Conversational AI for automated research interviews
- 48 hrs
- from interview completion to usable insights
Claude Integration Services
Claude integrations built for production, not the demo.
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 optimization, 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 optimization, model selection, context management, and caching strategies
Recent outcomes
Conversational AI · Research platform
48 hrs to insights
Built a Claude-powered conversational AI that runs automated research interviews and delivers usable insights within 48 hours of interview completion.
Healthcare AI · Remote patient monitoring
20% faster clinical decisions
Built a Claude-powered remote patient monitoring system that cut clinical decision-making time while maintaining HIPAA compliance.
4.9
on ClutchThe problem
Sound familiar?
Claude produces good results in testing but inconsistent outputs with real user inputs in production?
Context window costs growing unexpectedly as you handle longer documents or longer conversations?
Short answer
RaftLabs builds Claude API integrations for clients across the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia: document analysis, AI assistants, RAG pipelines, and workflow automation. A focused integration runs $10,000-$30,000. A full AI product with multiple Claude-powered features runs $30,000-$100,000+. Fixed cost, 4-12 weeks.
Key takeaways
- A focused Claude integration (one use case) runs $10,000-$30,000 on a fixed-cost basis.
- A full AI product with multiple Claude-powered features runs $30,000-$100,000+.
- Delivery timelines run 4-12 weeks depending on scope and number of use cases.
- RAG pipelines give Claude access to your proprietary knowledge base and internal documents.
- Prompt caching can reduce Claude API costs by up to 90% for applications with repeated system prompts.
- Claude's 200K token context window supports processing a full contract or annual report in a single pass.
Trusted by
It worked in the demo. Then the real users showed up.
A Claude integration sails through the demo. Clean inputs, expected questions, tidy answers. Ship it, and the real distribution arrives: half-formed queries, adversarial inputs, a 150-page contract instead of a paragraph, and the same prompt returning three different formats across three runs.
The model was never the problem. The prompt architecture, the context management, the evaluation harness, and the monitoring were. That reliability layer is the difference between a demo and a product.
The interface is the least interesting part. The engineering underneath it is the product.
Getting Claude to produce a useful answer in a demo is easy. Getting consistent, accurate, correctly-formatted answers across thousands of real queries, with edge cases, adversarial inputs, and production load, takes prompt architecture, context management, tool integration, evaluation, and monitoring.
According to McKinsey's State of AI 2025 report, 65% of organizations now regularly use generative AI, yet only 1% describe their AI rollouts as mature, meaning fully integrated into workflows and driving measurable outcomes. The gap between using the API and running a reliable production system is where most integrations stall, and it is entirely an engineering problem.
RaftLabs has shipped 100+ products since 2015, including 20+ AI products in the last 24 months, for clients including Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin, rated 4.9/5 by clients on Clutch. Recent Claude work: a conversational AI for automated research interviews that delivers usable insights within 48 hours of interview completion, and a remote patient monitoring system, built on Claude 3 Sonnet, that cut clinical decision-making time by 20% while maintaining HIPAA compliance. GDPR, HIPAA, and SOC 2 requirements are scoped in week 1, not retrofitted before launch. One team scopes the integration, builds it, and hands it over.
Claude earns its place when the workflow is real and the data is yours.
Everything on the left should already be true for your operation. Even one thing on the right, and a hosted assistant or an off-the-shelf tool is the smarter first step.
A fit
01
A production use case with real volume: document analysis, a knowledge assistant, workflow automation, or a conversational interface.
02
Proprietary data or an internal knowledge base Claude needs to reason over, plus existing systems (a CRM, a document management platform, or a customer product) to integrate against.
03
You need consistent, monitored outputs at scale, and budget for a fixed-cost build from $10,000.
Not a fit
- A one-off prototype or demo where an off-the-shelf tool already does the job.
- No proprietary data or system to ground Claude in, just generic Q&A a hosted assistant already covers.
- A workflow that depends on human judgment the model was never given context for.
What we build
What we build with Claude
- 01Document analysis and extractionClaude-powered document processing: contract review, legal and financial document analysis, compliance classification, and long-document summarization. The 200K token context window fits a 150-page contract in one pass, so the model reasons across the whole document, and every extraction is validated against a JSON schema before it reaches your database, with high-stakes fields routed to human review.
- 02Knowledge base assistantsAI assistants that answer questions from your organization's documents 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.
- 03Conversational interfacesMulti-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.
- 04Content generation pipelinesContent generation systems powered by Claude for marketing copy, product descriptions, reports, and personalized communications. Prompt systems with style guides, format constraints, and quality validation, plus batch pipelines for high-volume production with review workflows.
