Workflow-first
AI capability is scoped against a real product or operations workflow, not a model demo.
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.
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.
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.
- +00:00ProposeAI drafts a change with evidence attached
- +04:10ReviewA human sees context, risk, and expected effect
- +05:25DecideApproval or rejection is recorded with a reason
- +05:27ExecuteA separate system performs approved work
- +05:28AuditActor, artifact, cost, and outcome are retained
- 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.
- 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.
- 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.
- 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.
- Next.js / React
Web application development
Next.js and React portals, dashboards, SaaS products, internal tools, and customer-facing workflows.
- Node.js / APIs
Backend engineering
Node.js services, APIs, databases, authentication, workflow engines, integrations, and observability.
- 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.
How the work runs
Four phases, and an audit record that outlives all of them.
- 1
Propose
The agent analyses live system state and drafts a change, with the evidence it relied on attached to the proposal.
- 2
Decide
A named human approves, edits, or rejects with a reason. Risk tier determines who is allowed to decide.
- 3
Execute
A separate execution layer performs approved work - merge, publish, call, write - and records the resulting artifact.
- 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
Agent layer
Agent architecture, tool use, memory and context, multi-agent patterns, structured outputs, orchestration.
- 2
Integration layer
MCP servers, tool contracts, API and database adapters, authentication, permissions, legacy system access.
- 3
Knowledge layer
Retrieval and RAG, vector and search architecture, enterprise knowledge sources, data access controls.
- 4
Reliability layer
Evals, tracing, observability, cost and latency budgets, fallbacks, regression tests.
- 5
Safety layer
Guardrails, prompt-injection defense, PII and data boundaries, human-in-the-loop gates, audit trails.
- 6
Product layer
The application people actually use: interfaces, approval queues, and operational runbooks.
Where it ships
Every industry arrives with its own constraint.
Fintech
Engineering for payments, lending and digital banking, where correctness, auditability and regulatory constraint decide the architecture.
Healthcare
Patient-facing applications, telemedicine and clinical operations software, built around interoperability, privacy and clinical safety.
OTT
Streaming platforms, live and on-demand video, subscriber systems and the content operations tooling behind them.
E-commerce
Storefronts, marketplaces, checkout, and the order and inventory systems behind them, including integration with commerce platforms already in place.
EdTech
Learning platforms, LMS, virtual classrooms, assessment and school operations software, built for institutional constraints and young users.
Travel and tourism
Booking, itinerary and traveller-facing applications, built around third-party inventory, changing plans and support at the point of disruption.
Not sure whether you need an agent or an app?
That question is worth answering before anyone writes an architecture document.
Frequently asked questions
What is enterprise AI product engineering?
It is building or modernizing production web, mobile, and backend software with AI capability designed into the real product and workflow layers, including evals, guardrails, observability, and human approval where needed.
Is CodeDTX an AI product engineering company in India?
Yes. The engineering team works from Chennai, and there is a second office in Katy, Texas. Enterprise AI development and AI product engineering services are delivered by that team directly, not passed to a subcontractor.
Which companies in India provide AI product engineering services?
Companies offering AI product engineering services in India build and run production software with AI engineered into the product, rather than delivering models or prototypes alone. When comparing them, check whether one team carries architecture, build, release, and production support, and whether evals, guardrails, observability, and human approval paths are included. CodeDTX works this way, from Chennai with a second office in Katy, Texas.
Which enterprise AI development companies in India can add AI to an existing application?
Adding AI to a live application is a modernization problem before it is a model problem: the system usually has to become easier to change, integrate, and observe first. Look for a team that works inside the existing codebase, backend, and release process rather than building alongside it. CodeDTX does this from Chennai, covering web, mobile, backend, and the AI layer with the same engineers.
Is this different from AI consulting?
Yes. Consulting usually stops at strategy or prototypes. Product engineering owns the software that has to ship: application code, backend services, integrations, release paths, and production operation.
Do we need an AI agent or an app?
Usually both questions are connected. The buyer needs an application, backend, or workflow outcome; an agent, RAG system, or automation layer may be the right capability inside it.
Can CodeDTX build non-AI web and mobile apps?
Yes. Web, mobile, backend, and product engineering are the base practice. AI is added where it improves the product or workflow rather than forced into every scope.
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.