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Why teams choose Claude for certain work
Claude is a family of large language models from Anthropic. Its large context window means a single request can include a long contract, a lengthy report or a substantial part of a codebase, and the model can reason across the whole thing rather than a small extract. That makes it a strong fit for tasks where losing track of an earlier clause or section would produce a wrong answer.
We don’t treat any one provider as the answer to everything. Claude is a good choice for long-document reasoning, drafting in a consistent voice and careful extraction; for other tasks another model may be cheaper or better. We test on your actual material before recommending.
Typical projects
- Contract and agreement review — highlighting key terms, dates, obligations and unusual clauses for a person to check.
- Report analysis — turning long financial, technical or inspection reports into structured summaries.
- Proposal and document drafting — first drafts built from your templates and past documents, edited by your team.
- Tender and RFP processing for manufacturers and service firms, extracting requirements into a checklist.
- Internal knowledge assistants that answer staff questions from policy manuals and SOPs.
- Code-related tooling for software teams, such as documentation or review assistants.
What we deliver
- Integration through Anthropic’s official API, or through Amazon Bedrock or Google Cloud Vertex AI if your infrastructure already lives there.
- Prompt and output design — instructions and output schemas that return fields your system can store and use.
- Tool use — letting Claude call your own APIs, such as looking up a customer or fetching a document, with your code enforcing permissions.
- Document pipelines — handling PDFs and other files, splitting very large inputs sensibly when they exceed what one request should hold.
- Prompt caching for repeated large inputs, which can reduce cost and response time on supported models.
- Cost controls and logging — token budgets, per-user caps and a record of every request.
How the work runs
We begin with a sample of your real documents and the outputs you’d want from them — for example, twenty contracts and the fields a lawyer or manager would note from each. This becomes the evaluation set. We then design the prompts, choose between Claude’s model tiers (larger models for demanding reasoning, smaller ones for fast, simple tasks), and measure the results against the set.
Once quality is acceptable, we build the production integration: file handling, queues for long jobs, limits, logging and a review screen where people approve or correct outputs. Corrections are kept, so the evaluation set grows with real cases over time.
For outputs that people will rely on, we ask the model to point to the section or clause it used for each finding. That makes review faster, because the reviewer can jump straight to the source, and it makes unsupported statements easy to spot and reject.
What affects timeline and cost
- Document length and format — clean digital PDFs are simpler than scans that need OCR first.
- Volume — occasional analysis versus thousands of documents a month changes the architecture and the usage bill.
- Output complexity — a one-paragraph summary is quicker to validate than a fifty-field extraction.
- Hosting preference — direct API versus a cloud provider such as AWS or Google Cloud.
- Review requirements — legal, financial or medical content needs more human checking built in.
Mistakes to avoid
- Treating the model’s output as final on legal or financial questions. It is a fast first reader; a qualified person still decides.
- Sending whole archives when a section would do. A large context window is useful, but every token has a cost.
- No evaluation set, which leaves quality debates to opinion instead of measurement.
- Hard-coding one model version, which makes upgrades painful when newer models are released.
Frequently asked questions
Is Claude better than GPT?
Neither is better at everything. Each has strengths, and both providers release new models regularly. We test your real tasks on the options and recommend the one that gives the best result for the cost.
Can Claude read scanned documents?
Claude can process images and PDFs, but poor-quality scans are better handled with a dedicated OCR step first. We choose the approach after looking at samples of your actual documents.
Can we use Claude through AWS or Google Cloud?
Yes. Claude models are available through Amazon Bedrock and Google Cloud Vertex AI as well as Anthropic’s own API, which can simplify billing and data arrangements if you already use those clouds.
How is our confidential data handled?
We use the official business APIs, send only what each task needs, and keep logs on your own infrastructure. We recommend reviewing Anthropic’s current commercial terms on data use, as provider policies can change.
What about very long documents?
Most long documents fit in a single request. For very large sets we split them sensibly, process the parts and combine the results, keeping track of which part each finding came from.
Talk to us about claude ai integration
Anthropic's Claude models integrated for long-document reasoning, drafting and analysis work.