Claude Integration Services

Claude-powered document processing, contract review, legal document analysis, financial report extraction, compliance document classification, and long-document summarisation. Claude's 200K token context window handles documents that other models require chunking for: a 150-page contract, a full annual report, or an entire regulatory submission fits in a single context window, which means the model can reason across the whole document rather than independently processing fragments.

Document processing pipelines built with structured output enforcement: every extraction output is validated against a JSON Schema before being written to your system. If Claude returns a date in an unexpected format, a number as a string, or omits a required field, the validation layer catches it before it reaches your database. For contract analysis: key clause identification, obligation extraction, date and party name extraction, and risk flag classification, all returned as structured data your downstream system can act on, not a prose summary someone has to read. Human review workflows are built for the high-stakes case: a reviewer sees the original document alongside the extracted fields, with confidence scores on each extraction, and accepts or corrects each field individually. Corrections are logged as training data to improve accuracy over time. Accuracy validation runs against a labelled test set before deployment: no integration goes live without knowing its extraction accuracy on your document types.

AI assistants that answer questions from your organisation's documents, data, and knowledge base, using RAG pipelines that retrieve relevant passages and pass them to Claude for synthesis. Internal knowledge assistants, product documentation Q&A, and customer-facing help systems that give accurate, sourced answers rather than hallucinated responses.

Multi-turn conversational AI built with Claude, customer-facing chatbots, internal support assistants, interview and assessment tools, and AI form completion. Conversation state management, context window handling for long conversations, and escalation to human agents when confidence is low.

Content generation systems powered by Claude, structured content production for marketing, product descriptions, reports, and personalised communications. Prompt systems with style guides, format constraints, and quality validation. Batch processing pipelines for high-volume content generation with review workflows.

Claude agents that use tools to complete multi-step tasks, database queries, API calls, web search, file operations, and code execution. Tool definitions designed for reliable tool selection and parameter passing: tool descriptions written in plain language that makes the intended use unambiguous, parameter schemas with description fields that guide parameter value selection, and input validation on the application side before any tool executes.

Agent workflows for research, data enrichment, automated reporting, and workflow automation tasks. Error recovery built into every agent workflow: when a tool call fails (API rate limit, database connection error, malformed response), the agent receives the error and retries with backoff or switches to an alternative approach, rather than the agent loop failing silently. Guardrails are implemented as hard constraints that run before tool execution: a database agent cannot execute a DELETE or UPDATE statement, a customer-facing agent cannot access internal cost data, a research agent's web searches are logged for audit. Agent state persistence for long-running workflows: a research agent working through a multi-hour task checkpoints its progress to a database so a crash or timeout does not restart the entire workflow from the beginning. Parallel tool use for agents that need to call multiple APIs simultaneously: Claude's tool use API supports requesting multiple tool calls in a single response, and our runtime executes them in parallel to reduce total task completion time.

RAG pipelines connecting Claude to your proprietary data, product databases, customer records, internal documentation, and knowledge management systems. Vector databases (Pinecone, Weaviate, pgvector), embedding pipelines, and retrieval strategies optimised for your data type and query distribution. Claude answers about your specific data, not generic knowledge.

Production Claude integrations start with structured system prompt architecture, role definition, hard constraints, output format specifications, and domain grounding. Few-shot examples for complex output formats. Chain-of-thought for multi-step reasoning tasks. Prompts designed to produce consistent outputs across diverse user inputs, not optimised for the inputs you thought of.

We build evaluation frameworks before deploying any Claude integration, test cases covering your real distribution of user inputs, metrics for output quality, format compliance, and accuracy, and pass thresholds agreed before development starts. No Claude integration goes to production without being measured against real inputs first.

Context window management for long documents and conversations, summarisation strategies, sliding window approaches, and RAG for context that exceeds the window. Prompt caching configuration for repeated large system prompts. Model routing for cost optimisation. Cost monitoring per interaction so you can track inference costs as usage scales.

Production monitoring for Claude integrations: request volume, latency, cost per interaction, output quality metrics, and error rates. Alerting when quality metrics degrade or costs increase unexpectedly. Logging of inputs and outputs for debugging and evaluation updates. The observability layer that lets you manage the integration as a production system.