AI & LLM engineering that ships to production.
We build the AI features that actually make it past a demo: retrieval over your own data, autonomous agents, copilots embedded in your product, and document intelligence — with the evals, guardrails and cost controls that keep them reliable in the real world.
From "we should use AI" to a feature your users trust.
Most AI features fail after the demo, not before it — wrong answers, runaway costs, no way to tell when the model is guessing. We design for that from day one.
Retrieval & chat over your data (RAG)
Search and Q&A grounded in your own docs, tickets or knowledge base — with citations, not confident guesses.
Autonomous & multi-step agents
Agents that plan, call tools and complete real tasks — with human approval steps wherever the stakes call for it.
In-product copilots
An assistant embedded directly in your app's workflow — drafting, summarizing, or automating the steps your users repeat daily.
Document intelligence
Extraction, classification and summarization across contracts, invoices, reports and other unstructured documents.
Evals & guardrails
Test suites that catch regressions before your users do, plus input/output filtering, rate limits and fallback behavior.
Cost & latency tuning
Model routing, caching and prompt/context optimization so AI features stay fast and don't quietly eat your margin.
Model-agnostic by design.
We pick the model and stack that fit your data, latency and budget — not the one we happen to know best.
We treat AI features like production software.
Evals before launch, not after
Every AI feature ships with a test set and success metric — so "it feels right" isn't the bar.
Guardrails by default
Input validation, output filtering and human-in-the-loop steps wherever a mistake would be costly.
Cost is a design constraint
We size the model to the task and monitor spend from day one, not after the first surprise invoice.
Common questions
Which AI models do you use?
Whichever fits the job best — usually a mix of OpenAI, Anthropic Claude and open-weight models, chosen per task on accuracy, latency and cost. We don't lock you into one vendor.
How do you stop the AI from making things up?
Grounding answers in your actual data (RAG) with citations, constrained output formats, automated evals against known-good answers, and guardrails that catch low-confidence responses before they reach a user.
Can you add AI to an existing product?
Yes — most of our AI work is exactly that: adding a well-scoped AI feature to a product that already exists, integrated with your current backend and auth.
How long does a first AI feature take to ship?
A focused first version — one clear use case, evaluated and guardrailed — typically ships in a few weeks. Scope is set together on the discovery call so the estimate is real, not a guess.
Related services
Data Engineering
Pipelines and vector search that feed your AI features clean, current data.
Learn moreWeb Applications
The product surface your AI features live in — fast, type-safe, SEO-ready.
Learn moreAutomation & Integrations
Wire AI into the tools and workflows your team already uses.
Learn moreHave an AI feature in mind? Let's scope it.
Tell us the problem you're solving. We'll reply within one business day with honest feedback and the fastest realistic path to shipping it.