THE CAPABILITY LAYER FOR ENTERPRISE AI
Turns AI spend into AI return
Companies adopt AI tools but rarely train their people to use them well. TRACE coaches your team to use AI efficiently, and gives you the data to prove the return.
Most prompts fail one of two ways. TRACE fixes both.
Politeness and hedging don't reach the model, they only dilute your signal. Say the thing.
A prompt with no audience can't have a right answer. Name who it's for first.
TRACE applies the prompting principles these models are built for. It won't guess your audience, it makes you name it.
Built for trust, not surveillance.
Never stores prompt content
Keeps the pattern, not your words.
Individual scores stay private
Each person sees their own. No one else does, not even a manager.
Team view is aggregate only
Resolution and adoption by team. Minimum cohort of 10. Never by name.
DPA, privacy and architecture notice, infosec policy, sub-processor list and pentest available in the Trust Pack. SOC 2 on the roadmap.
You can measure what AI costs. You can't yet measure what it returns.
The models are powerful. What happens at the keyboard is uneven. Work gets redone two and three times, results vary person to person, and the spend lands on the invoice while the return never lands anywhere. That gap is the whole problem, and right now nobody in the org can see it.
Only 39% of organisations can attribute any bottom-line impact to AI.
McKinsey, State of AI, 2025The spend is real. The return is not visible.
AI seats cost more every year. The invoice is concrete. The return line is blank.
Enterprises will spend close to $270bn a year on AI software by 2027.Gartner, 2026
Most organisations cannot prove the impact.
The tools are adopted. The outputs vary wildly person to person. No one can point to the number.
Only 39% of organisations can attribute bottom-line impact to AI, and most say it is under 5%.McKinsey, State of AI, 2025
Rework eats the savings.
Work that should resolve on the first attempt goes back and forth. The time saved on the invoice is lost on the floor.
Around 40% of the time AI saves is lost to reworking weak or off-target output.Workday, 2025
Almost every engineer at Uber uses AI. Its COO still can't tie the spend to the output.
"That link is not there yet, right?"
It coaches the principles these models are built for.
TRACE sits where your team already works, across the tools they already pay for. No new platform, no migration.
One coaching layer across every tool
TRACE rides along inside Claude, ChatGPT, Copilot and Gemini as a single layer. No new platform, no migration.
Coaches in the moment
It catches the vague prompt before it is sent and hands back the one thing that is missing, like audience, length or format.
Coaches the habit, not just the task
Fix the prompt and you have fixed one task. Coach the habit and the person gets better over time.
Around 40% of the time AI saves is lost again to reworking weak or off-target output.
Workday, 2025The product, not a promise.
Three live views of TRACE: the coach inside the chat, one person's resolution score climbing, and the aggregate your leadership team sees.
Interactive: pick a weak attempt, apply the suggested move, and watch the resolution score lift.
A 90-day pilot, with the result agreed up front.
We set a success criterion together and sign it before we start. The Trust Pack lands before anything touches a real employee. Aggregates only, minimum cohort of 10. The EU AI Act and Irish employment-law read is confirmed before any pilot uses real-employee data.