Services · AI Integration
AI integration that
actually ships.
Wire OpenAI, Anthropic Claude, or an open-source model into your existing product. Streaming chat, tool-calling, structured output, RAG, eval, observability. No platform rewrites.
What I integrate
OpenAI
GPT-4o, GPT-4o-mini, the o-series for reasoning, plus Assistants, function-calling, and structured outputs.
Anthropic Claude
Claude Sonnet and Opus, the messages API, prompt caching, tool-use, computer-use where it fits.
Open-source LLMs
Llama, Mistral, Qwen via Ollama, vLLM, Together, or self-hosted GPU - when data residency or unit economics demand it.
Provider gateways
Vercel AI Gateway, OpenRouter, custom routers for model failover and cost shifting.
What you get
Streaming UI
Token streaming, partial state, cancellation, retry. Vercel AI SDK or a custom transport against your stack.
Tool-calling agents
Models that call your APIs to do real work, with human-in-the-loop checkpoints where it matters.
RAG over your data
Production retrieval with hybrid search, reranking, and citation grounding. See the RAG tutorial.
Eval harness
A labelled set, golden answers, regression tests. Prompts stop being a guessing game.
Observability and cost
Per-request logs, latency p50/p95, token cost, model attribution. Dashboard you can actually read.
Guardrails
Prompt injection defenses, output validation, fallback paths, refusal policies. Not optional in 2026.
Pricing
| Scope | Timeline | Price |
|---|---|---|
| Single AI feature integration (chat, summarization, classification) | 1-3 weeks | $3.5K-$15K |
| RAG over your docs with eval | 3-6 weeks | $15K-$35K |
| Agentic feature with tool-calling + HITL + observability | 4-8 weeks | $20K-$45K |
| Hourly retainer post-launch | Ongoing | On request |
What this looks like in practice
Worked examples of builds like this: the problem, the workflow, what the AI does and where people stay in the loop.
A company assistant that answers from SharePoint and Drive, and respects who may see what
Answers staff questions in Teams and Slack from SharePoint, Drive and Confluence, cites every source, and only searches what the person asking may open.
SharePoint and Teams / Google Drive / Slack / Confluence or Notion / Microsoft Entra ID or Google groups
A remote MCP server so customers can use your SaaS from Claude, ChatGPT and Copilot
Your product inside Claude, ChatGPT and Copilot Studio: OAuth sign-in, a small set of agent-shaped tools, per-tenant audit logs and rate limits that protect your API.
Your REST API / OAuth provider (Auth0, WorkOS or your own) / MCP TypeScript SDK / Claude / ChatGPT
An AI bill growing faster than your users: a cost audit, model routing and budgets
Traces model spend to each feature and customer, fixes caching, batching and prompt bloat, routes simple calls to small models, and proves quality holds with evals.
OpenAI and Anthropic APIs / Langfuse or Helicone / AI gateway / Data warehouse / Slack
An in-app AI assistant that acts on the user's own data, not one that only quotes the docs
Tool calling over your own API with the signed-in user's permissions, a confirm step before every write, undo, per-plan usage caps and a trace of every step.
Your product's API / Vercel AI SDK / OpenAI or Anthropic / Postgres / Stripe
Catching edited bank statements, payslips and invoices before they cost you money
Cross-checks uploaded statements, payslips and invoices against bank data, VAT records and the file's own structure, then puts the evidence in front of a reviewer.
Upload portal / Open banking provider / EU VIES / KYC provider (Onfido, IDnow or similar) / CRM or loan system
Connect your ERP and CRM to Claude, ChatGPT or Copilot without handing over the keys
Gives the assistants your staff already use a few scoped tools into the ERP, CRM and databases: read-only by default, writes behind approval, every call logged.
SAP Business One / HubSpot / Postgres or SQL Server database / Claude, ChatGPT and Copilot Studio / Microsoft Entra ID or another OAuth provider
Frequently asked questions
What does AI integration actually cover?
Wiring an LLM into your existing product so it does useful work: streaming chat, structured output, tool-calling, function execution, retrieval-augmented answers, summarization, classification, agent loops with human-in-the-loop checkpoints. Plus the unglamorous parts - eval, observability, cost monitoring, rate-limit handling, fallbacks.
Which models do you work with?
OpenAI (GPT-4o, GPT-4o-mini, o-series), Anthropic Claude (Sonnet, Opus), Google Gemini, and open-source via Ollama/vLLM/Together for self-hosted needs. The Vercel AI Gateway makes provider swaps trivial in most stacks.
Do you replace my existing backend?
No - integration means meeting your stack where it is. Most clients keep their existing API and database. I add an AI layer behind a clean interface so you can iterate the model without touching the rest of the app.
How long does an AI integration take?
Simple integrations (a streaming chat widget over your docs) ship in 1-2 weeks. A full agentic feature with tool-calling, eval, and observability is typically 3-6 weeks.
What about cost control and evals?
Both included by default. I set up per-request cost logging, latency tracking, and an eval harness so model changes can be tested before deploy. Skipping this is how AI features quietly burn budget.
Where are you based?
CET timezone. Working with clients across Europe and North America. See the page for hiring an AI developer in Kosovo for context.