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Claude (Anthropic) Integration & Development
Anthropic's Claude models used for document-heavy, tool-using and agentic features, with evaluation and guardrails built in.
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What Claude is
Claude is a family of large language models from Anthropic, available through a hosted API and through several cloud providers' model marketplaces. It is used for conversational assistants, document analysis, writing and code assistance, and agent workflows where the model calls tools and acts on the result.
Two characteristics matter most when choosing it for a build: it handles long inputs well, which suits contract, policy and report analysis without aggressive chunking, and it follows structured tool-use instructions closely, which suits agents that must call functions with predictable arguments. Neither removes the need for evaluation — they change how you design the system, not whether you test it.
Suitability
And when it is not. Choosing the wrong tool costs more than any implementation detail.
Good fit for
- Analysis, summarisation and extraction over long documents such as contracts, policies and reports
- Agent workflows where the model calls internal tools or APIs and the arguments must be reliable
- Assistants for regulated or sensitive domains where tone, refusals and instruction-following need careful control
- Teams that want a hosted API now but may need to run the same model through an existing cloud provider later
Consider alternatives for
- High-volume, narrow classification where a small specialised model is far cheaper per call and accuracy is comparable
- Workloads that must run fully offline or on-premises with no third-party API, where an open-weight model is the realistic option
- Features that need native image or audio generation; Claude is a text-and-vision understanding model, not a media generator
In practice
01
Document analysis and extraction
Pipelines that read long documents and return structured output — clauses, dates, obligations, summaries — with schema validation on the result and a review step where a wrong answer is costly.
02
Tool-using assistants and agents
Assistants that look up records, call internal APIs and take bounded actions, with permission scoping, logging of every tool call and a hard limit on what the agent can change without confirmation.
03
Retrieval-grounded Q&A
Question answering over a client's own knowledge base, where answers cite the passages they came from so a reader can verify them rather than trust the model.
Considerations
Practical notes from running this in production.
- 01
Model versions are retired on the provider's schedule. A production feature needs a pinned model identifier, an evaluation set and a plan to re-run it when the provider announces a deprecation, otherwise a forced upgrade silently changes behaviour.
- 02
Long context is not free: cost and latency grow with the amount of text you send. Retrieving only the relevant passages is often cheaper and more accurate than sending an entire document set every time.
- 03
Data handling depends on how it is accessed. Terms for retention and training differ between the direct API and a cloud provider's offering, so the access route is a compliance decision to settle before build, not after.
- 04
Prompt injection is a real risk once the model reads untrusted content (emails, web pages, uploaded files) and can call tools. Tool permissions, output validation and treating retrieved text as data rather than instructions are design requirements.
FAQ
Do you use Claude or OpenAI for a given project?
We have used both and pick per workload rather than by default. The decision turns on task fit measured against an evaluation set, cost at expected volume, data-handling terms and any existing cloud relationship. We would rather run a short bake-off on your real examples than recommend a provider from reputation.
Can Claude be used without sending our data to a third party?
Not entirely: it is a hosted model. What you can choose is the access route — the provider's API or a cloud provider's managed offering — and each has different contractual terms for retention and use. If a requirement is that no data leaves your infrastructure, an open-weight model run locally is the honest answer, and we will say so.
How do you stop a Claude-based agent from taking a wrong action?
By limiting what it can do rather than hoping it behaves. Tools are scoped to the minimum permissions, destructive actions require explicit confirmation, every call is logged and the agent is evaluated against adversarial inputs before launch. A capable model reduces mistakes but does not replace those controls.
How long does a Claude development project take?
It depends on scope. A focused first release is usually weeks rather than months, while larger platforms are delivered in phases. We give a written estimate after a short discovery conversation and agree milestones before work starts.
How is Claude development work priced?
We work either to a fixed scope with agreed milestones or on a time-and-materials basis, whichever suits how well the requirements are known. You receive the estimate and its assumptions in writing before committing.
By industry
Where we apply Claude. Tiles marked "Guide" open a dedicated write-up; the rest open the industry page.
Let’s talk about your Claude project
Tell us what you are trying to build or fix. We will come back with scope, approach and an honest view on cost and timeline.