Generative AI Integration Services

Generative AI integration that adds AI to the product you already run.

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.

  • GPT-4o, Claude 3.5, Gemini, and Llama 3 integrations

  • Embeddings, RAG, function calling, and structured output

  • Existing application or workflow, no full rebuild required

  • 20+ AI products shipped with generative model integrations

Recent outcomes

AI chatbot integration · SaaS startup

12 weeks to production

Built a conversational AI layer on top of an existing SaaS platform, routing 70% of routine queries without human intervention.

AI OCR pipeline · fintech platform

20,000+ daily transactions processed

Integrated an AI document extraction layer into an existing invoice processing app, eliminating manual data entry errors.

Generative AI writing · B2B SaaS

3x faster proposal output

Added AI email drafting and proposal generation to an existing CRM, cutting sales admin time by more than half.

4.9
on Clutch
See our work

The problem

Sound familiar?

  • 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?

Short answer

RaftLabs integrates generative AI into existing apps for clients across the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia. We connect GPT-4o, Claude, Gemini, and Llama to your product without a rebuild. A focused integration ships in 6 to 12 weeks at a fixed price.

Key takeaways

  • RaftLabs adds generative AI to existing apps without a full rebuild, serving clients in the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia.
  • Supported models include GPT-4o, Claude 3.5, Gemini, and Llama 3, selected based on your use case and cost requirements.
  • A focused AI integration ships in 6 to 12 weeks at a fixed price; complex RAG or multi-feature builds take 12 to 20 weeks.
  • We use RAG (retrieval-augmented generation) to ground AI responses in your own data rather than general training knowledge.
  • RaftLabs has shipped 20+ AI products, with one integration routing 70% of routine queries without human intervention.
  • Inference cost is estimated before build and optimised through model selection, response caching, and prompt efficiency.

Trusted by

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

Your competitors shipped AI features last year. You're still planning.

Your product works. Users rely on it every day. The question was never whether to rebuild it, it's which AI capability to add, and how to add it without breaking what already works.

A CRM that drafts the follow-up email. An invoice app that reads the invoice. A support tool that answers in plain English from your own data. None of that needs a new platform. It needs an integration layer on top of the product you already run.

That layer is the work. The feature your users see is the easy part.

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.

According to Gartner, more than 80% of enterprises will have used generative AI APIs or deployed generative AI-enabled applications by 2026. For most of those businesses, the path is integration, not a rebuild from scratch.

RaftLabs has shipped 20+ AI products, including an integration that routes 70% of routine queries without human intervention. Across 9 years and 100+ products for clients including Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin, the team that scopes the work is the team that ships it, with GDPR, HIPAA, and SOC 2 requirements designed in from week one, and a fixed price locked before development starts.

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.

Integration pays off when you already have a product AI can plug into.

Everything on the left should already be true for your business. Even one thing on the right, and a full generative AI build or a no-code tool is the smarter first step.

A fit
01

A live product your users already rely on, a CRM, ERP, mobile app, or internal tool, that you want to extend, not replace.

02

A specific AI capability in mind: drafting, summarisation, search, classification, or structured data extraction.

03

Proprietary data you want the AI grounded in, and a decision-maker who can define what a successful feature looks like.

Not a fit
  • No product yet. You need an AI-native product built from scratch, which is generative AI development, not integration.
  • A standard need a configured off-the-shelf AI tool already covers well.
  • Shopping for the cheapest per-hour team rather than a fixed-scope partner.

What we build

Generative AI capabilities we integrate

  • 01
    AI writing and content generation
    Add AI drafting to any text input in your application: CRM email drafting, report generation, proposal writing, and product description creation. The AI generates a first draft that users edit and send, so productivity goes up without replacing the human judgment that matters.
  • 02
    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 and reports, and classifications applied automatically. What took an analyst an hour takes a second.
  • 03
    Conversational search and Q&A
    Replace keyword search with natural language search. Users ask questions in plain English and get relevant, cited, accurate answers from your data instead of a list of links to sort through.
  • 04
    AI classification and routing
    Classify incoming data, support tickets, emails, form submissions, transactions, automatically, and route each to the right team, label, or workflow. No human in the loop for routine classification; humans review the edge cases.
  • 05
    Code generation and developer tools
    Add AI assistance to developer-facing products: code completion, natural language to SQL, test generation, and documentation drafting. If your product is used by developers, AI assistance is now a table-stakes feature.
  • 06
    Structured data extraction
    Use AI to extract structured data from unstructured inputs, invoices, forms, emails, PDFs, through function calling and structured output modes. The output is clean, structured JSON that feeds directly into your database or workflow.

The models we work with

  • 01

    OpenAI (GPT-4o, GPT-4 Turbo)

    The most capable reasoning models for complex tasks, with GPT-4o adding multi-modal use cases (text and images). Strong for complex reasoning, code generation, long-context tasks, and function calling.

  • 02

    Anthropic Claude (Claude 3.5 Sonnet)

    Particularly strong for long-document analysis, nuanced writing, and instruction-following. The 200K context window makes it practical for processing large documents without chunking, a good default for document intelligence and writing use cases.

  • 03

    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, self-hosted deployment, and fine-tuning.

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.

How it works

How we work

  1. Step 01
    01

    Use case definition

    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

  2. Step 02
    02

    Model and data design

    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

  3. Step 03
    03

    Integration development

    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

  4. Step 04
    04

    Feature build and UX

    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

  5. Step 05
    05

    Testing and monitoring

    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

What clients say

What our clients say

Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.

Amer Abu Khajil
Amer Abu Khajil
Canada flagCanada
Founder, Peak Studios & Perceptional

I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.

01 / 03

See all projects

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.

Related services

Generative AI Integration Services, scoped in one call.

Tell us what's broken. Within one business day you get a straight take on cost, timeline, and the right first step. No deck, no pressure.

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Frequently asked questions

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.
  • All conversations are NDA-protected.

Go deeper

How to integrate an LLM into existing softwareGenerative AI development cost guideSoftware cost calculatorBrowse our AI case studies

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