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IntertechSolutions

We build AI agents you can check

Two weeks, €3,000 fixed, one task — an agent doing that task on your own data, with a number on how often it gets it right and a list of what it gets wrong. If it does not pay for itself, we say so on day three and there is nothing to pay.

Discuss your caseSee the evidence →

Agents we have shipped

An agent is given a job, fetches what it needs, works through the steps, and returns a draft, a filled-in record or a finished report for a person to approve or reject. We score agents on real test sets before they ship, test inputs that were not selected for a demo, keep an audit trail of what they did, and pressure-test them against prompt-injection attacks.

Multi-agent

A six-agent pipeline

OpenClaw runs insurance claims through six agents with a Run → Eval → Improve loop, and an LLM judge scores every pass.

View the code ↗
Auditable

An on-chain agent you can check

Trustodian enforces a spending mandate and leaves a trail you can audit after the fact. Second place at The Money Agent Hackathon.

Adversarial-tested

Built to resist attacks

Our BitGN agent has to finish ordinary tasks while refusing the prompt-injection traps hidden inside them, and it's scored by what it actually changed.

View the code ↗
In production

Running for real users

A nutrition agent lives in a live chat with memory and context, shipped only after we widened its eval set. A team of agents reads location data to write sourced expansion briefs for a retail-analytics product. And an offer agent builds a uniform manufacturer's client offers from their own catalogue, showing the plan before it spends anything.

What goes into an agent that holds up

We offer three services around an agent: build one agent for one task and prove it pays, give it access to what the company actually knows, and measure what it gets wrong before release. They can be used together or bought separately, depending on what you already have.

  • 01

    Agent Build

    Two weeks, one task, one working agent on your own data. The first days settle which task pays for itself and end on a go/no-go — if the answer is no, there is nothing to pay. The rest builds the winner against a test set of your real cases. Code, test set and numbers are yours.

    Proven by

    How the build works →

  • 02

    Knowledge Base (RAG)

    Your own documents start answering questions, and every answer links back to where it came from. We agree on a test set up front and show you the quality numbers before handover.

    Proven by

    How we build it →

  • 03

    Evals / QA

    We build test sets from your real cases and score your AI the same way on every release, so a drop in quality shows up before your users run into it.

    Proven by

    How evals work →

Built for one industry

Offer sheets for uniform makers

A one-line brief in chat comes back as a branded offer sheet. It uses the manufacturer's own garments and the client's brand colours, in their sheet format, in Serbian or English.

It has been running on their real client work since July 2026.

See what it does →
An offer sheet for kitchen staff: a pair in the uniform, colour palette, detail close-ups and materials

Built by researchers who ship

PhD-trained researchers and engineers who compete in the open and publish what they build. Each line below is a result someone other than us can look at.

Meet the team

When an agent fits the task

The task needs to happen often enough, and take enough staff time, for the build to pay for itself. It also needs a named owner inside your company after handover. If what you need is a chat widget on your site, ready-made products are available at software prices. The first days of a build are a go/no-go: if none of your candidate tasks pays for itself, we say so on day three and there is nothing to pay.

How the build worksGet in touch →