AI engineering services

We design, build and deploy cutting-edge AI solutions from RAG-powered chatbots to custom AI automation tools.

AI that understands your business

ChatGPT, Gemini and other standard AI chatbots are not trained on your business data, so they often lack knowledge that is required for internal use cases.

We set up retrieval-augmented generation (RAG) over your company's own knowledge.

  • Internal chatbots and assistants that use the RAG, or integrations with the AI providers you already use.
  • Index documents, wikis, e-mail and relational databases.
  • Runs in cloud or on your own servers.

Customer support and custom AI workflows

AI can analyze and understand emails, forms, feedback, customer orders and can perform many repetitive and time-consuming tasks.

  • Auto-responders that know your inventory, prices, availability times.
  • Ticket triage, routing and reply drafting for the support team, connect other products like CRM.
  • Sentiment analysis, classification and customer metrics.
  • Scheduled and event-driven workflows across your existing tools.
  • Only escalate to human when necessary.

Chatbots with no server at all

Kaval.AI offers chatbots that run entirely in the user's browser: no infrastructure to manage and no inference bills.

  • The model runs in the visitor's browser, so there is no inference bill.
  • Supports static RAG indexes to enhance AI experience.
  • Cutting-edge features like streaming, complex data types.
  • Optionally set up backoffice for analyzing the conversations.

AI-driven business analytics

We can help setting up data warehouses, data lakes and infrastructure for large-scale distributed processing and business analytics.

  • Data pipelines, dashboards and ad-hoc analysis.
  • Stream processing, near-real-time data analysis.
  • PII data governance, anonymization and datalake integrations.
  • Business questions answered from your own data, with explanations.

Most of our products use kavalai, our open-source Python library for agentic workflows. Agents are declared as typed workflow graphs rather than hand-written glue code. The same building blocks go into client projects, so nothing we deliver depends on a framework we cannot fix ourselves.

Testable

Inputs, outputs and tool calls are typed models, so a workflow has a contract to test against. An evaluation suite replays real conversations before every deploy, and a failing case leads straight back to the run that produced it.

Robust

The model's output is validated before your code sees it. Streaming, retries, rate limiting and sessions are handled by the library, and any provider works behind one interface, so switching models is a configuration change.

Comes with a backoffice

Every session, run, node and model call is recorded and browsable. Conversations lead to runs, runs to tasks, and each task opens into its input and output, so a bad answer can be traced to the node that caused it.

Timo Petmanson, founder of Kaval.AI

Timo Petmanson, founder

Timo Petmanson is a data scientist and engineer with over 15 years of experience in data analytics, natural language processing and data engineering.

He studied computer science at the University of Tartu, was a junior researcher working with Estonian e-health data and a major contributor to the Estonian natural language processing toolkit ESTNLTK.

He later worked as software engineer at Google in Zürich in ads team, was a data scientist at Venturebeam in Tallinn and developed datalakes at Veriff.

In 2026 he founded Kaval.AI to build AI workflows, chatbots and tools for companies.

  • ex-Google
  • ex-Veriff
  • ex-Funderbeam
  • University of Tartu

Tell us what you are building, or what is taking up your team's time, and we will get back to you.

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