Zetta360 takes scaleups and enterprises from “where’s the value in AI?” to a system running in production — strategy and engineering under one roof. 22+ senior engineers. Built for Onfido, Danske Bank, and Revolut.
Enterprise AI doesn’t fail on models — it fails on the last mile: integration, data, evaluation, and change management. The gap between a demo and a system in production is engineering. That’s the gap we close.
Strategy that ships
We don’t stop at recommendations. We put the system in production.
Senior engineers at the core
22+ A+ engineers delivering regulated-enterprise production work — no proxy teams.
Outcomes from day one
Measured in revenue, cost, and time saved — not model demos.
De-risked adoption
A staged path so you adopt AI without betting the business.
Four steps from “where’s the value?” to a system at scale. Start small, prove it, then build — each step earns the next.
~1 week · fixed fee
Find where AI creates real value — and what it’s worth.
Teams unsure where AI pays off. The low-commitment front door.
3–4 weeks
Build one use case for real — and measure it.
Teams with a candidate use case who need proof before committing.
6–12 weeks
Put the system into production — integrated and compliant.
Validated use cases ready to deploy.
Ongoing
Expand across workflows — and level up your team.
Organizations scaling AI beyond the first system.
The full stack of applied AI — delivered by senior engineers, not slideware.
Actionable strategy tied to your P&L — not hype.
Learn moreAutonomous agents that automate real business processes.
Learn moreRetrieval over your own knowledge — grounded and current.
Learn moreTailored models with the eval harness to trust them.
Learn moreAssistants integrated with your systems and data.
Learn morePipelines that make your data usable by models.
Learn moreAI-powered fraud detection and behavioral analysis, wrapped in a three-pronged security framework that hardened a banking app’s identity and transactions — without slowing users down.
A driver app + admin dashboard that pulled a cargo company out of pandemic deadlock and unlocked 200% fleet growth in a year.
Replatformed a 12M-page-view legacy PHP marketplace onto a cloud-agnostic stack — load times from 24+ seconds to instant, delivered a month early.
Mobile SDK — detection & auth
Engineering collaboration
ATM system
Project collaboration
Most enterprise AI dies between the demo and production. The blocker is rarely the model — it’s integration, data, and evaluation. Here’s where it breaks and how to close the gap.
The proof-of-concept worked. Now comes the 80% no one scoped: evaluation, integration, monitoring, and cost. A practical checklist for getting AI into production.
Not every AI problem deserves a custom model — and not every one is solved by an off-the-shelf API. A simple framework for choosing between buy, build with RAG, and fine-tune.