airisDATA is Innovative's specialized AI and data engineering practice — purpose-built to deliver explainable, governed, production-grade artificial intelligence to banking, financial services, and other regulated enterprises. Founded in 2015 with 150+ engineers across Hyderabad, Pune, and Princeton, we don't experiment in labs. We ship to production.
Most AI vendors talk about pilots. airisDATA delivers production systems that pass model risk management review, satisfy auditors, and earn a place in the trade-floor toolchain — in risk, treasury, product control, and legal.
Pick the capability that fits the work — or combine them inside a managed delivery squad. The framework library underneath is the same.
Multi-agent systems where autonomous agents reason, plan, and execute against complex enterprise workflows. Human-in-the-loop control, explainability (XAI), and guardrails that meet financial-services governance.
Production-grade GenAI built with proper guardrails, retrieval architectures, and continuous evaluation. Move from experimental chatbots to embedded AI capabilities that drive measurable productivity gains.
Classical and deep learning models deployed at production scale across risk forecasting, fraud detection, customer churn, and demand prediction. Models with the rigor required for capital and regulatory use cases.
Modern data platforms that prepare your enterprise to be AI-ready. We re-architect legacy data infrastructure into cloud-native, multi-tenant platforms that scale to billions of records per day.
Active Data Quality monitors regulatory data feeds in real time, flagging anomalies before they become compliance incidents. Combined with our Model Trust framework: AI you can audit, explain, and defend.
Discovery workshops, AI readiness assessments, and roadmap design for enterprises starting their journey. Architecture reviews and CoE design for mature adopters. Our advisors have led production AI delivery at the most demanding institutions.
A selection of solutions delivered into production at named institutions. Many of these have evolved into reusable IP that accelerates new engagements by ~80%.
Automated front-office to back-office trade matching that resolves discrepancies without manual intervention. Replaced multi-hour analyst workflows with sub-second autonomous resolution.
Multi-tenant, self-service finance data platform serving Product Control, Treasury, and Tax. Business teams onboard data sources independently with role-based security and AI-powered DQ alerts.
NLP-driven contract analysis system that supported the IBOR transition at scale, classifying clauses, flagging exposures, and prioritizing remediation across thousands of legal documents.
Real-time anomaly detection on regulatory data feeds, flagging quality issues before submission. Reduced manual data steward workload while raising regulatory submission accuracy.
Asset allocation efficiency engine that improved collateral utilization across complex multi-asset portfolios — freeing up capital and reducing funding costs.
Accelerated quant risk calculation system enabling risk teams to compute VaR on demand rather than overnight — supporting intraday risk decisions and stress testing.
AI-driven Risk-Weighted Asset forecasting that delivers explainable forecasts to challenge legacy capital models. Reduces RWA funding costs and frees up regulatory capital that would otherwise sit idle.
4–6 week structured engagement. Our team works with your stakeholders to assess AI readiness, identify high-value use cases, and design a 12-month roadmap.
Output: prioritized backlog, business case, architecture blueprint.
Outcome-based delivery teams operating under a defined statement of work. Fixed scope, fixed timeline, fixed price — with airisDATA accountable for the deliverable.
Best for: regulatory deadlines, defined initiatives.
Multi-disciplinary squads (data scientists, ML engineers, data engineers, MLOps) embedded into your delivery organization. Operates as an extension of your team.
Cadence: weekly stand-up, quarterly business review.
Because we've built these systems before, every new engagement inherits an accumulated framework library from 12 years of banking AI delivery — typically an 80% head-start. Lower risk, shorter timelines, predictable outcomes.
Whether you're standing up an agentic platform, building a regulatory AI system, or modernizing your data foundation — let's compare what you're trying to do with what we've already built.