LLM apps, RAG, and agents grounded in your data — useful assistants, not party tricks.
LLMs are only useful when they answer with your data, follow your rules, and hand off cleanly when they shouldn’t answer at all. We build LLM apps with retrieval, guardrails, and evaluations from day one — because the difference between a demo and a product is exactly those three things.
It’s the right fit for teams turning a document library into a searchable assistant, product teams adding AI features, and ops teams that want agents to handle first-line work.
Retrieval-augmented answers over your data, with automated evaluations we can show you.
Every engagement ships against outcomes we agree up front.
Answers grounded in your documents, cited to source.
Chat, agents, and copilots for your product.
Multi-step agents with tool use — behaviour bounded.
Refusals, safety filters, and PII handling by default.
Slack, WhatsApp, your product, your APIs.
Automated evals for accuracy, safety, and cost.
A real sequence — each step earns the next.
A working thin-slice in your data.
Automated evals and human review.
Guardrails, retries, caching, and cost tuning.
In production with monitoring.
Concrete artifacts you take away from the engagement.
Retrieval and citation are the default.
Model choice, self-hosting option, and PII controls.
Automated evals catch regressions before your users do.
Model routing and caching to keep bills sane.
Retrieval-augmented answers grounded in your content, a strict prompt, and refusals on out-of-scope questions.
No — not by default. We use provider APIs with training opt-out, or self-hosted open-weight models on your infra.
Depends on quality, latency, and cost. We route between models — and can self-host where needed.
Depends on model, prompt size, and traffic. We show a dashboard and tune for cost.
A grounded assistant is typically 4–8 weeks; agents take longer.
Tell us what you’re building — we’ll reply within one business day.