AI Startup Engineering Partner
From prototype to production for AI-native startups — agents, RAG, evals, and the unsexy infra that makes them work.
Most AI startups demo well and break in production. We build the unsexy half: the eval harnesses, observability, fallback logic, cost ceilings, and structured-output validation that turns a great prototype into something a paying customer trusts.
The ai startups problems we get called for.
The demo is great, customers churn
Pilot users love it. Paying customers hit edge cases the demo never showed, and there's no eval data to even know how often that happens.
Costs are unpredictable
Some customer sessions cost USD 0.20, others USD 18.00. There's no metering, no caps, no dashboard. Burn rate is a roulette wheel.
Investors are asking about the moat
The product is a thin wrapper over OpenAI. Series A diligence wants to see proprietary data, fine-tunes, eval rigour — and there's none.
The agent breaks under real users
Tool calls fail silently, prompt injections leak system prompts, infinite loops eat tokens, retries make it worse. Production is one ticking incident.
How ai startups engineering should look.
Eval infrastructure first
Golden test sets, automated regression on every PR, production sampling, human-in-the-loop scoring. AI quality treated like test coverage.
Cost observability + ceilings
Per-session, per-user, per-feature cost dashboards. Hard caps that fail closed. Cost-per-conversation tracked next to revenue-per-conversation.
Production-grade agents
LangGraph state machines, retries with circuit breakers, deterministic fallbacks, schema-validated tool calls, prompt-injection defences.
RAG that works
Hybrid retrieval (keyword + vector), reranking, freshness handling, eval-driven chunking strategy. Not the demo version.
The moat layer
Proprietary eval data, structured human feedback, fine-tune candidates, distillation pipelines — the things investors want to see in diligence.
Multi-model strategy
OpenAI + Anthropic + open-weights via Bedrock / Together. Per-task model routing, seamless swap, no provider lock-in.
Outcomes, measured.
Battle-tested for ai startups.
The capabilities behind the work.
AI Solutions
LLM agents, retrieval pipelines, and ML integrations that unlock real business leverage — not demos.
SaaS Product Development
Zero to revenue. Multi-tenant architecture, billing, auth, dashboards, analytics — done properly.
API Integrations
Payments, identity, messaging, analytics — integrated with rock-solid reliability and clean abstractions.
Common questions about ai startups.
We're pre-seed — is this overkill?+
No. The cheapest time to bake in eval infrastructure and cost observability is week 1, not month 12. We scale the rigour to your stage.
Can you fine-tune models for us?+
Yes — when it's the right call. Fine-tuning is right when you have repeatable structured tasks at scale or strict latency / cost ceilings. Most early-stage startups should focus on retrieval + better prompting first; we'll tell you which.
What about our data and model training?+
We use enterprise tiers (OpenAI, Anthropic) that contractually exclude your data from model training, or self-host open-weights for the most sensitive workloads. Your data, your control.
Can you help us prep for Series A diligence?+
Yes — investors increasingly diligence the AI stack itself. We package eval rigour, cost observability, prompt versioning, and architectural choices into a diligence-friendly summary.
Building something in ai startups?
30-minute scoping call. Concrete plan and fixed pricing in writing within a week.