From the right agentic bet to a system in production.
We design and deploy agentic systems in your environment, with governance and unit economics built in from the start rather than bolted on later.
See how we work →
uniox ~ %
Agentic AI native · Advisory · Execution · Growth
Only 4 of 33 enterprise AI proofs ever reach production.
We build for the four.
UNIOX.AI is an agentic AI native solutions & services firm. We design, reuse, build, and run agentic AI systems that survive contact with production — plus the go-to-market and diligence work that surrounds them. One team, global markets.
Isometric diagram of 33 enterprise AI proofs of concept funneling through narrowing layers, with only 4 lit in sapphire at the top, in production.
01Why now
Almost everyone has started. Far fewer have something that pays for itself.
The gap between a pilot and a system that ships is where most AI budgets go quiet.
39%
Enterprise profit
of companies report real profit from AI.
McKinsey, 202560%
Abandoned
of AI initiatives without AI-ready data will be scrapped through 2026.
Gartner, 202540%+
Canceled
of agentic AI projects are on track to be cancelled by 2027.
Gartner, 2025Most agentic AI fails quietly, somewhere between the pilot that impressed everyone and the bill that did not.
Global by design
One firm, global markets, one agentic standard.
UNIOX.AI runs as a single practice across the globe, not a headquarters with satellite offices. Local and global experts, the same delivery model, and the same Responsible AI checkpoint, wherever the engagement sits.
UAE
AI advisory & transformation
USA
M&A, capital, and Silicon Valley venture
UK
Technology M&A & investment banking
India
Fintech and enterprise AI execution
SEA
APAC execution & go-to-market
03Three lines, one bench
Industry Experts, wherever you are in the AI economy.
Go to market like you have sold this before.
We help agentic AI founders find their first repeatable revenue motion and build the pipeline that earns a Series A or an enterprise contract — with proof, not a pitch. Delivered under RevenueRamp.
Visit RevenueRamp →Deals underwritten by real agentic AI diligence.
We test the technical and unit-economics claims that decide whether an agentic AI asset is worth what the model says it is.
Explore mandates →04Solutions library
Agentic AI, mapped to where you sit.
Every engagement starts from a library of pain points, use cases, and agent capabilities we have already mapped — by industry and by function — so we spend the first week on your architecture, not on discovery.
05Methodology
Four steps. We do not skip ahead to the slides.
Assess → Architect → Prove → Scale. Slower on step one, faster on every one after it. Most engagements reach production in eight to twelve weeks.
- Step 01
Assess
We audit your data, infrastructure, team, and governance gaps before any agent writes to a production system.
- Step 02
Architect
We pick the bet worth making and design the agent system to fit your problem, not our preferred vendor.
- Step 03
Prove
We ship one working version and check that the cost and the behaviour both hold up.
- Step 04
Scale
We move it into production, set the FinOps guardrails, and hand the keys to your team.
assess → architect → prove → scale
06Proof
What changed after we showed up.
01
Case study
Advisory & Transformation
The onboarding pilot that would not ship.
A document-heavy KYC workflow at an enterprise bank had been stuck in pilot for eleven months. The model performed; the economics did not. We rebuilt it as an agentic pipeline, set per-case cost controls, and moved it into production under the bank's existing governance.
OutcomeProduction in ten weeks. Cost per case fell by 60% — far enough to keep it there, and the pilot became a standing mandate.
02
Case study
Technology M&A & IB
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 by 20% — the terms before signing.
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
07Responsible AI
We would rather catch it in review than read about it later.
Responsible AI is not a service we sell. It is the condition for every agent we ship, whether we are building, growing, or doing diligence.
Independent, by design
We resell nothing and take no platform commissions. Fifty-plus platforms evaluated, and the only side we are on is yours.
Nothing ships unexamined
Bias, provenance, hallucination risk, and human oversight are settled before launch, not after the incident.
Secure and sovereign
Your data stays yours, in your region, under your regulator. Sovereign hosting is the default where the law demands it, not an upsell.
You keep the keys
The data, the systems, the runbooks. You could end the engagement tomorrow and everything keeps running.
08Questions, 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 expert 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.