How do you measure ability with AI everywhere?

Cadra is a platform that builds, runs, and scores real work done with AI. Evidence you can hire, train, or place on.

The brief

An FMCG company's sales team. Joining as an AI Engineer.

Build a production-grade multi-agent assistant that answers what, why, and what-to-do questions over the company's sales data.

90 min
AI budget: 150 requests
contract: POST /ask
every answer cited

Building it

retrieval/query.pyedited

index = load(corpus)

chunks = split(doc, 512)

chunks = semantic_split(doc)

p95 620ms to 380ms

chunking is the bottleneck, try semantic splits

What we capture

14 prompts, 3 outputs rejected and reworked
2 wrong assumptions caught before merge
Full edit history, with the reasoning behind each call

The read

Technical depth4.3

AI tool mastery3.5

Engineering rigour4.6

4.1/ 5.0Above threshold

AI-enabled people perform measurably better.

+40%better work quality when AI is used within someone's competence

2×the ROI when AI investment comes with capability building

The cost of inefficiency

Billionsin excess AI spend78%of IT leaders report unexpected AI chargesand budgets are now being capped

The difference is problem solving: understanding a scenario and guiding AI through it the right way.

Cadra measures that, on real scenarios.

Cadra platform screen showing a furnished simulation with data, deliverable, and deadline

Cadra platform screen showing a live working session being captured

Cadra capability report with composite score and dimension breakdown

Simulate. Cadra simulates a real work scenario, built around the skills being measured.furnished simulation

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Three ways to use one platform.

For Hiring

YouJD opensRole, seniority, and pass bar. Configured once.

Cadra platformAssessThe platform builds a role-specific task from your JD and scores every applicant on it. Reports in four days, every score cited.

Add-onInterviewOptional: a first round driven by the report, probing what it flagged.

YouDecideR2 and fitment are yours. Managers meet validated candidates, evidence in hand.

Roles live todayai engineerforward deployed engineerdata scientistml engineerdata engineer

“Who out of this pool can do the job, and who's close enough to get there?”

For Hiring

Cadra runs thisYour stepOptional add-on

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Three agents, on a learning engine.

Click on any card to know more.

Learning Enginepersona knowledge · feedback · improves from every run

Monitoring AgentFuture · watches live work

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From live pilots

Outcomes so far.

Spread

20–30 pts

the spread between using AI and using it well, same task, same rubric

People assessed

300+

across live pilots, from a single hire to a full college cohort

Hours given back

~150 hrs

of engineer screening time returned so far, about 19 working days

See it run on a real task.

Thirty minutes. We run a scenario and tell you whether it fits.

Join the early pilot Or get in touch