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CodeDTX

Enterprise AI product 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.

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Propose–Decide–Execute

CodeDTX builds agentic systems on the Propose–Decide–Execute pattern: agents may only write proposals with evidence attached, a named human records an approval or rejection with a reason, and a separate execution layer carries out approved work and logs the artifact. No agent holds a write tool to the outside world.

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

What we build

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

Read the position
  1. Agents / tools / evals

    AI Agent Development

    Production agents for enterprise workflows: architecture, tool use, memory, orchestration, and structured outputs - engineered to run reliably in production, not as demos.

  2. Existing estate

    Agentic Modernization

    Make the software you already run agentic. We retrofit agent capability into your existing estate - APIs, databases, SaaS, legacy systems - without a rebuild.

  3. MCP / auth / boundaries

    Enterprise AI Integration

    MCP servers, tool contracts, authentication, permissions, and data boundaries - the integration layer that connects agents to enterprise systems safely.

  4. Embedded pod

    Dedicated AI Engineering Teams

    Prefer to build in-house? Engage a dedicated pod of agentic-AI engineers working inside your organization - capability transfer included.

  5. Next.js / React

    Web application development

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

  6. Node.js / APIs

    Backend engineering

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

  7. Android / iOS

    Mobile application development

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

Have a system that needs AI inside it?

Tell us what runs today and where a decision currently waits on a person.

Discuss your system

How the work runs

Four phases, and an audit record that outlives all of them.

  1. 1

    Propose

    The agent analyses live system state and drafts a change, with the evidence it relied on attached to the proposal.

  2. 2

    Decide

    A named human approves, edits, or rejects with a reason. Risk tier determines who is allowed to decide.

  3. 3

    Execute

    A separate execution layer performs approved work - merge, publish, call, write - and records the resulting artifact.

  4. 4

    Audit

    Actor, reason, evidence, artifact, tokens, and cost are retained for every run, so any decision can be reconstructed later.

Reference architecture

Six layers we build and review against.

  1. 1

    Agent layer

    Agent architecture, tool use, memory and context, multi-agent patterns, structured outputs, orchestration.

  2. 2

    Integration layer

    MCP servers, tool contracts, API and database adapters, authentication, permissions, legacy system access.

  3. 3

    Knowledge layer

    Retrieval and RAG, vector and search architecture, enterprise knowledge sources, data access controls.

  4. 4

    Reliability layer

    Evals, tracing, observability, cost and latency budgets, fallbacks, regression tests.

  5. 5

    Safety layer

    Guardrails, prompt-injection defense, PII and data boundaries, human-in-the-loop gates, audit trails.

  6. 6

    Product layer

    The application people actually use: interfaces, approval queues, and operational runbooks.

Where it ships

Every industry arrives with its own constraint.

Not sure whether you need an agent or an app?

That question is worth answering before anyone writes an architecture document.

Talk to our engineers

Frequently asked questions

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