We Build Your AI Advantage Beyond the Pilot

Enterprise-grade AI for engineering, service and content, governed and optimised to deliver continuous returns

Trusted by

AI Models Were Never the Problem. Last Mile Was.

3 AI Streams

deployed across Telecom, Insurance, Automobile and technology. ONE CX Delivery Benchmark

95%

of enterprise GenAI pilots deliver no measurable P&L return - MIT NANDA, State of AI in Business 2025

Mahindra Auto

the AI content system ONE CX built for dealer-network consistency

The model works. The demo always works. That is never where enterprise AI breaks. It breaks in the last mile, where the model meets real systems, messy data, governance and runaway cost.

That is why most pilots return nothing, and why Gartner expects over 40% of agentic AI projects to be cancelled by end-2027 on cost, unclear value and weak governance.

ONE CX builds that last mile, the integration, the cost intelligence, the DPDP-aligned governance, the measurement, and then we run it. So your AI reaches production and keeps compounding outcomes.

AI Models Were Never the Problem. Last Mile Was.

3 AI Streams

deployed across Telecom, Insurance, Automobile and technology. ONE CX Delivery Benchmark

95%

of enterprise GenAI pilots deliver no measurable P&L return - MIT NANDA, State of AI in Business 2025

Mahindra Auto

the AI content system ONE CX built for dealer-network consistency

The model works. The demo always works. That is never where enterprise AI breaks. It breaks in the last mile, where the model meets real systems, messy data, governance and runaway cost.

That is why most pilots return nothing, and why Gartner expects over 40% of agentic AI projects to be cancelled by end-2027 on cost, unclear value and weak governance.

ONE CX builds that last mile, the integration, the cost intelligence, the DPDP-aligned governance, the measurement, and then we run it. So your AI reaches production and keeps compounding outcomes.

Gaps Our Audits Keep Finding in AI Initiatives

Lives in Demo, Fails in Production

Pilot proves the concept, but is never integrated into the systems, data and workflows where decisions actually happen.

Cost of Scale

Token, inference and compute costs grow faster than business value. The pilot works; the economics of production fail.

Governance Was an Afterthought

Consent, data residency and guardrails were never built into the design. Risk and legal stop the initiative before production.

We Build Beyond Pilots

We build, run and own AI systems, from pilot to production, with a continuous optimisation layer underneath.

AI Content Studio

4 Enterprise Builds

Governed content at scale in English and Indian languages, plus internal knowledge your teams can retrieve, all grounded in your own sources

AI Hyperlocal

AI that runs reviews, sentiment and outlet-level sales intelligence across a brand's retail footprint, in Indian languages.

AI Coding Systems

Coding agents wired into your in-house team's real workflow, production-grade delivery, reviewed and governed for regulated enterprises.

We Build Beyond Pilots

We build, run and own AI systems, from pilot to production, with a continuous optimisation layer underneath.

AI Content Studio

4 Enterprise Builds

Governed content at scale in English and Indian languages, plus internal knowledge your teams can retrieve, all grounded in your own sources

AI Hyperlocal

AI that runs reviews, sentiment and outlet-level sales intelligence across a brand's retail footprint, in Indian languages.

AI Coding Systems

Coding agents wired into your in-house team's real workflow, production-grade delivery, reviewed and governed for regulated enterprises.

Our Operating Layer That Gets AI to Production

Every system we build runs on the same layer, governed, measured and run by us long-term. Steps two, three and four close the three gaps above.

01 Model & Orchestration

We pick, route and swap models per task, so you get the best for the job, not a high level roadmap

02 Integration

Wired into your core systems and the real workflow, with the fallbacks that keep it running when a model or API fails

03 Cost Intelligence

Cost tracked per feature, with caching, routing and smaller models where they hold quality. Unit economics are optimised

04 Governance

Consent, data residency, audit logs and output guardrails built in from the first commit, mapped to the DPDP Act and its rules.

05 Measurement

Instrumented end to end, with quality evaluations tied to business metrics, so value is shown, not guessed.

06 Operation

We monitor for drift, re-evaluate and retrain on a cadence, so accuracy holds and the system keeps improving instead of decaying.

Our Operating Layer That Gets AI to Production

Every system we build runs on the same layer, governed, measured and run by us long-term. Steps two, three and four close the three gaps above.

01 Model & Orchestration

We pick, route and swap models per task, so you get the best for the job, not a high level roadmap

One AI System, Pilot to Production in 90 Days

One use case, validated on your own data, governed, instrumented and live in production within a single quarter.

00–30 Days

Target. Validate

We select a use case, assess feasibility, and test models on your own data to prove they meet the standard before we build.

