AI Integration

AI Integration Services for Existing Software

We integrate large language models into the applications and workflows you already run — with the same engineering standards as the rest of your system. Software engineers applying proven AI APIs, not an AI research lab.

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  • OpenAI API
  • Anthropic Claude
  • Azure OpenAI
  • RAG
  • Embeddings
  • Vector databases
  • AI agents
  • n8n
Abstract 3D network of connected points shaped like a brain

AI integration services

Practical AI features that solve a specific problem in your product or operations, integrated through well-tested APIs.

  • LLM integration

    Add summarization, drafting, classification and extraction to your application using LLM APIs.

  • OpenAI & Azure OpenAI integration

    Integrate OpenAI models directly or through Azure OpenAI for Azure-hosted, enterprise-governed deployments.

  • Anthropic Claude integration

    Build on Claude models via the Anthropic API for long-document reasoning, drafting and tool use.

  • RAG development

    Question answering and search over your documents using embeddings, vector databases and citation of sources.

  • AI chatbots & assistants

    Customer-facing or internal assistants that answer from your knowledge base and hand off to humans.

  • AI agents

    Agents that call your own APIs as tools — with permissions, confirmations and audit logs around every action.

  • AI document processing

    Extract structured data from invoices, forms, contracts and emails into your systems, with human review where needed.

  • AI automation & workflows

    Automate business processes with LLM steps, using code or tools such as n8n connected to your APIs.

  • AI-powered search & analytics

    Semantic search across your content and natural-language questions over reports and operational data.

How a RAG integration fits into your system

A typical retrieval-augmented generation architecture. The model never gets direct access to your database — your backend decides what it sees.

RAG architecture: documents are chunked and embedded into a vector database; at query time the backend retrieves relevant chunks, builds a prompt and calls an LLM API, then returns an answer with sources to the user INGESTION Your contentPDFs · docs · records Chunk & cleansplit · metadata Embeddingsembedding model API Vector databasepgvector · managed index QUERY Userweb · mobile · chat Your backend (ASP.NET Core / Node.js)auth · permission filter · retrieve · build promptguardrails · logging · cost tracking LLM APIOpenAI · Claude · Azure relevant chunks Answer with cited sources → user
Illustrative architecture. Components are chosen per project — for example, pgvector on PostgreSQL or a managed vector index.

How we approach AI integration

  1. Use case

    Pick one valuable, measurable task. Collect real examples.

  2. Prototype

    Test models, prompts and retrieval against those examples.

  3. Integrate

    Build it into your backend with security, logging and fallbacks.

  4. Monitor

    Track quality, latency and cost; improve with real usage.

Model APIs

  • OpenAI API
  • Anthropic API (Claude)
  • Azure OpenAI
  • Other LLM APIs

Retrieval

  • Embeddings
  • Vector databases
  • pgvector
  • Hybrid search

Orchestration

  • Tool / function calling
  • AI agents
  • n8n
  • API automation

Integration layer

  • ASP.NET Core
  • Node.js
  • TypeScript
  • Flutter clients

AI features built like production software

  • Security

    API keys server-side, per-user permission filtering on retrieval, and no direct model access to your database.

  • Data handling

    Only necessary data is sent to providers; retention and residency requirements agreed up front.

  • Evaluation

    Test sets from real examples so prompt or model changes can be checked before release.

  • Cost control

    Token usage logging, caching and model selection matched to the task.

From the blog

Guides on AI Integration

Frequently asked questions

Do you build your own AI models?

No. We integrate established model APIs — such as OpenAI, Anthropic Claude and Azure OpenAI — into your software. We do not train proprietary foundation models. For most business use cases, well-engineered integration of existing models is faster, cheaper and easier to maintain.

What is RAG and do we need it?

Retrieval-augmented generation (RAG) retrieves relevant passages from your own documents or data and gives them to the model with the question, so answers are grounded in your content. You need it when the model must answer from information it was not trained on, such as your policies, manuals or records.

Can you add AI to our existing .NET or Flutter application?

Yes. We commonly add AI features through a backend service in ASP.NET Core or Node.js, which the web or Flutter app calls. API keys and data access stay on the server.

How do you handle data privacy?

We design integrations so only the data needed for a task is sent to a model provider, use enterprise API terms or Azure OpenAI where required, keep credentials server-side, and log usage. Data-handling requirements are agreed with you before build.

Which model provider should we use?

It depends on your quality, cost, latency and data-residency requirements. We often build a thin abstraction so you can compare or switch providers without rewriting your application.

How do you know the AI feature is working well?

We build a small evaluation set from real examples at the start, test prompts and retrieval against it, and monitor quality and cost once the feature is live.

Have an AI use case in mind?

Bring the problem, the data involved and the system it needs to fit into. We will give you an honest view of what is feasible and how to start small.

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