplifying the remaining human technicians.
- →Embodied AI for contaminated and hazardous environments: nuclear decommissioning, chemical weapon disposal, and deep infrastructure inspection in environments too dangerous for humans.
Pulse of the market
As previously air-gapped physical systems get connected to enable AI control, the attack surface is expanding rapidly. The threat model is categorically different from traditional industrial cybersecurity. You're not just protecting the network — you're protecting against adversarial manipulation of the AI itself: feeding it bad sensor data, corrupting its decision model, exploiting the gap between what the AI thinks is happening and physical reality. When an attacker compromises a traditional IT system, you lose data. When an attacker compromises an AI controlling a factory, a power grid, or a fleet, the consequences are physical. The global cybersecurity market is already $200B+ and growing — the physical-world AI security layer could be larger because the consequences of failure are existential and governments will mandate it.
We'd love to meet founders thinking about
- →Adversarial robustness testing for physical infrastructure AI: testing platforms that systematically probe AI systems controlling critical infrastructure for vulnerabilities.
- →Physical/digital integrity verification: systems that cross-check AI decisions against independent physical sensors to detect when the AI's model of reality has been compromised — before consequences become physical.
- →Incident response and forensics for AI physical breaches: purpose-built tools to reconstruct what the AI "believed" versus what was actually happening and untangle cyber compromise from physical consequence.
- →Physics-aware cyber defense: security infrastructure that combines cyber defense with physical system modeling, detecting anomalies that only make sense when you understand how a power grid, factory, or autonomous fleet operates.
Pulse of the market
The physical world is drowning in data it can't access. Governments sit on massive amounts of physical world data locked in analog formats: paper permits, hand-drawn utility maps, physical land records. Millions of miles of underground pipes, cables, and conduits were never digitally mapped. Roughly $500T in physical assets on Earth have no real time digital representation of their condition, utilization, or value. And the environments that matter most for the next wave of AI models have never been sensed at scale — underground infrastructure, subsea systems, and interior industrial processes require acoustic, pressure, chemical, and thermal sensing modalities that are only now becoming viable. As physical space becomes multi-dimensional — drone corridors above, utility corridors below, autonomous vehicle routing zones across — the absence of coherent physical world data infrastructure becomes the binding constraint on everything from infrastructure investment to insurance pricing to national security.
We'd love to meet founders thinking about
- →The analog-to-digital conversion of government infrastructure: permits, deeds, environmental assessments, inspection histories, and hand-drawn utility maps locked across thousands of agencies.
- →Subsurface sensing and mapping: platforms combining ground-penetrating radar, electromagnetic detection, and AI to build 3D maps of underground infrastructure that was never digitally recorded.
- →The multi-dimensional land registry: a spatial rights platform for mapping, registering, pricing, and transacting across every dimension of physical space — drone corridors, solar rights, air rights, autonomous vehicle routing zones.
- →Water data infrastructure: distributed sensing for real-time water quality and composition, leak detection combining acoustic sensors and pressure analytics, and water accounting and trading infrastructure.
- →Interior industrial processes: what happens inside a blast furnace or casting mold is almost entirely unobserved. New sensing modalities — acoustic emission, microwave — make interior observation possible for the first time.
- →Expert-in-the-loop labeling platforms: labeling infrastructure for physical-world domains where annotation requires trade or engineering expertise — welders, structural engineers, agronomists.
Pulse of the market
When agents become the customers, the entire commercial infrastructure built around human psychology, attention, and judgment becomes irrelevant or actively counterproductive. Agents don't respond to brand, narrative, or relationship — they query, evaluate, transact, and move on. Every layer of the commercial stack gets rebuilt around what agents actually optimize for: verified performance, price, availability, and compatibility. The infrastructure required to support agent-to-agent commerce — identity, credentialing, trust verification, real-time performance registries, and auction-based procurement — doesn't exist at commercial scale. This is closer to programmatic advertising infrastructure than to traditional procurement, and programmatic advertising created an entirely new industry when human ad selection was replaced by algorithmic selection.
We'd love to meet founders thinking about
- →Agent identity and cross-organizational trust infrastructure: verifiable identity, scoped authorization, and decision logs — the SSL certificates of agent-to-agent commerce — that also resolve cross-organizational trust across customers, suppliers, and banks.
- →Verified performance registries: continuously verified performance records across vendors (uptime, accuracy, latency, error rates, compliance) — a ground-truth dataset on which vendors actually perform as claimed.
- →Anti-collusion infrastructure for agent markets: market surveillance that detects emergent cooperative behavior, proves or disproves intent, and provides compliance evidence regulators will demand.
- →Agent negotiation infrastructure: protocols for agent-to-agent bargaining, market-clearing, fairness constraints, deadlock resolution, and deal verification. Every transaction becomes a micro-market.
- →The data brokerage layer for agents: the $250B+ data industry restructured for agent consumption — real-time delivery, clear provenance, programmatically enforced usage rights, and consumption-based pricing.
Pulse of the market
Industrial and commercial waste is one of the last major physical flows with almost zero data infrastructure. Companies pay to have waste hauled away and that's where visibility ends. But waste streams contain valuable signals — a change in a factory's waste composition can indicate process drift, quality problems, or regulatory exposure before any other metric catches it. On the recovery side, the economics of recycling and materials recovery are entirely dependent on knowing exactly what's in the stream, which today requires manual sorting or expensive lab analysis. AI-powered waste characterization turns a cost center into an intelligence layer.
