Generative AI development builds a product from the ground up. Generative AI integration puts AI capabilities into the product you already have. Most businesses don't need a new AI platform. They need their existing CRM, ERP, mobile app, or internal tool to do something it couldn't do before, draft an email, summarise a document, answer a question, or generate a report. We build the integration layer that adds those capabilities without rebuilding the product.
Competitors added AI features to their product last year, you're still planning?
Your team wants AI capabilities but your dev team doesn't know where to start?
In short
RaftLabs integrates generative AI into existing applications, adding GPT-4o, Claude, Gemini, and Llama capabilities to your web app, mobile app, or internal tools without rebuilding from scratch. We handle integration architecture, prompt engineering, RAG data connectivity, output validation, and production deployment. A focused AI feature integration takes 6 to 12 weeks. Every engagement is fixed cost with scope defined before we start.
Trusted by
Generative AI integration is about your existing product
Most businesses have a product. It works. Users rely on it. The question isn't whether to rebuild it, the question is which AI capabilities to add to it, and how to add them without breaking what works.
We've added AI to existing products across every major framework. React and Next.js frontends, Django and Rails backends, mobile apps, internal tools, enterprise software. The pattern is the same: we understand what the AI needs to do, design the integration layer, and add the capability to the existing product without a full rebuild.
The result is a product your users already know, now with AI capabilities they didn't have before.
For AI that retrieves answers from your documents and databases, we build RAG pipelines. For document reading and structured data extraction, see AI document intelligence.
Capabilities
Generative AI capabilities we integrate
AI writing and content generation
Add AI drafting to any text input in your application. CRM email drafting, report generation, proposal writing, product description creation. The AI generates a first draft. Users edit and send. Productivity goes up without replacing the human judgment that matters.
Document summarisation and analysis
Add AI reading to your document management system, inbox, or data platform. Long documents summarised in seconds. Key information extracted from contracts, reports, and emails. Classifications applied automatically. What took an analyst an hour takes a second.
Conversational search and Q&A
Replace keyword search with natural language search. Users ask questions in plain English and get answers from your data, product docs, support articles, internal knowledge base, or database records. Relevant, cited, accurate responses instead of a list of links to sort through.
AI classification and routing
Classify incoming data, support tickets, emails, form submissions, transactions, automatically. Route to the right team, apply the right label, trigger the right workflow. No human in the loop for routine classification. Humans review the edge cases.
Code generation and developer tools
Add AI assistance to developer-facing products. Code completion, natural language to SQL, test generation, documentation drafting. If your product is used by developers, AI assistance is now a table-stakes feature.
Structured data extraction
Use AI to extract structured data from unstructured inputs, invoices, forms, emails, PDFs. The output is clean, structured JSON that feeds directly into your database or workflow. We use function calling and structured output modes to make extraction reliable.
Technology
The models we work with
OpenAI (GPT-4o, GPT-4 Turbo)
The most capable reasoning models for complex tasks. GPT-4o for multi-modal use cases (text + images). Function calling for reliable structured output. Strong for complex reasoning, code generation, and long-context tasks.
Anthropic Claude
Claude 3.5 Sonnet is particularly strong for long-document analysis, nuanced writing, and instruction-following. 200K context window makes it practical for processing large documents without chunking. Good default for document intelligence and writing use cases.
Open-source models (Llama, Mistral)
For high-volume use cases where per-token cost is prohibitive, we deploy open-source models on your own infrastructure. Lower latency, no token costs, full data privacy. We handle model selection, deployment, and fine-tuning.
Want to add AI to your existing product?
Tell us what your product does today and what you want AI to do. We'll design the integration and give you a fixed cost.
We start by identifying exactly which AI capability adds the most value to your existing product, and what a successful integration looks like. Most products can benefit from AI in multiple ways; we help you prioritise and define the first integration with clear success criteria.
Existing product and workflow review
AI capability mapping to your product's jobs-to-be-done
Success criteria and output format definition
Integration scope agreed before any development begins
We select the right model for your use case, cost, context window, reasoning capability, and latency all factor in. If your integration needs to use your proprietary data, we design the RAG architecture or fine-tuning approach. You get a technical design document before we build.
Model evaluation and selection for your specific task
RAG architecture design if your data needs to be indexed
Data pipeline and embedding strategy
Cost estimation based on your expected usage volume
We build the API integration layer, the prompt engineering, and the data flow between the AI model and your existing application. Streaming responses, rate limit handling, fallback logic, and error states are all designed in. The AI feature connects to your product without a full rebuild.
API integration with model provider
Prompt engineering and guardrail design
Streaming response handling for low-latency UX
Retry logic, rate limit handling, and fallback design
We design and build the user-facing feature in your existing product. The AI capability should feel native, not bolted on. We design the input interface, output display, loading states, and error handling so users know what the AI is doing and can trust the output.
UI component design for the AI feature
Loading, streaming, and error state handling
Human review or approval flow if needed
Integration into your existing design system
We test the integration against real inputs from your product environment. Hallucination patterns, edge cases, and high-risk outputs are identified and handled before launch. Post-launch monitoring tracks accuracy, latency, user adoption, and token costs.
Integration testing with real product data
Hallucination and edge case identification
Accuracy baseline measurement
Production monitoring for cost, latency, and usage
Which AI feature would make your product 10x more useful?
Tell us what your product does and what you want AI to add. We'll design the integration and give you a fixed cost.
Generative AI development builds a new AI-native product from scratch. Generative AI integration adds AI capabilities to an existing product or workflow. If you have a CRM and you want it to draft follow-up emails, that's integration. If you're building a new AI research assistant that doesn't exist yet, that's development. Integration is usually faster and cheaper because you're not starting from zero, you're extending something that already works.
We've integrated GPT-4o and GPT-4 Turbo (OpenAI), Claude 3.5 Sonnet and Claude 3 Opus (Anthropic), Gemini 1.5 Pro (Google), Llama 3 (Meta), Mistral, and Cohere. Model selection depends on your use case, cost per token, context window, reasoning capability, and latency all vary by model. We recommend the right model for your specific task, not the most expensive one.
Yes. We add AI capabilities to existing applications via API integration. We connect your application to the AI model, build the prompt logic, handle the streaming responses, and design the user experience around the AI feature. Depending on your tech stack, this can be done in 4--10 weeks for a focused feature.
For most business use cases, you want the AI to use your data, your product knowledge, your customer history, your internal documents, rather than relying on what the model learned during training. We do this through RAG (retrieval-augmented generation), which indexes your data into a vector store and retrieves relevant content before generating a response. The AI's answers are grounded in your specific information.
Token costs vary significantly by model and use case. We design the integration with cost in mind, using appropriate models for each task, caching responses where possible, and structuring prompts to avoid unnecessary tokens. Before we build, we estimate the monthly inference cost based on your expected usage. For high-volume use cases, we evaluate open-source models (Llama, Mistral) that you can host yourself to eliminate per-token costs.
A focused AI integration adding one AI capability to an existing application typically takes 6 to 12 weeks. More complex integrations with RAG pipelines, multiple AI features, and custom data processing take 12 to 20 weeks. We agree on scope at the start. You get a working feature, not a demo, at the end.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Generative AI 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.
