Anthropic

We use Claude for assistants and copilots, document understanding and summarisation over long inputs, extraction and classification, code-related tasks, and tool-using agents. Its large context window is a real advantage when a feature needs to reason over a lot of material at once — long contracts, extensive knowledge bases, or whole codebases — without heavy chunking gymnastics.

Claude's strong instruction-following and steerability make it a dependable choice for agentic systems, where the model must plan, call tools, and stay within defined boundaries reliably across many steps. We build the retrieval, validation, and orchestration around it that turns that capability into a product users can trust.

Even the best model is non-deterministic, so we engineer for reliability rather than hoping for it. We ground Claude's responses in your data through retrieval (RAG), so answers come from your knowledge rather than the model's guesses, and we build evaluation suites that measure output quality against real cases so changes are validated with evidence.

Guardrails complete the picture — input and output validation, topic boundaries, and escalation paths to a human when the model is out of its depth. This is the difference between an AI feature that's safe to put in front of customers and one that's a liability the first time it's surprised.

We architect AI features behind a clean provider abstraction, so Claude, GPT, or another model can be used per task — or swapped as new models ship — without rewriting your product. That keeps you on the best available model over time and preserves your leverage on cost and terms.

In practice that often means using different models for different jobs: a fast, cheap model for simple calls and a frontier model like Claude for the hard reasoning. We help you make those trade-offs deliberately, measuring quality and cost rather than guessing.