We'd love to meet founders thinking about
- →Real-time waste stream characterization: computer vision and spectroscopic sensors at the point of generation, turning bins and dumpsters into data collection points capturing composition, volume, and frequency.
- →Process intelligence from waste composition: platforms that use waste as a leading indicator for manufacturing quality, efficiency, and compliance.
- →Regulatory compliance for waste generators: automatic tracking, classification, and reporting of hazardous and regulated waste, where penalties are severe and record-keeping is still manual.
- →Waste stream matching and material arbitrage: mapping what industrial operators discard and matching it to processors who can extract value — a spread without chemistry IP or capex.
- →Co-location recovery modules: semiconductor fab waste contains high-purity source materials at concentrations above any mining operation. Coal ash contains germanium and gallium being thrown away. Modular recovery systems unlock that value on site.
Pulse of the market
Rebuilding US manufacturing capacity is a national priority backed by hundreds of billions in government incentives, but the infrastructure to actually make it happen barely exists. There are 600,000+ manufacturing facilities in the US, and virtually all of them run on tacit, embodied knowledge held by floor managers who are retiring. Most reshoring efforts are failing because companies are trying to replicate supply chains that took decades to develop in Asia. Individual factories operate as isolated islands, each solving the same problems independently. And the economics of production still assume high-volume runs that exclude a massive long tail of products.
We'd love to meet founders thinking about
- →The manufacturing intelligence layer: systems that continuously absorb operational intelligence from sensors, operator behavior, and process outcomes — capturing the tacit knowledge of retiring floor managers and making it available to every shift, facility, and new hire.
- →The factory capital markets platform: connecting real-time factory performance data to capital providers, so a $5M equipment loan can be underwritten on actual machine productivity, uptime, and output rather than tax returns.
- →Manufacturing knowledge pooling: a network where manufacturers share operational learnings without sharing proprietary data — the way hospitals contribute to medical research without exposing patient records. Particularly powerful for fragmented sectors like machining, metal fabrication, plastics, and food processing.
- →On-demand and micro-manufacturing infrastructure: AI-driven quoting, process planning, and QA that makes runs of 1–100 units economically viable. Distributed networks matching micro-production jobs with nearby shops, and digital inventory platforms that replace warehousing 50,000 SKUs with manufacturing on demand.
Pulse of the market
Historically, only large physical assets had enough value to justify the overhead of individual financial tracking: a building, an aircraft, a ship. AI and IoT are driving the cost of monitoring individual physical assets toward zero, which means you can now create a financial identity for a single machine tool, a shipping container, a construction crane, or even a pallet of goods. When every physical asset can report its own location, condition, utilization, and performance in real time, you unlock entirely new financial products: asset-level financing, usage-based insurance, real-time collateral valuation, and liquid secondary markets for physical assets that were previously illiquid.
We'd love to meet founders thinking about
- →Persistent asset identity platforms: giving individual machines, vehicles, or equipment a digital financial identity tied to real-time physical data — location, condition, utilization history, maintenance records, and performance — that follows the asset through its full lifecycle.
- →Dynamic collateral valuation: systems that use IoT data to update the value of physical assets in real time for lenders. A well-maintained machine in active use is worth more than an idle one, and lending infrastructure has no way to reflect that today.
- →Usage-based financing platforms: financing where payments on physical equipment are tied to actual utilization data rather than fixed schedules — when the machine is producing you pay more, when it's idle you pay less.
- →Liquid secondary markets for physical assets: transparent condition and performance data reduces the information asymmetry that makes used equipment markets inefficient, turning illiquid categories into something approaching a functioning market.
The team
When we back you, we're in it with you.
Accelerator founders get a dedicated Managing Director working alongside them for 20 weeks. Studio founders get a dedicated venture builder and full technical build team. This is a hands-on, high-value, and lower risk way to build a company.
Your Managing Directors — Forum Accelerator
Kevin Corliss
Managing Director
Dave Coen
Managing Director
Deirdre Clute
Managing Director
Neal Sarraf
Managing Director
Your Venture Builder — Forum Studio
Alice Krenitski
Venture Builder
Dallas Price
Venture Builder
Forum was the fractional cofounder that helped us refine our product, and calibrate our vision to resonate with the right investors to raise a Seed. With all the ups and downs in the early days, having the Forum team in our corner helped us keep up the momentum, along with endless resources to help us execute to the best of our ability. So proud to be a part of the Forum community.
Find your path
Two ways in.
Still in idea mode
Builders Between Ventures
You see the opportunity but haven't built the company yet. Builders Between Ventures is a 3-week program with our Studio team to validate your concept, understand whether your idea can become a venture-scale company, and pitch our studio for $250k.
- →You have deep industry experience
- →You haven't taken the leap yet
- →You want a co-builder, not just capital
Already building
Forum Accelerator
You have a company forming — maybe a co-founder, maybe early signal. We invest $100k and work alongside you for 20 weeks with hands-on guidance from our team.
- →You're incorporated or close
- →You have strong founder-market fit
- →You want real investment with real support
Not sure which fits? Email us: founders@forumvc.com