Generative AI Integration Services

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.

See our work

  • 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

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

12 weeks to production

AI OCR pipeline · fintech platform

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

20,000+ daily transactions processed

Generative AI writing · B2B SaaS

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

3x faster proposal output
4.9 / 5 on ClutchSee all work

Recognition

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?

In short

RaftLabs integrates generative AI into existing apps for clients in the US, UK, and Australia. 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.

Trusted by

Vodafone
Nike
Microsoft
Cisco
T-Mobile
Aldi
Heineken
GE

AI development, by the numbers

AI products shipped in 24 months
20+
from kick-off to production-ready AI product
12 weeks
rated by clients on Clutch
4.9/5
years shipping software and AI products
9+

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.

Why us

Why teams choose RaftLabs

  1. Senior engineers build what they scope

    The engineers who assess your problem also build the solution. No bait-and-switch, no offshore handoff after the contract is signed. The team you meet in week 1 ships in week 12.

  2. Fixed price before development starts

    We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.

  3. 9 years and 100+ products shipped

    Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record across AI, SaaS, mobile, automation, and enterprise platforms across healthcare, fintech, logistics, and hospitality.

  4. Compliance built in from the start

    GDPR, HIPAA, SOC 2 — compliance requirements are scoped in week 1, not retrofitted before launch. We have shipped HIPAA-compliant systems for US healthcare clients and GDPR-compliant products for European markets.

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.

Talk to our AI team

Process

How we work

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

02

Model & 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

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

04

Feature build & 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

05

Testing & 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

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.

Talk to our AI team

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

Related services

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 guideFree AI cost estimatorBrowse our AI case studies

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