We use paid customer deployments — not speculation — to discover, build, and scale vertical B2B AI products. Customers fund our R&D. Our agent runtime makes each deployment faster than the last.
AI demos are everywhere. Production AI is not. The gap is not models — it is the operational distance between a generic copilot and a workflow that actually runs a business.
Most companies bridge that gap with consultants who don't ship product, or with horizontal tools that don't fit. We do it differently.
Think of it as Every.to's studio model, inverted: where they use media to discover consumer AI products, we use customer-funded engineering to discover B2B agent products. Their CAC is negative because readers want what they make. Ours is negative because customers pay us to find out.
Anyone can call themselves a forward-deployed AI shop. Almost no one has all three of the things below — and the combination is what makes this defensible.
We built cloud computers and a skills layer that let us deploy production agents in days, not months. Every customer engagement adds skills to the library. Each new skill makes the next deployment faster. The system gets cheaper to operate over time — the opposite of a services business.
Our forward-deployed engineers are senior operators and ex-founders, recruited through our technical network. Customers don't get junior consultants — they get product builders who understand both the code and the business. That is rare and not replicable by capital alone.
Our sales co-founder runs a deep network across Mexican mid-market and enterprise. We sign customers weekly in a market with high willingness to pay, weak local AI competition, and strong relationship economics. LATAM is our beachhead; the runtime and products are globally portable.
Most AI agent work is glue: connecting LLMs to systems, handling state, writing one-off integrations, managing tools, retries, evals, and operational edge cases. Every agency rebuilds this from scratch on every project. We don't.
Each agent runs in a persistent sandboxed environment — what we call a cloud computer — with native access to tools, memory, file systems, and integrations. On top, our skills library is a growing catalog of reusable capabilities: CRM operations, document workflows, communication primitives, vertical-specific actions.
New deployments compose existing skills. New verticals add new skills. The library compounds. Deployment time bends down. Gross margin bends up.
| Approach | Time to production |
|---|---|
| Big-firm AI consultancy | 6–12 months |
| Boutique AI agency | 2–4 months |
| In-house team (from scratch) | 3–9 months |
| deployed.engineering | Days to weeks |
Real estate is our flagship vertical. The AI Broker Copilot automates lead intake and qualification, client communication, property matching, valuation support, follow-up, appointment coordination, document collection, CRM updates, and deal pipeline management.
First deployment: $150K committed contract. The product is positioned as a copilot, not a replacement — it supports licensed brokers, sidestepping the regulatory questions that block "AI broker" framing.
Three additional paid deployments are running in parallel: real estate valuation, WiFi installation management, and an ERP workflow interface. These are not scattered bets. They are funded R&D for the next products the studio will extract — each one validating that the runtime generalizes.
Two founders with the rare full stack: technical depth to build the runtime, sales depth to fill the pipeline. A bench of senior engineers and ex-founders working as forward-deployed engineers.
Director of Engineering at Thirdweb. Founder/CTO at Blocktorch. Senior PM at Celonis. SAP. Built the runtime. Multilingual (DE/FR/EN), KIT & INP Grenoble.
Deep enterprise sales network across Mexican mid-market and LATAM enterprise. Owns the customer-acquisition motion.
We are raising an angel / pre-seed round to compound the three advantages above: deepen the runtime, scale the engineering bench, and replicate the LATAM sales motion to convert pipeline into signed revenue.
Turn internal tooling into productized infrastructure. Expand the skills library. Build the workflow modules that ship the next three customers in half the time.
Hire 3–5 senior engineers and ex-founders. More benches means more deployments means faster product extraction.
Formalize the playbook. Hire 1–2 enterprise reps. Convert the $1M pipeline into signed contracts and recurring revenue.
Decide and ship the second vertical agent product — likely valuation, installation, or ERP — turning the next paid deployment pattern into a repeatable SKU.
We're closing a small, founder-friendly round with operators and angels who understand both forward-deployed engineering and vertical B2B distribution.
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