3
AI streams deployed as enterprise-grade builds across telecom, insurance and automotive - ONE CX™
95%
of enterprise GenAI pilots deliver no measurable P&L return — MIT NANDA, State of AI in Business, 2025
93%
listings completeness across 5,300 stores, run by the AI layer we built for rStorefront
The model works. The demo always works.
Enterprise AI breaks in the last mile, where models must integrate with fragmented systems, unreliable data, governance requirements and production economics.
That is why most pilots never become business systems, and why Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027 due to rising costs, unclear value and weak governance.
ONE CX builds the last mile: production integrations, enterprise workflows, cost intelligence, DPDP-aligned governance and outcome measurement. Then we operate and optimise the system so AI keeps delivering measurable business returns, quarter after quarter.
AI Models Were Never the Problem. The Last Mile Was
3
AI streams deployed as enterprise-grade builds across telecom, insurance and automotive - ONE CX™
95%
of enterprise GenAI pilots deliver no measurable P&L return — MIT NANDA, State of AI in Business, 2025
93%
listings completeness across 5,300 stores, run by the AI layer we built for rStorefront
The model works. The demo always works.
Enterprise AI breaks in the last mile, where models must integrate with fragmented systems, unreliable data, governance requirements and production economics.
That is why most pilots never become business systems, and why Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027 due to rising costs, unclear value and weak governance.
ONE CX builds the last mile: production integrations, enterprise workflows, cost intelligence, DPDP-aligned governance and outcome measurement. Then we operate and optimise the system so AI keeps delivering measurable business returns, quarter after quarter.
Gaps Our Audits Keep Finding in AI Initiatives
Lives in Demo, Fails in Production
The pilot proved the model, but it never integrated into the systems, data, and workflows where business decisions happen. AI remained a showcase instead of a business capability.
AI Costs Scale. Business Value Doesn't.
Token spend, inference costs, and compute kept rising. Business outcomes didn't. Without cost intelligence and optimisation, AI became increasingly expensive to operate.
Governance Was an Afterthought
Privacy, consent, security, and compliance were added after deployment instead of built into the architecture. As AI adoption expanded, governance became the biggest barrier to scaling.
We Build Beyond Pilots
From pilot to production, we build governed AI systems that integrate with your business, optimise continuously, and deliver measurable returns.
We Build Beyond Pilots
From pilot to production, we build governed AI systems that integrate with your business, optimise continuously, and deliver measurable returns.
Our Operating Layer That Gets AI to Production
Our framework that gets enterprise AI to production, and keeps it delivering outputs and measurable outcomes.
Model & Orchestration
We select, route and switch models based on the task, ensuring every use case runs on the right model instead of being constrained by a single vendor.
One AI System. Pilot to Production in 90 Days.
We identify the right use case, validate the model on your own data, and take it through production with governance, measurement and continuous optimisation built in.
Why Enterprise Leaders Trust ONE CX With AI
01
We Operate What We Build
Production is the starting point. We monitor drift, optimise performance, retrain where needed, and improve business outcomes long after go-live.
We Operate What We Build
Production is the starting point. We monitor drift, optimise performance, retrain where needed, and improve business outcomes long after go-live.
02
Unit Economics Must Work
Every AI system is measured against its unit economics. If costs outgrow business value, we optimise the architecture until the economics hold.
Unit Economics Must Work
Every AI system is measured against its unit economics. If costs outgrow business value, we optimise the architecture until the economics hold.
03
Governance Is Built In
Consent, guardrails, auditability and DPDP-aligned controls are built in from the first commit, so compliance is engineered—not reviewed release by release.
Governance Is Built In
Consent, guardrails, auditability and DPDP-aligned controls are built in from the first commit, so compliance is engineered—not reviewed release by release.
04
Your Stack. Best Model Per Task.
Model-agnostic by design, we route every task to the best-fit model while integrating with the platforms and infrastructure you already use.
Your Stack. Best Model Per Task.
Model-agnostic by design, we route every task to the best-fit model while integrating with the platforms and infrastructure you already use.
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 last mile 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 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.
Can AI coding agents work inside a regulated enterprise?
Only with the rails around them. Agents run inside your environment, on your repositories, under your access controls, no code leaves the perimeter. Every agent-authored change enters the same review, test and approval gates a human's does, with an audit trail of what was generated, by which model, and who approved it. Your engineers stay accountable for what ships; the agent removes the work they should never have been doing by hand.
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 scale, and in a range of Indian languages.
Don't we need the data layer sorted before any of this?
Partly.Pretending otherwise is how pilots die. AI MAX validates every use case on your own data in the first 30 days, precisely to find out what your data can already support. Where the foundation is sound, we build on it. Where it isn't, our Data Engineering practice fixes the specific gap the use case needs, not a two-year data-platform programme before any AI ships.
What does enterprise AI look like across telecom, insurance, automotive and retail?
The operating layer is the same, integration, cost intelligence, governance, measurement. The use cases and constraints differ. In telecom, it is content and service at subscriber scale, in Indian languages, on consented data. In insurance, it is regulated workflows, document intelligence, vernacular customer communication, where every output must be auditable and DPDP-defensible. In automotive, it is the dealer network: AI carrying lead qualification, service communication and outlet-level intelligence across hundreds of touchpoints. In retail, the AI layer we built for rStorefront runs listings across 5,300 stores at 93% completeness, the same hyperlocal pattern that applies to any wide-footprint brand. Different sectors, one discipline: the last mile built as part of the system, not promised as phase two.
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.