Services
AI Applications
Copilots, document intelligence, retrieval systems, and customer-facing AI features that hold up in production.
An LLM feature that's impressive in a demo and unreliable in production isn't a shipped feature — accuracy, latency and cost all have to hold at once, under real traffic.
The gap between a working demo and a production system is exactly where most LLM applications stall. Naive retrieval alone fails to surface the right context a large share of the time — and it's retrieval quality, not the underlying model, that's usually the actual bottleneck holding an application back from reliable answers.
From internal copilots grounded in your company's knowledge to customer-facing AI features inside your product, we take LLM applications from prototype to production. Every response is grounded with source citation — no unverifiable output. Architecture stays model-agnostic. Cost and latency budgets are defined and tested before launch, not discovered after.
Retrieval gets the same engineering rigor as the model call itself — a query classifier routes simple questions down a fast, cheap path and sends genuinely complex ones through deeper retrieval, rather than running everything through the most expensive pipeline available. Before launch, every application is scored against a held-out test set for faithfulness to its sources and answer accuracy against a defined threshold — the same discipline as automated testing for any other software, just measuring something less binary than pass or fail.
Frequently asked
RAG or fine-tuning for a company knowledge base?
RAG, for almost every business use case — increasingly the two aren't an either/or. The strongest production systems now fine-tune the model on your organisation's tone and output format, then use RAG to inject the current facts, so style stays consistent and knowledge stays current without retraining every time something changes.
How do you evaluate an LLM application before launch?
We build a test set of real questions with known-correct answers, run it against the application, and score both answer accuracy and faithfulness to the retrieved source material against a defined threshold before it ships — the same discipline as automated testing for any other software.
How do you stop an AI agent from hallucinating in production?
Grounding every answer in retrieved source material with an explicit instruction to answer only from that context — and say so when it can't — plus confidence scoring that routes uncertain answers to a person instead of guessing.
Why does retrieval quality matter more than which model we use?
Because a wrong or missing piece of context breaks the answer regardless of how capable the underlying model is. Retrieval failing to surface the right passage is the single most common cause of a wrong answer in production, well ahead of the model itself getting something wrong once it has the right context in front of it.