Production AI for Indian enterprises, built to the DPDP deadline.
India is adopting AI faster than most markets and capturing less value from it than it should. The gap is rarely the model. It is data quality, legacy integration, governance that lags deployment, and a compliance calendar that has now been published with dates on it.
Noida
- Enterprise and mid-market IT leadersMoving from multiple AI pilots to a governed production estate, with legacy systems that were never designed to feed a model.
- SaaS and product companiesAdding AI features to a product sold in India and abroad, which means one build has to satisfy DPDP, GDPR and US buyer questionnaires at once.
- NBFCs, fintechs and regulated firmsWhere the RBI holds the regulated entity accountable for its vendors, so the vendor's engineering has to stand up to the regulated entity's audit.
- Global Capability CentresBuilding AI capability for a parent company abroad while short of senior AI engineers, and needing a partner that can work inside their delivery rhythm.
What we see holding AI programmes back in this market, from the engagements we run and the regulation now in force.
- 01
Data accuracy and legacy integration stall most programmes
Indian organisations cite data accuracy and management as the leading obstacle to scaling AI, with legacy integration close behind. Pilots run on clean extracts; production runs on the systems as they are.
- 02
Governance and security lag the pace of deployment
A majority of enterprise leaders describe data governance and security as a severe barrier to scaling AI, and most programmes reach production before access controls, logging and review workflows exist for the AI layer.
- 03
The DPDP calendar now has dates on it
The Rules were notified in November 2025. Consent managers become operational in November 2026 and full compliance is due by 13 May 2027, with penalties of up to ₹250 crore for inadequate security safeguards. Systems being built now will be live when enforcement starts.
- 04
Six hours to report an incident
CERT-In requires incident reporting within six hours of detection, and RBI-regulated entities carry a parallel six-hour clock. A system that cannot tell you what happened, to whose data, inside that window is a liability.
- 05
Regulated entities are accountable for their vendors
The RBI's outsourcing directions make the bank or NBFC answerable for a vendor's breach, and the November 2025 NBFC outsourcing directions set an April 2026 transition for existing contracts. Vendor engineering is now inspected, not assumed.
- 06
Senior AI talent is scarce, especially inside GCCs
GCCs report an AI talent gap of roughly 38 to 42 percent, and the shortage is sharpest for people who combine model skills with production engineering judgement.
What each obligation means for the system itself. We build the controls; your counsel confirms the legal position.
Security review inside the build, not after it
Threat model, access control, secrets handling, dependency scanning and audit logging are designed with the architecture and documented as evidence your own auditor can read. Nothing is retrofitted for a questionnaire.
Designed so the six-hour CERT-In report is answerable from your own logs.
AI audit and evaluation you can hand to a regulator
Every AI feature ships with an evaluation set, grounding and hallucination measurements, regression runs per release and traced inputs and outputs. The result is an evidence pack, not a demo.
Evaluation evidence that survives an RBI or internal-audit review of an AI feature.
Compliance mapped to the build
We map the system to the obligations that apply to it, control by control, so legal and security teams review a document rather than reverse-engineer the code. We are engineers, not your counsel, and we say so.
DPDP obligations mapped to consent capture, retention and processor terms in the build.
Data residency and model-provider exposure settled in architecture
Where data lives, which model providers see which fields, and what stays in-region are decisions made in the architecture phase with options costed, not discovered in procurement.
In-country hosting choices and which fields leave for a model provider, decided and documented.
Human oversight for consequential decisions
Approval steps, confidence thresholds and human review queues are built into workflows that touch money, eligibility, health or employment, with logs that show who approved what.
Approval steps for credit, claims and eligibility decisions, with an audit trail.
Portability and cost control by design
Model-portable interfaces, cost budgets per feature and monitoring from day one, so a provider change or a price change is a configuration decision rather than a rebuild.
Model-portable interfaces so a provider or price change does not force a rebuild.
Only the services and AI solutions we run in production are listed. Anything outside this catalogue is not something we sell.
- Cloud & DevOpsWe make releases safer, infrastructure more observable, and product systems ready to scale.
- Custom Software DevelopmentWe design and build secure, scalable software around the way your business actually operates.
- Data EngineeringWe connect scattered business data into reliable pipelines, dashboards, and decision-ready intelligence.
- Resource AugmentationAdd capable engineers, designers, and AI specialists who work with your team, tools, and delivery rhythm.
- Agentic AIBuild AI agents that route work, coordinate tools, review information, and assist operational teams.
- Generative AIDesign assistants, content workflows, knowledge tools, and product features powered by generative models.
- MLOpsDeploy, monitor, evaluate, and improve AI models after launch.
- Predictive AnalyticsTurn historical and operational data into forecasts, risk signals, and decision-ready analytics.
- RAG SystemsCreate retrieval-augmented search and answer systems over private documents and business knowledge.
Sectors we serve hereEducation · FinTech · Insurance · Manufacturing · SaaS
- Same time zone, same working dayOur engineers are in Noida. Stand-ups, demos and incident response happen in your hours, in person when it helps.
- Discovery first, priced honestlyA short discovery sprint settles scope, data readiness and the DPDP and sector obligations that apply, before a fixed-scope estimate is written.
- Contracts your auditor can useProcessor terms, security clauses, audit rights and exit provisions are standard in our engagement, because your regulator expects them in yours.
- Cost-aware deliveryCloud and model-provider costs are budgeted per feature and monitored from the first release, so the economics of an AI feature are known before it scales.
Yes. AWS, Google Cloud and Azure all offer Indian regions, and we design so personal data can stay in-country while model calls are routed and minimised deliberately. Where a model provider has no Indian region, we document what leaves and why.
Building for India? Let's take it to production.
Share your product, AI, workflow, or data challenge. We will help identify the right starting point, delivery risks, scope, timeline, and next step.
Book a Project CallOr email sales@ilytixservices.com