Outcome: A validated use case, with a model that earns the next phase

31–60 Days

Build. Harden.

Integrated into the workflow, then hardened with grounding, cost controls, guardrails and DPDP governance before it reaches production

Outcome: The governance and controls enterprise deployment needs

61–90 Days

Ship. Instrument

We ship to a controlled production rollout, measure every output against a business metric, and tune until it holds under real use.

Outcome: Live in production, with early signals measured.

91+ Days

Tune. Expand.

We operate it, monitor for drift and retrain, prove the impact over the months that follow, then take the next use case onto the same layer.

Outcome: A system that keeps improving, and the next one queued

Why Enterprise Leaders Trust ONE CX With AI

01

We Operate What We Ship

We monitor drift, retrain on cadence, and we optimise against business metrics long after go-live

We Operate What We Ship

We monitor drift, retrain on cadence, and we optimise against business metrics long after go-live

02

Economics Must Close

Every system carries its unit economics: If the economics don't align with business, we re-architect until they do

Economics Must Close

Every system carries its unit economics: If the economics don't align with business, we re-architect until they do

03

Compliance Approves It Once

Consent, guardrails and audit logs from the first commit, built to the DPDP Act and its Rules, so legal signs off the system once, not every release

Compliance Approves It Once

Consent, guardrails and audit logs from the first commit, built to the DPDP Act and its Rules, so legal signs off the system once, not every release

04

Your Stack. Best Model Per Task

Model-agnostic and stack-agnostic by design, we route each task to the best model for it, on the platforms you already pay for.

Your Stack. Best Model Per Task

Model-agnostic and stack-agnostic by design, we route each task to the best model for it, on the platforms you already pay for.

What Enterprise Leaders Ask the ONE CX AI Team

Why do most enterprise AI pilots fail?

MIT's 2025 State of AI in Business research puts the failure rate at 95%, and attributes it not to model quality but to integration: pilots that never connect to the systems, data and workflows where decisions happen. The pattern we see matches: the model works, the demo impresses, and the orchestration between model and customer is never built. The fix is structural, build the last mile (integration, cost control, governance, measurement) as part of the system, not as a phase two that never comes.

How do you keep AI costs from growing faster than the value?

Cost intelligence is built into the layer, not reviewed at quarter-end: inference cost tracked per feature, response caching, model routing that sends each task to the cheapest model that holds the quality bar, and smaller fine-tuned models where they outperform general ones. The discipline is unit economics, so scale improves the economics instead of breaking them.

How does the DPDP Act apply to AI systems?

Every AI system processing personal data needs verifiable consent, purpose linkage, residency control and an audit trail, built into the architecture, not added before launch. We build to the DPDP Act and its 2025 Rules from the first commit: data residency in Indian cloud regions, output guardrails, explainability logs, so legal signs off the system once, and the board can defend it.

Do you replace our existing AI tools and platforms?

No, we're stack-agnostic by design. Model and orchestration routing means we use the best model per task across providers, integrate with the platforms you already run, and swap components. So your system follows your requirements, not a vendor's roadmap.

How long until AI shows a business result?

A validated use case with a model that clears your quality bar by day 30. A production-grade, governed build by day 60. Live in controlled production with every output instrumented to a business metric by day 90. Then the impact is proven over the months that follow, and the next use case ships on the same layer.

What is AI hyperlocal, and why does a brand with many outlets need it?

AI hyperlocal runs a brand's presence at every outlet, the listings, reviews, content and customer signals, automatically, across hundreds or thousands of locations. A brand with a wide outlet or dealer network cannot manage each location by hand, so the AI does it at a scale, and in a range of Indian languages.

What is an AI content studio?

It is a system that produces a brand's content across many formats, marketing creative, training and L&D material, internal communications, and dealer or retail collateral, on brand and at scale. It is not a single tool; it is the generation engine plus the brand controls and workflows that make the output usable.

Isn't AI content generation commoditised now? Why not just use ChatGPT?

Generation is commoditised; the system around it is not. Anyone can prompt a model, but keeping thousands of pieces on brand, in the right format, in Indian languages, governed, and plugged into how your teams work is the hard part. That operating environment is what we build, and it is what a raw model on its own cannot give you.

One team. Six more ways to drive the outcome

Experience Engineering

Data Engineering

Personalisation

AI MAX

CX Strategy & Transformation

Experience Design (UX)

Martech Solutions

Say Hello Headline: We Build the Last Mile Your AI Pilot Never Reached.One use case, validated on your data. One operating layer, integration, cost, governance, measurement. Live in production in 90 days, and still working a year later.