BONVO

Industries / AI & Agentic Platforms

Agents in production. Not another demo.

The gap between an impressive LLM prototype and a system your enterprise can actually run — governance, auditability, evaluation, cost — is where most AI initiatives stall. Bonvo has been shipping language-model systems into production since before ChatGPT existed.

The problem

Where AI platforms get stuck

A prototype demos beautifully but can't pass security, compliance, or a real cost review.

Agent workflows are bolted onto a platform built for humans — brittle, unobservable, impossible to audit.

Adoption stalls: the tooling works, but nobody owns it and it depends on whoever built it.

No evaluation harness, so nobody can tell whether a model or prompt change made things better or worse.

What Bonvo brings

What Bonvo brings

01

Agent & LLM architecture that ships

MCP servers, tool-use pipelines, and agent workflows designed as first-class, reviewable artifacts — with the governance and auditability that regulated environments require.

02

AI-assisted engineering in your org

The same practice run on a production team: a Claude Code slash-command suite in daily use, an MCP-driven evidence pipeline, and review automation derived from a 1,000-merge-request corpus.

03

Evaluation, cost & vendor pragmatism

Model and vendor selection judged on production outcomes, with evaluation harnesses and cost controls — not benchmark theatre.

Track record

Grounded in real AI work

Bonvo runs a live applied-AI practice on a contract-intelligence platform serving government and defence clients: agent workflows and MCP pipelines in production, a review-corpus analysis of 1,000 merge requests that produced tooling automating roughly a sixth of recurring review feedback — plus an OpenAI-powered content system shipped back in 2021–22, before ChatGPT. This is enterprise AI in a regulated, high-stakes environment, not a weekend project.

0merge requests analysed to automate review
~0/6of recurring review feedback automated
0shipping production LLM systems since — pre-ChatGPT

What colleagues say

I had the pleasure of working alongside Gergely, and it was one of the most enjoyable collaborations of my career. He brings a rare combination of energy, integrity, and curiosity to everything he does. Gergely is genuinely up to date with the AI landscape and the latest tools and methodologies, and what stood out most was his willingness to share that knowledge with the whole team — constantly experimenting with new tools to find better, faster ways of working. Beyond the technical side, Gergely is the kind of person who holds a team together: helpful, approachable, and always ready to support others. Working with him was a real privilege, and I sincerely hope our paths cross again. Any team would be lucky to have him.

Gergő VeresTest Automation Engineer·Affinitext

The practice scales

One architect accountable. A team when the job needs it.

Bonvo is led hands-on by one senior architect. But you are never limited to one pair of hands: the practice scales a vetted senior team up to the size a job requires — and back down when it's done — without ever changing who owns the outcome.

01

One accountable owner

Every engagement is owned end to end by Gergely Kovács — the architect who does the work, commands the incident, and signs off on the result. That never changes.

02

A senior bench, on demand

When a build needs more hands, Bonvo assembles a vetted team of senior engineers scaled to the job — from a solo rescue to a full squad — and stands it down when the work is done.

03

No bench-warming, no dilution

You pay for the size the problem needs, not a fixed team. The standard stays senior; the outcome stays owned by one person.

AI & Agentic Platforms

Bring the problem as it actually is.

A first conversation is a mapping session, not a sales call. Write in English, Hungarian, Ukrainian, or Russian.