Claude AI integration services
We deliver Claude AI integration services for product teams working with long documents, explicit rules and internal tools. We build the retrieval, quality tests and controls for permissions, spending and human review around Anthropic's model.
01Capabilities
Claude integration for documents, rules and internal tools
We connect the Claude API to document workflows through AI integration work.
- Documents
Document review against accepted answers
We build workflows for contracts, policies, tender packs and case files used in fintech and healthcare. We test whole-file inputs against retrieval on your corpus, using accepted answers to identify missed clauses.
- Rules
Redaction and rewriting to your rules
Redaction, rewriting to a house style and sorting against a long policy each need explicit acceptance criteria. We turn your rules into tests for instruction following and output format, then compare the results on real inputs.
- Agents
Function calls within agreed permissions
Claude plans a task, calls one of your functions and reads the result before continuing. We define the permitted functions and human approval points. Connecting tools through the Model Context Protocol is MCP development.
- Refusals
Human review built into the workflow
For clinical wording, arrears letters and insurance decline notices, we test refusal handling alongside the accepted answers. The integration routes declined parts to a person with the context already assembled, while Claude handles the supported parts of the task.
02Fit
When Claude is the right model, and what else fits
A Claude integration must fit the document task, its volume and the restrictions on processing the files.
| Your situation | What we recommend |
|---|---|
| Long documents, read closely, where a missed clause is the failure | Evaluate ClaudeWe score Claude and an alternative on your own files, using accepted answers to decide which model handles the task. |
| Redaction or formatting against a long written policy | Test the rulesTurn each requirement into a scored check. Compare instruction following on the same inputs before choosing the model. |
| A low-volume workflow already reviewed by a person | Assess the workflowA document tool with manual review can suit a bounded task. We compare the operating cost and review effort before recommending a build. |
| Millions of short, near-identical decisions, and the bill decides feasibility | Evaluate a smaller modelCompare DeepSeek or a task-specific model built with PyTorch against the same quality criteria. The choice follows measured quality and running cost. |
| The files may not leave your own network under any terms | Keep inference localFor files that must stay on your own hardware, we evaluate Llama or Qwen for a self-hosted deployment. |
| Legitimate wording that the selected model repeatedly declines | Design the handoffSplit the task and pass declined parts to a person with the context assembled. Test a second provider where a category of requests remains blocked. |
Scope
We set spending ceilings and define the Anthropic Claude integration's behaviour when they are reached. Your team supplies accepted answers and approves the data and actions in scope. unicrew has built software since 2012, with 100+ senior in-house engineers across six countries and ISO 27001:2022 certified information-security management.
03Delivery
Proving it handles your documents before you depend on it
We carry the agreed document tests through context design and into operation.
- Build a reference set from real documentsWe agree the reference set with your reviewers, for example twenty real tasks with the answers they would accept. It includes difficult files and the rules each output must meet.
- Compare two or three models on the same setWe use the same prompt, inputs and scoring criteria, hiding the model name where reviewers make a subjective judgment. The recorded comparison supports the selection.
- Design and test the contextWe decide what is retrieved, how it is ordered, what is cached and what is left out. Whole files and retrieval are compared on answer quality and cost.
- Operate supported versions and a tested alternativeWe pin supported model versions and plan for provider retirement. Monitoring covers latency and refusal frequency; the second provider stays tested behind the same interface. The feature goes through our QA and test automation practice.
04Case studies
Client work delivered with Claude
On Pet4Me, unicrew's engineer works on the code, screens, tests and documentation with Claude Code, Claude Design, Claude.ai and Claude Cowork, and decides what ships. On WaiverKing, unicrew ran a CRM API migration estimated at 260 hours, delivered in about 80.
See all case studies
MarketplaceHow a Forward Deployed Engineer Helps Pet4Me Keep Shipping with ClaudeFollowing a five-person build phase, one unicrew Forward Deployed Engineer took over the rest of the build and ongoing delivery. Working with Claude, they continue to deliver trust and safety improvements and new features with roughly 80% fewer billed hours per month.5 to 1Delivery team, from the build phase to continuous delivery
SaaSClaude-assisted CRM API migration for WaiverKing, led by a Forward Deployed Engineerunicrew's Forward Deployed Engineer led the migration of WaiverKing's live Mindbody Online integration to a new API version, using Claude Code to analyse anonymized records without disrupting customers.~80 hoursTo complete WaiverKing's migration
05Stack
What sits either side of the model call
These technologies support provider comparison, local-model requirements and the code around the Claude API.
06Questions
Asked before a regulated team signs it off
Deployment terms, refusal handling and retrieval decisions shape the security review for a Claude integration.
Yes. Our engineers build and operate Claude integrations for document, rule and tool workflows.
An architect outside the delivery team reviews the integration design. Our AI QA and evaluation practice tests model behaviour against the agreed tasks.
For delivery with unicrew coordination, choose a managed development team. A scoped product feature is AI and machine learning development.
We route the declined work to human review and test the refusal pattern before changing the design.
We log refusals, let the model handle the supported parts of the task and pass the rest to a person with the context already assembled. If a category of requests remains blocked, we evaluate a second provider against the same requirements.
Claude is available through managed cloud platforms, but that does not mean it runs entirely within your own account.
We review the selected platform's service, region, features and processing terms. Retention depends on the endpoint, enabled features and agreement, with enforcement and legal exceptions. Third-party cloud terms can differ.
For a self-hosted deployment on your own hardware, we evaluate Llama or Qwen. Claude weights are not available for download.
A larger window can support whole-file analysis; it does not settle whether retrieval belongs in the design.
We compare whole-file input and retrieval on your documents, measuring answer quality and cost. The retrieval design also defines which document material reaches the model under the agreed access rules.
Start with a scoping call and three documents showing the task and the rule you need applied.
We return a written recommendation on the model comparison and integration scope. For evaluation, your reviewers help assemble real task examples and acceptable answers. Most engagements start within two to four weeks.
We quote Claude integration work against its document, evaluation and system-integration scope.
- Time and materials: billed hourly, quoted per project.
- Fixed price: outcome based, quoted per project.
- Team extension: billed monthly per team member.
Build a Claude document workflow with clear acceptance criteria
Send three documents and the rule you need applied. We will recommend what to test, how to score the answers and where Claude fits in the workflow.