01
Production changed the answer.
AI quality is no longer a lab metric; it has to show up in live workflows.
About
Specialist AI builders with the proof discipline to defend the work.
Management-consulting rigor meets big-tech engineering: AI Strategy, AI Transformation, and AI Fluency with Governance, Audit, and Evals built into the work. We have run AI under audit standards inside Goldman Sachs and JPMorgan and shipped production AI at enterprise scale at Thena.
02
Dashboards missed the outcome.
Teams need evidence tied to decisions, not more fragmented reporting.
03
AI needed one operating view.
Value, risk, owners, controls, and fluency belong in one read.
Three beliefs
Full-spectrum builder. One proof discipline.
The company worldview now starts with Strategy, Transformation, and Fluency; Evals and Governance are the proof layer built into the work, not the whole offering.
Full spectrum, not partial views
Strategy, transformation, fluency, evaluation, and compliance are one operating problem in several vocabularies. Evals and Governance make the proof portable across the whole build.
Continuous, not periodic
Point-in-time attestation was designed for deterministic systems. Production AI is not deterministic. Continuous evidence survives the question: what was the system doing at 3:47pm on Tuesday?
Baselines, not checklists
Frameworks define what to track. Baselines define what good enough means for a specific use case. Evals makes that baseline explicit before the output becomes the record.
Team
Engagement-led, not headcount-led.
An AI-native team with niche skills across enterprise systems, regulated workflows, and AI-native product companies.
Audit-standard operators
Goldman Sachs and JPMorgan experience building AI and data systems under regulated-enterprise review.
Foundational AI experience
Production ML, evaluation, agent workflows, governance evidence, and review loops that run after launch.
Small team
Engagement-led rather than headcount-led. Practitioners stay close to the operating read, the platform, and the handoff.
How we ship
Bounded engagement. Durable operating loop.
The platform stays, the practitioner transfers the method, and the customer owns the loop after handoff.
A neutral read of the number, the mechanism, and the proof gap, then route into Strategy, Transformation, Fluency, or Quick Audit.
Strategy, Transformation, Fluency, Evals, Governance, or Engineering sized to the gap.
A named practitioner works inside the engagement window while the platform records the method.
Platform, playbook, evidence pack, and operating cadence transfer to the customer team.
Why services
Software first. Services only where the operating problem needs a landing team.
The missing FAQ comes back as a plain statement of the model: product at the center, bounded services around real deployment work.
Why services?
TrustEvals is a specialist AI builder with a trust harness underneath. Services exist to set strategy, ship transformation, and build fluency around real operating problems; the customer leaves with software, evidence, and a method they can run.
What stays after the engagement?
The evidence layer, playbook, owner cadence, and operating method stay with the customer team. The engagement is intentionally bounded so the loop can keep running without us in every meeting.
Why keep an independent read?
The team building AI needs measurement in the workflow, and leadership still needs a neutral view of whether the system holds when it reaches production.
UR
Co-founder and CEO
Unmukt Raizada
16 years at the intersection of data and AI; 10 years building AI under audit standards across Goldman Sachs and JPMorgan; co-founder of Thena, backed by Lightspeed and First Round.
Unmukt leads company direction for TrustEvals: AI Strategy, AI Transformation, and AI Fluency with Governance, Audit, and Evals built into every build.
The discipline we sell is the discipline we hold ourselves to: every public number carries its real qualifier, whether stated, measured, modeled, or realized.
The public site shows one founder card for now; customers and partners remain anonymized unless explicitly approved.
AI systems in productionEvaluation and governance evidenceBoard-readable operating viewsArm's-length audit available
Trustable, reliable AI in production
Start with the AI work that moves the number. Keep the proof built in.
Start with Strategy, Transformation, or Fluency; use Quick Audit when the first need is an independent read on what is already running.