Reasoning over long, messy documents
Contracts, policy packs, inspection histories, regulatory text — inputs measured in tens of pages, where the answer depends on holding several sections in mind at once and citing them precisely.
AI / Claude
This is a capability page, not an endorsement. Here's where Anthropic's Claude models tend to land in the architectures we build, what we ask of them, and where we deliberately don't.
Contracts, policy packs, inspection histories, regulatory text — inputs measured in tens of pages, where the answer depends on holding several sections in mind at once and citing them precisely.
Workflows where the model has to choose the right function, fill its arguments correctly, notice a failed call and recover — rather than confidently narrating an action it never took.
Pulling typed fields out of unstructured input where the hard part isn't parsing, it's deciding what a clause actually means before writing it into your schema.
Migration assistance, test generation, reviewing a diff against a spec — internal engineering tasks where a careful second opinion saves hours and a wrong one is caught by CI.
Whichever model sits underneath, the surrounding system looks the same — and that's the point. Nothing here is Claude-specific enough to strand you.
Published model comparisons go stale within weeks, and they're measured on tasks that aren't yours. We'd rather spend a week building an evaluation from your real cases and let that decide.
You hold the vendor relationship and the bill. We're paid to engineer the system around it, which keeps our advice on model choice unbiased.
For high-volume, low-judgement work a smaller or self-hosted model is often the right call — and cheaper by an order of magnitude.
Any model can be confidently wrong. The design question is what your system does when it is, and that's what we build for.
Bring a handful of real examples and the answer you'd want. That's enough to start an evaluation and stop guessing.