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.
Autonomous agents that automate real business processes.
Retrieval over your own knowledge — grounded and current.
Tailored models with the eval harness to trust them.
Assistants integrated with your systems and data.
Pipelines that make your data usable by models.
AI-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.