Production changed the answer.
Pre-production evals looked clean. Real customer environments exposed drift, prompt changes, and corpus shifts.
About
About TrustEvals.
TrustEvals gives finance leaders one operating view of AI value, AI risk, workforce fluency, and the next move. The AI Audit is the two-week read that starts the work.
Built by practitioners for the people who have to answer the AI question this quarter. TrustEvals comes from production AI work where regressions, policy gaps, and missing proof showed up after launch.
Origin
Where we came from.
Enterprise AI works only in the places it is measured well. The TrustEvals team brings decades of enterprise operating experience and foundational AI experience across big tech, global enterprises, finance, and AI-native companies.
Dashboards missed the outcome.
Adoption tools showed logins. Governance tools showed policy. Nobody showed whether AI produced the promised result.
Finance needed one operating view.
TrustEvals exists to make AI value, evidence, and fluency visible on one operating trace.
Three beliefs
Three beliefs the product is pre-committed to.
01
Belief 1. Full spectrum, not partial views
Adoption, evaluation, and compliance are one measurement problem in three vocabularies. Most of the market sells you three products. We build the single picture. If we lose the moat, it is because someone else also built the single picture, not because someone else built a better module.
02
Belief 2. Continuous, not periodic
Point-in-time attestation was designed for deterministic systems. Production AI isn't deterministic. An audit that is current as of last quarter is already stale. Continuous evaluation is the only answer that survives the question "what was the system doing at 3:47pm on Tuesday?"
03
Belief 3. Baselines, not checklists
Frameworks define what to track. Baselines define what "good enough" means for a specific use case. The enterprise that operationalizes baselines per use case, rather than pretending a single threshold fits every deployment, is the enterprise that can actually run AI at scale.
Team
AI-native team with niche skills.
We are an AI-native team with niche skills across enterprise systems, regulated workflows, and AI-native product companies. Engagement-led rather than headcount-led.
Enterprise operating experience.
Enterprise systems, finance, and applied AI teams that had to ship under scrutiny.
AI experience.
Production ML, evaluation, agent workflows, governance evidence, and the operational patterns that make AI usable.
Practitioners close to the work.
The same team that maps the operating read stays close when a transformation, governance, or fluency workstream follows.
Founders
The founders stay close to the work.
TrustEvals was founded in late 2025 by Unmukt Raizada and Ankit Saxena. The company is built close to customer work: product architecture, finance context, and the operating read behind the AI Audit.
Unmukt Raizada
Co-founder & CEO
Leads company direction, customer work, and the finance AI operating model behind the AI Audit.
LinkedIn →
Ankit Saxena
Co-founder
Leads product architecture, engineering direction, and the systems that turn audit evidence into an operating loop.
LinkedIn →How we ship
We embed. Fixed-scope. Platform-anchored.
The engagement is intentionally bounded. The platform stays, the practitioner transfers the method, and your team owns the operating loop.
01AI Audit
Two-week visibility baseline.
02Workstream
Transformation, Governance, or Fluency.
03Embed
A named practitioner for the window.
04Handoff
Platform, playbook, and operating cadence.
How we work
Platform first. Practitioners where it matters.
TrustEvals deploys as a platform in one day. For most customers that's enough: they run the platform themselves, instrument their internal agents via SDK, and talk to us when they want a compliance mapping turned on.
Where the problem is deeper, a named TrustEvals practitioner embeds for the engagement window. The practitioner transfers method, not code. You walk out with the instrumentation and the playbook for running it yourselves.
Every engagement is scoped, bounded, and transfers methodology to your team.
1
AI Transformation
Re-architect priority workflows around AI. Where value capture actually lives.
2
AI Governance
Continuous evidence pipeline. Catch shadow AI, drift, and policy violations before the audit committee asks.
3
AI Fluency
Every employee meaningfully better at their day job. A workforce that compounds with the model.
Industries
Where the work has taken us so far.
Private EquityBanks & Capital MarketsAsset & Wealth ManagementFintechREITs / Real EstateInsurance
These are the domains where TrustEvals practitioners have done engagement work to date. Named customer stories ship on /resources as customers consent to public attribution.
Ready when you are.
FAQ
Questions about the company.
TrustEvals was founded in late 2025. The first product motion is focused on finance teams that need one operating view of AI value, AI risk, workforce fluency, and the next move.
We are a global team with roots in Bangalore and a US go-to-market footprint: small, deliberate, and engagement-led rather than headcount-led.
Yes. Platform first. We deploy in one day and most customers run the platform themselves. Where the problem is deeper, the work splits across three workstreams: AI Transformation, AI Governance, and AI Fluency. A named TrustEvals practitioner embeds for the engagement window. Most engagements start with the AI Audit, a two-week baseline across all three workstreams. Methodology transfer, not engineers-by-the-hour.