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How we work

Four steps. We do not skip ahead to the slides.

Every engagement, in every market, runs the same sequence: assess, architect, prove, scale. It is slower on slide one and faster on every one after that.

Four steps. We do not skip ahead to the slides.

  1. Step 01

    Assess

    We audit your data, infrastructure, team, and governance gaps before any agent writes to a production system.

  2. Step 02

    Architect

    We pick the bet worth making and design the agent system to fit your problem, not our preferred vendor.

  3. Step 03

    Prove

    We ship one working version and check that the cost and the behaviour both hold up.

  4. Step 04

    Scale

    We move it into production, set the FinOps guardrails, and hand the keys to your team.

assess → architect → prove → scale

02What each step means

No step gets skipped, whatever the deadline.

01 · Assess

Before any agent touches a production system, we know what it is working with.

  • Data readiness and lineage audit
  • Infrastructure and integration surface review
  • Team capability and change-readiness check
  • Governance and regulatory gap analysis

02 · Architect

We pick the bet worth making and design the agent system around your problem, not around a vendor roadmap.

  • Use case prioritization against cost-to-value
  • Agent and human-in-the-loop workflow design
  • Model and platform selection, evaluated independently
  • AI FinOps guardrails designed in from the start

03 · Prove

One working version, checked against both behaviour and cost before it goes anywhere near scale.

  • A single production-grade version, not a demo
  • Cost-per-transaction and accuracy benchmarks
  • Responsible AI checkpoint — bias, provenance, oversight
  • Stakeholder sign-off against numbers, not a screenshot

04 · Scale

Into production, with the guardrails already in place, and the keys handed to your team.

  • Production rollout under existing governance
  • FinOps monitoring and cost controls live
  • Runbooks, documentation, and team handover
  • Ongoing measurement against the numbers that mattered on day one

03Case studies

What changed after we showed up.

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.

Read every case study →

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.

Want to see this run against your stack?

The assessment is a short, scoped engagement on its own. It tells both of us whether the rest is worth doing.

Start with an assessment