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What changed after we showed up.

Case studies and testimonials from advisory, growth, and capital engagements. Figures reflect work across UNIOX.AI and RevenueRamp; named references are available to a serious prospect under NDA.

01Case studies

Five engagements, five different rooms.

01 Case study Advisory & Transformation
FintechKYC & OnboardingAgentic AI

The onboarding pilot that would not ship.

A document-heavy KYC workflow at an enterprise bank had been in pilot for eleven months. The model performed; the economics did not. We rebuilt the workflow as an agentic pipeline, set per-case inference-cost controls, and moved it into the production environment under the existing governance regime.

OutcomeProduction in ten weeks. Cost per case fell far enough to keep it there, and the pilot became a standing mandate.
02 Case study Technology M&A & IB
Enterprise SaaSBuy-side M&AUnit economics

The inference cost the seller had not modeled.

On a buy-side review of an AI-enabled SaaS target, the acquirer asked us to test the pitch against the architecture. We ran the model stack, data provenance, and cost-at-scale ourselves rather than relying on the data room, and quantified the gap between the quoted and the real cost per user.

OutcomeThe number changed the terms before signing.
03 Case study Startup Growth · RevenueRamp
AI-native startupGTM & PricingSeries A

From pre-revenue to a raise with signed pilots.

A technical founder had a working product and no repeatable buyer. We narrowed the ideal customer to a single first wedge, built the discovery and proof-of-value playbook, and tied pricing to the value the model actually delivered rather than to seats.

OutcomeThe founder walked into the raise with signed pilots, not a pitch. The pipeline did the talking.
04 Case study Advisory & Transformation
InsuranceClaimsAgentic AI

Three weeks to three days on first notice of loss.

A mid-size carrier's claims intake was drowning in document triage during catastrophe season. We deployed an agentic pipeline for document extraction, coverage verification, and adjuster routing, with a human reviewer on every claim above a set exposure threshold.

OutcomeMedian cycle time on standard claims fell from three weeks to three days, with fraud referrals up, not down.
05 Case study Advisory & Transformation
HealthcareRevenue CycleAgentic AI

Denials that stopped compounding.

A regional health system's revenue cycle team was losing ground to eligibility and coding-driven denials. We built agentic eligibility verification and claim-scrubbing ahead of submission, with appeals tracking for what still slipped through, aligned to HIPAA and HITRUST requirements throughout.

OutcomeFirst-pass claim acceptance rose enough to fund the next two use cases from cash already being left on the table.
10+
AI startups launched
50+
Platforms evaluated, independently
4
Markets, one standard

Figures reflect work across UNIOX.AI and RevenueRamp engagements. We will walk a serious prospect through named references under NDA.

02Testimonials

In our clients’ own words.

They did not hand us a strategy and leave. They stayed until the thing was running, and then showed us how to run it without them.
Head of Data · Enterprise financial services client
We had already been burned by one vendor that oversold the model and undersold the cost. UNIOX.AI was the first team that led with the unit economics, not the demo.
COO · Regional insurance carrier
The partner on our first call was the partner in every working session after. That alone put them ahead of two Big Four teams we also ran a bake-off against.
VP Revenue Cycle · Multi-site health system

03Questions, answered

The things people ask before the first call.

What does UNIOX.AI do?

UNIOX.AI is an agentic AI native services firm. We help founders ship AI products, enterprises move agentic AI from pilot to production, and investors run technical diligence on AI deals. Every engagement is led by senior people and ends in a working system, not a slide deck.

How are you different from a Big Four AI practice?

The partner who scopes your engagement is the practitioner who delivers it. You get senior judgment on every call, faster decisions, and no junior team learning on your budget. We take on a limited number of clients so each one gets real attention.

What is AI FinOps?

AI FinOps is the practice of managing the cost and unit economics of AI systems. It means knowing your cost per query, per user, and per model before you scale, then setting controls so inference spend stays predictable. We build it in from the start rather than after the bill arrives.

How fast can you get an agentic AI system into production?

Most of our build engagements reach production in eight to twelve weeks, depending on data readiness and governance needs. We start with a short assessment, ship one working version, and check that the cost and the behaviour both hold up before we scale it.

Do you work with regulated industries?

Yes. We work across financial services, healthcare, insurance, and other regulated sectors. Every engagement carries a Responsible AI checkpoint covering bias, data provenance, and human oversight, and we support sovereign or in-region hosting where your regulator requires it.

Where does UNIOX.AI operate?

UNIOX.AI runs as one practice across the United States, the UAE, India, and the United Kingdom, with the same senior people and the same delivery standard in every market.

Want the reference list, not just the excerpts?

We will walk a serious prospect through named references under NDA.

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