Chatbot development grounded in your own data
We build chatbots and assistants that answer from your own content instead of making things up. Using RAG, we connect the language model to your documentation, product catalog, or internal knowledge base. With guardrails, systematic evaluation, and a clean handoff to a human when the bot reaches its limits.
What we build
Customer support assistants, internal knowledge bots, and document-grounded chatbots that read from your PostgreSQL database, your PDFs, or your Confluence. Every answer is tied to a retrievable source through RAG and shown with a citation, so users can trace where it came from. Not a demo toy, but an assistant that holds up in daily use and knows when it should not answer.
How we work
We start with a tightly scoped use case and a test set of real questions we grade every answer against. Weekly demos show you the progress live, with no surprises at the end. Guardrails against prompt injection and false answers, an automated evaluation pipeline, and a clean escalation path to your team are part of the build from day one.
The stack
We build with Python and Langchain for orchestration, OpenAI and Anthropic as the language models, and PostgreSQL with pgvector for retrieval. The frontend is Next.js and embeds into your product as a widget or through an API. Switching models stays possible at any point, because we never lock you into a single provider.
Why Olio
Senior engineering without the full-time hire: you work directly with the people writing the code. We host on EU-sovereign, GDPR-compliant infrastructure — Hetzner, GCP EU regions, or AWS Frankfurt — and account for EU AI Act requirements from the outset. We hand over documented, tested code your team can carry forward.
What you get
- Answers drawn from your own data via RAG, each with a traceable source instead of invented facts
- Guardrails against prompt injection, off-topic drift, and confidently wrong responses
- Automated evaluation against a test set, so quality is measured rather than guessed
- Clean handoff to a human when the bot is unsure or a case needs escalation
- EU-sovereign, GDPR-compliant hosting and an EU AI Act-aware implementation
- No vendor lock-in: the model and cloud stay swappable, and the code is yours
Core Technologies
Let's scope your chatbot
Common questions
What does chatbot development cost?
A focused production chatbot with RAG starts in the low five figures. Larger assistants with multiple data sources, guardrails, and evaluation run higher. After a short scoping call, we give you a concrete fixed-price range for your specific use case.
How long does it take to build?
A first production chatbot is typically live in 4 to 8 weeks. A narrow, well-defined use case ships faster; multiple data sources and complex escalation logic take proportionally longer. You see weekly demos from week one.
How do you stop the bot from hallucinating?
Through RAG, the bot ties every answer to retrievable sources from your data and cites them. Guardrails keep it to allowed topics, and when no source fits it says so honestly instead of guessing. An evaluation pipeline measures answer quality against a real test set.
What technologies do you use?
Python and Langchain for orchestration, OpenAI and Anthropic as language models, PostgreSQL with pgvector for retrieval, and Next.js on the frontend. For sensitive data, we also integrate open-source models that run entirely inside your own environment.
Can the data stay in the EU?
Yes. We host on EU-sovereign, GDPR-compliant infrastructure — Hetzner, GCP EU regions, or AWS Frankfurt. For maximum data control, we run the language model self-hosted, so your content never leaves your infrastructure.
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