• Claude produces good results in testing but inconsistent outputs with real user inputs in production?

  • Context window costs scaling unexpectedly as you handle longer documents or longer conversations?

Claude Integration Services

Claude is Anthropic's most capable AI model -- strong on nuanced reasoning, long-context analysis, and instruction-following. Integrating Claude into a production product is a different problem from using the API in a demo: prompt architecture for consistent outputs, context window management for long documents, tool use configuration, cost and latency optimisation, and monitoring across real user inputs.
We build Claude integrations for production use cases -- document analysis, AI assistants, knowledge retrieval, workflow automation, and conversational interfaces -- with the reliability engineering that makes them viable products.

  • Claude API integration for document analysis, Q&A, content generation, and workflow automation

  • Prompt engineering and system prompt architecture for consistent, production-grade outputs

  • RAG pipelines that give Claude access to your knowledge base and proprietary data

  • Claude API cost optimisation -- model selection, context management, and caching strategies

RaftLabs builds Claude API integrations for production applications -- document analysis systems, AI knowledge assistants, conversational interfaces, content generation pipelines, and workflow automation using Anthropic's Claude models. Claude integration services cover API integration, prompt engineering, RAG pipeline development, tool use configuration, context management, and cost optimisation for the Claude API. Most Claude integration projects deliver in 4--12 weeks at a fixed cost depending on complexity.

Vodafone
Aldi
Nike
Microsoft
Heineken
Cisco
Calorgas
Energia Rewards
GE
Bank of America
T-Mobile
Valero
Techstars
East Ventures

Claude in production is an engineering problem, not just a prompt problem

Getting Claude to produce a useful answer in a demo is easy. Getting it to produce consistent, accurate, appropriately-formatted answers across thousands of real user queries -- with edge cases, adversarial inputs, and production load -- requires prompt architecture, context management, tool integration, evaluation, and monitoring.

We build Claude integrations that work in production, not just in demos.

What we build with Claude

Document analysis and extraction

Claude-powered document processing -- contract review, legal document analysis, financial report extraction, compliance document classification, and long-document summarisation. Claude's long context window handles documents that other models require chunking for. Built with output schema enforcement, accuracy validation, and human review workflows for high-stakes extractions.

Knowledge base assistants

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.

Conversational interfaces

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

Content generation pipelines

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.

Tool use and agent 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. Agent workflows for research, data enrichment, automated reporting, and workflow automation tasks.

Claude with custom data access

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.

Claude API integrations built for production scale and reliability

Prompt architecture, RAG pipelines, tool use, cost optimisation, and monitoring. Fixed cost delivery.

How we build Claude integrations

Prompt architecture for consistency

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.

Evaluation before deployment

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 and cost management

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.

Monitoring and observability

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.

Claude integrations that work reliably at production scale

We engineer the reliability layer that makes Claude a trustworthy part of your product. Fixed cost.

Let's talk about your project

Tell us the use case, the data you need Claude to access, and what the integration needs to do. We'll scope the system and give you a fixed cost.

Frequently asked questions

We integrate with the current Claude model family from Anthropic: Claude Opus (highest capability, for complex reasoning and long-context tasks), Claude Sonnet (balanced capability and cost, the most commonly used model for production applications), and Claude Haiku (fastest and most cost-effective, for high-throughput simpler tasks). We help you select the right model for your specific use case based on the required reasoning complexity, context length, latency requirements, and cost per call. We also design systems to route between models -- using Haiku for simple classification and Sonnet or Opus for complex analysis -- to optimise cost without sacrificing output quality.

Claude has one of the longest context windows available -- 200K tokens for Claude 3 models -- making it well-suited for long document analysis, whole-contract review, long conversation history, and multi-document synthesis. For use cases where documents fit within the context window, Claude can process them directly without chunking. For document sets that exceed the context window, we build RAG pipelines that retrieve the most relevant passages for each query rather than loading the full document. The choice between direct context loading and RAG depends on your use case: direct loading is simpler and preserves full document coherence; RAG scales to document sets of any size.

Claude API costs are driven by token consumption -- input tokens (prompt + context) and output tokens (model response). Cost optimisation strategies we apply: (1) Prompt compression -- removing unnecessary text from system prompts while preserving effectiveness. (2) Context management -- summarising conversation history rather than appending the full history to every request. (3) Model routing -- using Claude Haiku for simple tasks and Sonnet/Opus only where complexity warrants it. (4) Prompt caching -- Anthropic's prompt caching feature reduces costs by up to 90% for applications that repeat large system prompts across many requests. (5) Output length control -- constraining response length where shorter answers are sufficient. We monitor cost per interaction in production and report on efficiency.

A focused Claude integration -- one use case (document Q&A, AI assistant, or content generation) with prompt architecture, RAG pipeline, and basic monitoring -- typically runs $10,000--$30,000. A complete AI product with multiple Claude-powered features, custom tool integrations, user-facing interface, and production monitoring runs $30,000--$100,000+. Cost depends on the number of use cases, complexity of the RAG pipeline, custom tool integrations required, and UI/UX development included. We scope every project before pricing it.