Skip to main content
CodeDTX

Enterprise AIproduct engineering

CodeDTX is an enterprise AI product engineering company. We build and modernize web, mobile, and backend systems with governed AI capability built into real production workflows — with evals, guardrails, observability, and human approval where consequential actions need control.

Discuss your systemExplore services
A luminous blue AI orb

What we build

End-to-end product engineering, with AI where the workflow needs it.

Next.js / React

Web application development

Next.js and React portals, dashboards, SaaS products, internal tools, and customer-facing workflows.

Read moreNode.js / APIs

Backend engineering

Node.js services, APIs, databases, authentication, workflow engines, integrations, and observability.

Read moreAndroid / iOS

Mobile application development

Native Android, native iOS, KMP, Flutter, and React Native applications connected to production backends.

Read more
Category

Enterprise AI Product Engineering

The broad page for buyers who need web, mobile, backend, modernization, and AI capability delivered as one production system.

Read the position
AI services
Agents / tools / evalsAI Agent DevelopmentExisting estateAgentic ModernizationMCP / auth / boundariesEnterprise AI IntegrationEmbedded podDedicated AI Engineering Teams

How CodeDTX is different

We treat AI as product infrastructure, not a feature badge.

Buyers still need applications, backends, mobile clients, integrations, support paths, and release discipline. AI only becomes valuable when it is engineered into those systems with clear boundaries.

Workflow-first

AI capability is scoped against a real product or operations workflow, not a model demo.

System-aware

The work includes interfaces, data, permissions, backend contracts, and release paths.

Governed by design

Consequential actions route through human approval, risk tiers, and audit records.

Built to operate

Evals, tracing, cost, latency, fallbacks, and support paths are part of the build.

Governed delivery

Agents propose. Humans decide. Systems execute.

That is the control pattern behind the AI work: proposals carry evidence, decisions are recorded, and execution happens through a separate layer with an audit trail.

Decision ledger
  1. +00:00ProposeAI drafts a change with evidence attached
  2. +04:10ReviewA human sees context, risk, and expected effect
  3. +05:25DecideApproval or rejection is recorded with a reason
  4. +05:27ExecuteA separate system performs approved work
  5. +05:28AuditActor, artifact, cost, and outcome are retained

Start with the system

Building new software, modernizing old software, or adding AI to production?

Tell us what exists, what needs to change, and where AI should improve the workflow. We will respond from an engineering seat.

Talk to our engineers