We build the software around the model — tenancy, queues, evaluation, observability and cost control — so what you ship keeps working after the demo lands.
Wiring an LLM call into an application takes an afternoon. What takes real engineering is everything around it: what happens when the provider is slow, how you know the answer quality dropped after a prompt change, how one tenant's data stays invisible to another, and what a single request actually costs at ten thousand a day.
These are ordinary software problems, which is exactly why AI-first teams miss them. We build the unglamorous layer — row-level security, retry semantics, evaluation harnesses, per-call cost accounting — because it is the difference between a product and a prototype with good marketing.
Tenant isolation enforced at the database with Postgres row-level security, not by hoping every query remembers a WHERE clause. Verified by tests that assert isolation directly.
pgvector-backed retrieval with real chunking strategy and evaluation. We measure retrieval quality before blaming the model for a bad answer.
Tool-using agents with bounded autonomy — explicit tool contracts, idempotent execution and a transcript of every decision the system made.
Anything slow moves to a worker. BullMQ on Redis, with retry policy, backoff and dead-letter handling, so a provider outage delays work instead of losing it.
Golden datasets and regression suites for model-dependent behaviour, so a prompt change that quietly degrades accuracy fails CI instead of reaching customers.
Expo and React Native shipping through EAS to TestFlight and the App Store, Next.js on the web. The same team builds the API and the client.
The core component is non-deterministic and gets worse without warning. That changes the engineering: you need evaluation suites rather than only unit tests, cost as a first-class metric, graceful degradation when a provider is slow, and observability at the level of individual model calls. Teams that treat an LLM as just another API discover this in production.
Whatever fits the task and the budget, behind an abstraction so it can change. In practice that is usually Claude or GPT for reasoning-heavy work, a smaller and cheaper model for classification and extraction, and a self-hosted embedding model where volume makes API pricing unattractive. Provider lock-in is a design choice we avoid.
Yes, and it is often the better arrangement. We embed to build the foundation — architecture, tenancy, evaluation, CI — and hand it over with your engineers already fluent in it. The alternative, where an agency keeps the knowledge and you keep the invoice, serves nobody.
Scoped per engagement. We deploy in your region, use zero-retention provider endpoints where the contract requires it, and can run open-weight models on your infrastructure where data cannot leave. For regulated work we scope this before writing code, not after.
Voice agents on live telephony, multi-tenant fleet operations software with verified tenant isolation, a marketplace with payments and wallet reconciliation live on the App Store, identity verification integrated with a national ID system, and semantic matching over pgvector. Real users, real money, real production incidents.
A paid technical discovery of one to two weeks that ends with an architecture, a scoped plan and a fixed price for phase one. You keep everything produced whether or not you continue with us.
If the demo works and production doesn't, that's the conversation we're best at having.
