The wrong problems
Teams automate what's visible, not what's broken. Without a real diagnosis, AI lands on processes that were never the bottleneck.
Intelligence infrastructure & deployment
From AI spend
From AI spend
to AI value
We're the AI surgeons. We diagnose what's broken, map what your workforce can really do, and deploy the bespoke AI agents that finally capture the value. Every score backed by evidence.
Cited evidence
Deterministic scores
Human or agent
95%
of organizations aren't realizing value from their AI investments. The spend is real. The return rarely reaches the P&L.
95% value stranded5% value realized
The problem
Spending on AI is easy. Realizing value is the hard part.
Many organizations are investing heavily. Nearly all stall on the same things: the wrong problems, a workforce that isn't ready, and deployment they can't trust.
Deployment you can't trust or afford
Agents are unpredictable, ungoverned, and the token bill runs away. Pilots never become production.
200%
of annual salary: the cost of a single wrong hire.
6 in 10
workers need reskilling, but the workforce isn't ready.
1 in 20
capture the value they expected from AI.
Our approach · AI surgeons
Diagnose. Prepare. Deploy.
A real diagnosis in days, a clear map of what your workforce can do, and bespoke agents that capture measurable value. Every step is run by our forward-deployed engineers.
AI interviews that find the real problem
Agents you actually talk to. They probe, collect the real challenges, and connect symptoms to root cause. Across the whole workforce at once, not a small sample.
6-8 wks → 1discovery, compressed
Whole orgnot a sample
Voice agent
many conversations at once
An objective map of what your people can really do
Voice, simulations, and CV screening synthesize into one capability map, scored on evidence, not resumes. Strengths and critical gaps, by function. Human or agent.
Objectivecited & reproducible
STORMthe evaluation engine
Synthesis
evidence → capability map
Voice interviews
Work simulations
CV screening
17 critical gaps surfaced
Bespoke agents around your biggest bottlenecks
The findings become a governed plan, shaped to your processes and data. Human in the loop on the unpredictable moments, token-tracked, and deployed only when it clears a proven bar.
Governedhuman in the loop
On the P&Lspend maps to value
Bespoke agent
turning findings into a plan
- Stakeholder mappingownership & approvers traced
- Risk assessmentadoption risks surfaced early
- Implementation plandrafting a phased rollout
- Cost governancePending
- Human-in-the-loop checkpointsPending
We're early stage and partnering with organizations to bring this to life. We'd love to build it with you.
The evaluation engine · STORM
We grade how the work gets done. Human or agent.
The same engine that maps your workforce grades an AI agent: the same rubric, the same citations, the same human baseline. No black box. Every score points to a real quote from the actual work.
Cited
Every "yes" or "no" must point to an exact quote from the operator's chat, code, spreadsheet, or call. No quote, no score.
no quote → "insufficient evidence"Deterministic
Same evidence, same prompt, same model. Same result. Reproducible quarters from now, with the prompt version and model snapshot stored alongside the score.
temperature 0 · seed 42 · model pinnedHuman-decides
The AI scores. You decide. See every observation, every quote, every "insufficient" tag, and override any score with a comment that joins the audit trail.
AI assists · you decide01
Turn the role into testable questions
Each criterion becomes a small set of yes/no observation questions: the operational definition of "good", versioned and bound to the role.
02
Capture what they actually did
Voice, code, spreadsheets, slides, and chat are bundled into a structured evidence package, PII-redacted before any model sees it.
03
Score every question against the evidence
One question, one citation, one answer. If there's no quote to justify it, the only legal answer is "insufficient evidence".
04
Verify every citation. Punish hallucinations.
A deterministic validator (pure code, no AI) checks each quote actually exists. Fabricated quotes are auto-downgraded and logged.
05
Aggregate honestly. Never fake confidence.
Assessed, partially assessed, or not assessed. We say plainly what the work surfaced instead of averaging our way to a clean number.
06
Make every score reproducible, forever
Prompt version, model snapshot, seed and citations are stored with the result, so any score can be re-run and audited later.
Citation validator · livefunnel metric definition
Q1 · Distinguished started vs. submitted apps?
cite: chat[turn 6] · verified
Q2 · Verified the metric against the source?
cite: code[diff line 42] · verified
Q3 · Connected the mismatch to the post-launch drop?
cite: voice[03:14] · verified
Q4 · Named a concrete remediation?
cite: not found in evidence bundle
Q4 quote failed validation → coerced to insufficient · kind=citation_violation
What you walk away knowing
An objective map of what your people can really do.
Resumes and self-assessments don't show capability. We build it from simulations and CV screening, scored the same way for every person, and increasingly every agent. Strengths, critical gaps, and where to act first.
Capability map Live
Engineering
Product
Service
Operations
159
Assessed
76%
Avg score
17
Critical gaps
Where we'd start
What a pilot looks like.
One focused engagement on a single function or process, over about 30 days, judged on a value metric you choose up front.
Find the real problems
AI interviews plus our engineers map the true challenges and how the work actually happens.
Map the workforce
We build the capability map for that group and pinpoint its real strengths and gaps.
Shape the agent
We design a bespoke agent for the highest-value challenge, governed and cost-aware.
You walk
away with
away with
The real problems mapped, a capability map of the team, and a costed plan for a bespoke agent with a clear value case, plus a success metric agreed at the start.
Who's behind it
Operators, engineers, and AI-lab builders.
A team that has run businesses at scale, built ML infrastructure, and shipped from inside national AI labs.
Built by engineers & operators from
GoogleMicrosoftEYGEICOBerkshire Hathaway
Advised by leaders from
DatabricksSarvam
From AI spend to AI value
Let's find the value that's stranded.
A focused pilot on one area, judged on a metric you choose, plus a working session on our methodology, governance, and data approach. We're early stage and looking for the right partners to build and succeed with.
Or reach us directly: contact@yolexlabs.com