AI chatbot development — engineers, not agencies
You need a chatbot that actually works over your internal data. That means RAG architecture, proper vector indexing, and engineers who’ve done this before — not an agency quoting six months for a proof of concept.
What our engineers build
RAG-based enterprise chatbots
Retrieval-augmented generation over your internal documents: PDFs, Confluence wikis, SharePoint libraries, database records. The chatbot retrieves relevant context at query time before calling the LLM — this is what separates a useful enterprise chatbot from a hallucinating demo.
LLM integration
The engineers work with OpenAI (GPT-4o, o1), Anthropic (Claude), and open-source models (Llama 3, Mistral) depending on your data residency and cost requirements. Model selection is a decision you make together with the engineers, not something baked into a fixed proposal.
Vector databases
Pinecone, Weaviate, or pgvector — chosen based on your existing infrastructure and query volume. If you already run Postgres, pgvector is often the fastest path to production. If you need managed scale, Pinecone or Weaviate.
Chat UIs
React-based chat interfaces built to your design system, or integration into Slack, Teams, or an existing internal tool. The engineers handle the frontend if needed; the default assumption is you have a frontend team and they’re building the backend only.
Typical team
1 AI developer + 1 backend Python engineer
The AI developer owns the RAG pipeline, prompt engineering, and LLM integration. The backend engineer builds the API layer, handles auth, and connects to your data sources.
Optional: data engineer — if RAG requires ingesting data from a messy source (multiple databases, unstructured file stores, real-time feeds), a data engineer handles the ingestion pipeline separately so the AI developer can focus on the retrieval layer.
Most chatbot projects start with the two-person core team and add the data engineer if the ingestion work turns out to be substantial. You find that out in the first two weeks.
Tech stack
| Layer | Technologies |
|---|---|
| Language | Python |
| Orchestration | LangChain, LlamaIndex |
| LLM APIs | OpenAI, Anthropic, Hugging Face |
| Vector DB | Pinecone, Weaviate, pgvector |
| API layer | FastAPI |
| Frontend (if needed) | React |
How billing works
You rent the engineers. We bill by the hour at a fixed monthly rate — no agency markup, no project management overhead, no “discovery phase” billed at partner rates.
You own everything: the code, the prompts, the vector index configuration, the deployment. When the project ends, you have an engineering team you can hand off to internal staff or contractors.
The engineers are employed through staffai.eu. You don’t deal with contracts, payroll, or IR35 compliance for each individual.
GDPR and EU data residency
For EU clients building chatbots over internal documents: the engineers are based in Eastern Europe (Romania, Bulgaria, Poland, Czech Republic). Your data does not need to leave the EU perimeter during development or in production.
If your legal team has data residency requirements, the engineers can architect the system around EU-hosted LLM endpoints (Azure EU regions, AWS EU regions, or self-hosted open-source models) from the start.
What this costs
Indicative monthly team cost for a 2-person AI chatbot team (AI developer + backend engineer):
€8,000 – €12,000/month depending on seniority level.
Compare that to a US or Western Europe agency quoting the same scope: typically $30,000–$60,000 for a fixed-price project that doesn’t include the iteration you’ll need after the first demo.
T&M means you can run for two months to validate the architecture, then pause, then scale back up for production hardening. You don’t pay for time you don’t use.
Related pages
Get a team estimate
Tell us what you’re building and we’ll come back with a suggested team composition, seniority mix, and monthly cost range. No commitment, no sales call required.
Use the AI Engagement Estimator →
Pre-scoped for RAG chatbot projects. Takes about 3 minutes.