Ground-truth agents

Answers about your company that you can check yourself.

Org charts, surveys and CVs ask you to take somebody's word for it. We build agents that find out what is actually true, and show you where every answer came from.

What people really say What they can really do What actually gets done

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The problem

You are deploying AI onto an organization you cannot see.

95% of organizations are not realizing value from their AI investments.

The spending is not the problem. The blindness is.

Nobody can tell you where the work actually breaks, because the people who know will not say it in a survey with their name on it. Nobody can tell you what the team can really do, because CVs and performance reviews are self report all the way down. And nobody can tell you whether the agent you are about to deploy is ready, because there is no bar to hold it to.

So the agents get pointed at whatever is most visible. Which is almost never whatever is most expensive.

  • You automate what is visible.

    The loudest process gets the agent. The bottleneck keeps running.

  • You staff from claims.

    CVs, interviews and org charts describe people. None of them measure anyone.

  • You deploy without a bar.

    A person who is not ready underperforms. An agent that is not ready acts.

The products

Three agents. One thesis.

No claim without evidence. Each one points at a different part of the work.

How they work together

Say, do, done.

The products are one loop. Each step feeds the next, and every step produces evidence you can hand to someone who disagrees with you.

  1. 01

    Listen · CandoorFind where the work actually breaks.

    A voice interview across the whole group, not a sample. Cheap, broad, and safe enough that people say the real thing. What used to be a six to eight week discovery becomes one round.

  2. 02

    Measure · StormFind out what your people can actually do.

    Listening tells you where to look. Simulation tells you what is really there. Objective, cited, reproducible, and pointed only at the roles that matter.

  3. 03

    Deploy · AgentsClose the gap, and prove it closed.

    A bespoke agent scoped around the biggest bottleneck, governed, human in the loop, measured against the metric you picked on day one. It passes the same readiness bar we hold a person to before it goes near production.

Most tools stop at step one. Asking people what is wrong is the cheap part.

The pilot

What a pilot looks like.

About 30 days. One area of the business. One metric, chosen by you before we start.

  1. Week 1 · Listen

    Find where the work actually breaks.

    Candoor across the group, and what it costs.

  2. Weeks 2 to 3 · Measure

    Map what the team can really do.

    Storm on the roles that matter. Not a sample, and not a survey.

  3. Weeks 3 to 4 · Design

    Scope the agent that closes the top gap.

    Costed, governed, and pointed at the metric you chose.

You walk away with

The real problems ranked by cost, a capability map of the team, and a costed plan for a bespoke agent with a clear value case, judged against the success metric you picked on day one.

The team

Who is behind it.

A team that has run businesses at scale, built ML infrastructure, and shipped from inside national AI labs.

  • Google
  • Microsoft
  • EY
  • GEICO
  • Berkshire Hathaway

Advisors from Databricks and Sarvam.

Stop guessing how your company works.

A focused pilot on one area, judged on a metric you choose, plus a working session on our methodology, governance and data approach. We are early and looking for the right partners to build with.

Request a pilot Book a working session

Or reach us directly: contact@yolexlabs.com