- 05Tool use and agent workflowsClaude agents that use tools to complete multi-step tasks: database queries, API calls, web search, file operations, and code execution. Error recovery, hard guardrails before execution, and progress checkpointing mean a failed tool call retries instead of failing silently and a crash never restarts the task from the beginning.
- 06Claude with custom data accessRAG pipelines connecting Claude to your proprietary data: product databases, customer records, internal documentation, and knowledge management systems. Vector databases like Pinecone, Weaviate, and pgvector, embedding pipelines, and retrieval strategies tuned to your data type and query distribution, so Claude answers about your specific data, not generic knowledge.
How we engineer for consistency
- 01
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 and chain-of-thought produce consistent outputs across diverse user inputs, not just the inputs you thought of.
- 02
Evaluation before deployment
We build evaluation frameworks before deploying any integration: test cases covering your real distribution of user inputs, metrics for output quality, format compliance, and accuracy, with pass thresholds agreed before development starts. No integration reaches production without being measured against real inputs first.
- 03
Context and cost management
Context window management for long documents and conversations: summarization, sliding-window approaches, and RAG for context that exceeds the window. Prompt caching for repeated system prompts, model routing for cost, and per-interaction cost monitoring so you can track inference spend as usage scales.
- 04
Monitoring and observability
Production monitoring for request volume, latency, cost per interaction, output quality metrics, and error rates, with alerting when quality degrades or costs spike. Input and output logging for debugging and evaluation updates, so you can manage the integration as a production system.
What would Claude handle in your product?
Walk us through the use case. We'll tell you how we'd engineer it for production scale and what it costs to build.
How it works
From scope to shipped
Every Claude integration follows the same four phases. Scope is locked and price is fixed before development starts.
- Week 101
Discovery and scope
We map the use case, the data sources, and the user workflow. You leave week 1 with a written scope document, a system prompt architecture plan, and a fixed-price quote. No development starts without your sign-off.
- Weeks 2-302
Prompt architecture and design
System prompt design, output format specifications, and few-shot examples before any production code. Prompt decisions made here cost a fraction of what they cost in week 8. The architecture is locked before the build starts.
- Weeks 4-1203
Build, integrate, and evaluate
API integration, RAG pipeline, tool configuration, and evaluation framework running in parallel. Working integration at a staging URL by the end of sprint one. Bi-weekly demos. No integration ships without passing agreed accuracy thresholds.
- Weeks 12+04
Deploy and monitor
Production deployment with cost and quality monitoring activated on launch day. 8 weeks of post-launch support included. Alerting configured before go-live.
What clients say
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Paula Castro
IrelandReservations Officer, City Break Apartments
Working with RaftLabs has been amazing. The team is super responsive and quick to address our needs. They built a booking platform that's been a game changer for our team and our guests.
01 / 03
Where you land depends on scope, not negotiation:
- Focused integration, $10,000-$30,000
- One use case (document Q&A, an AI assistant, or content generation) with prompt architecture, a RAG pipeline, and basic monitoring, in 4 to 12 weeks.
- Full AI product, $30,000-$100,000+
- Multiple Claude-powered features, custom tool integrations, a user-facing interface, and production monitoring.
What it costs
Claude integration, starting at $10,000.
Prompt architecture, RAG pipeline, tool use, cost optimization, and monitoring, the reliability layer that makes Claude a trustworthy part of your product.
Starts at $10,000
A focused integration, one use case with prompt architecture and monitoring, ships in 4 to 12 weeks. Start there and add tool use or a second use case once it's proven.
Most teams start with one Claude-powered use case, prove it in production, then expand into a full AI product once the pattern works.
No hourly billing
Once we scope your first use case, that price is locked in writing. No hourly billing, and a scope change is a priced request you approve first, never absorbed onto the invoice.
One team, start to finish
The team that scopes your integration is the team that ships it. No handoff after the contract is signed. The people you meet in week 1 deliver in week 12.
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Read moreFrequently 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 optimize 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 optimization strategies we apply: (1) Prompt compression, removing unnecessary text from system prompts while preserving effectiveness. (2) Context management, summarizing 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.
Yes. We sign mutual NDAs before any scoping conversation. Most Claude integration projects involve proprietary data, internal knowledge bases, or competitive workflows, so we treat confidentiality as a baseline condition, not a negotiation point.
Yes. Most Claude integration work connects Claude to existing systems, an internal knowledge base, a CRM, a document management platform, or a customer-facing product. We design the integration layer around your existing APIs and data schemas. If your system lacks an API, we build a connector as part of the project scope.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Claude Integration Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.