We integrate AI into the systems your business already runs on — grounded in your own data, measured against real KPIs, and operated with the same rigour as everything else in the stack.
Twenty-two years of software, data, and infrastructure engineering behind every model we put in front of your users.
Most AI projects stall between the demo and the ledger. We start from the workflow that costs you money, connect the model to your governed data, and ship it inside the software your people already use — with evaluation and guardrails in place before anyone depends on it.
Assistants that answer from your documents, policies, and records — embedded in your portal, ERP, or support desk, with citations and access control that match your existing permissions.
Document intake, reconciliation, classification, routing, drafting. Multi-step agents with tool access, human approval gates, and a full audit trail of every action taken.
Forecasting, demand and churn prediction, anomaly detection, scoring. Built on your history, validated against holdout data, and delivered as a service your applications can call.
The part that decides whether AI survives contact with auditors: evaluation suites, prompt and model versioning, cost and latency budgets, PII handling, and a documented fallback for every failure mode.
Value per workflow, data readiness, and the constraints that rule options out early.
A working slice on your real data, scored against a baseline you agree on up front.
Into the live application, behind your auth, with evals, guardrails, and cost limits.
Staged release, adoption tracking, and the KPI dashboard that proves the case.
Reporting fails for two reasons: the numbers disagree, or nobody can tell what to do about them. We fix the first with a governed semantic layer, and the second with KPI design — every metric owned, defined once, and tied to a decision someone is accountable for.
Metric trees from board KPI down to operational driver, with owners, targets, thresholds, and a single written definition per metric.
Executive, operational, and self-serve layers — designed for the decision at hand, not decorated with every chart the tool can draw.
Modelled, tested pipelines from source systems to a governed layer — so finance and operations quote the same number in the same meeting.
Ask-your-data in plain language, narrative summaries of the week, and automatic alerts when a KPI breaks its threshold.
Cost per order rose in the two southern depots only; driver is overtime, not volume. Suggested owner: Regional Ops.
Line-of-business platforms, integrations, and the modernisation of software that has outlived its architecture. Greenfield products and rescue missions alike, shaped around how your business actually works.
Web and mobile applications built for the specific shape of your operation — not bent around someone else's template.
Legacy systems restructured incrementally, with integrations that let old and new run side by side while the migration completes.
The unglamorous work that decides whether everything else holds up: pipelines, environments, observability, and release discipline.
Security and quality applied as a discipline, not a checkbox — and extended to cover what AI adds to the threat surface: prompt injection, data leakage through context, and model access to systems of record.
Application and infrastructure review, access model design, hardening, and remediation planning with priorities you can defend.
Tenant isolation, PII redaction, prompt-injection defence, retention rules, and audit logs for every model call.
Test strategy, automation suites, and release gates run by people who did not write the code.
Load profiling, query and cost tuning, and capacity planning before the traffic arrives, not after.
Most engagements fail not in the code, but in the decisions made before the code is written. Atzonix exists to put twenty-two years of those decisions on your side of the table.
We were building data platforms long before models got interesting. The AI we ship sits on pipelines, permissions, and architecture that were designed properly.
Discovery, architecture, build, deployment, and ongoing operations — under one engagement, with one accountable team. No vendor stitching.
Proven technology by default; new tools only where they genuinely earn it. The systems we build are still standing years later.
Clear scope, clear trade-offs, clear estimates — and the discipline to say what we won't do as readily as what we will.
A predictable engagement model designed to make the first conversation worthwhile and the last conversation unnecessary.
A focused conversation to understand the system, the constraints, and what success actually looks like for you.
A written proposal with scope, approach, timeline, and a clear engagement model — no surprises later.
Senior engineers, weekly progress visibility, and working software you can review at every milestone.
Documented handover, optional retainer for operations, and a phone number that picks up when something matters.
Tell us the workflow that is costing you most. We respond to every serious inquiry within one business day, and the first conversation is on us.