The Trust Deficit
Shopping Agents Are Deciding What You See and What You Pay.
India's e-commerce is exploding — projected to reach ₹7 lakh crore by FY25. AI agents now autonomously recommend products, set dynamic prices, manage inventory, and handle customer queries in 22 languages. As these systems become agentic — reasoning, deciding, and acting without human intervention — the governance gap between what agents do and what anyone can explain is widening fast.
Our Approach
Commerce Agents Need Operations, Not Just Models
Most vendors ship a recommendation engine and call it done. Agentic commerce — where AI reasons, decides, and acts autonomously across pricing, inventory, and customer interactions — needs a fundamentally different governance architecture.
Commerce Agents Without Operations
- Deploy recommendation agent, validate accuracy on test set
- No reasoning trace for why specific products are shown to a customer
- Pricing agent changes prices dynamically with no audit trail
- Customer service agent hallucinates product features and delivery timelines
- No fairness monitoring for recommendations across demographics
- ONDC compliance is manual and after-the-fact
Commerce Agents with Rotavision
- Every agent registered with autonomy level and impact scope
- Reasoning capture for every recommendation and pricing decision
- Customer service agents with hallucination detection — never promise what doesn't exist
- Fairness monitoring to prevent recommendation bias across demographics
- Bounded autonomy for pricing agents — guardrails prevent predatory pricing
- ONDC and DPDP-compliant audit trails from day one
Where It Matters
Agentic AI for India's Commerce Reality
Not generic recommendation engines — autonomous agents solving the specific problems Indian retailers face every day.
India's next 300 million online shoppers are vernacular-first. They search in Hindi, negotiate in Tamil, compare prices in Telugu, and chat in code-mixed Hinglish. These shoppers are coming from Tier 2, 3, and 4 cities — and the agents serving them must do far more than translate. They must understand regional preferences, local festivals and shopping seasons, regional sizing conventions, and cultural context around gifting and occasions.
When a vernacular commerce agent recommends a product or declines a return, its reasoning must be explainable — not just in the developer's logs, but in the customer's language. When an agent upsells a premium product to a first-time online shopper, the system must capture why. Fairness monitoring ensures recommendation quality doesn't degrade for users interacting in less-resourced languages.
Vishwas monitors agent recommendations for fairness across language, geography, and demographic categories — with explainability in 22 languages. Orchestrate manages agent lifecycle, policy enforcement, and reasoning capture across every vernacular interaction.
Quick commerce — Blinkit, Zepto, Instamart — grew over 60% year-on-year, and demands real-time pricing decisions. A pricing agent operating across thousands of dark stores must decide in milliseconds: adjust prices for demand spikes, manage markdowns on perishables, coordinate stock transfers between locations. These agents operate with near-zero human oversight at 10-minute delivery speed.
But pricing agents without guardrails are a liability. Surge pricing that exploits demand spikes during extreme weather or supply disruptions is a regulatory and reputational risk. Inventory agents coordinating across dark stores need bounded autonomy — they can rebalance stock within guardrails, but escalate when decisions cross thresholds. Every pricing change and inventory movement needs an audit trail that explains why.
Guardian monitors pricing and inventory agents for drift, anomalies, and policy violations in real time. Orchestrate enforces bounded autonomy — guardrails that prevent predatory pricing while allowing agents to operate at the speed quick commerce demands.
ONDC is India's public digital infrastructure play for commerce — an open protocol where agents from different sellers, logistics providers, and buyer platforms interact without a central platform controlling the experience. A buyer agent on one app discovers products listed by a seller agent on another, with a logistics agent from a third provider coordinating delivery. This is multi-agent commerce at national scale.
Agent governance across an open network is fundamentally different from governance within a single platform. Every agent needs a verifiable identity. Policy enforcement must happen at the protocol level, not just within each participant's system. When a seller's pricing agent and a buyer's comparison agent interact, the reasoning chain across both must be capturable. Document agents handle invoices, returns, and compliance documentation flowing across the network.
Orchestrate provides agent identity, registration, and policy enforcement across ONDC's open network. Dastavez deploys document AI agents for invoicing, returns processing, and compliance documentation — with every agent action auditable against ONDC protocols and DPDP Act requirements.
Solution Package
Commerce Agent Trust Accelerator
A combined assessment, platform, and integration package for retailers deploying AI agents across pricing, recommendations, and customer service — with Consumer Protection Act 2019 and DPDP Act compliance built in.
What's Included
Audit agent readiness across pricing, recommendation, and customer service agents against Consumer Protection Act 2019 and DPDP Act requirements. Gap analysis with compliance roadmap.
Orchestrate + AgentOps configured for retail — pricing agents, recommendation agents, and customer service agents registered with autonomy boundaries and consumer impact scope.
Agent governance for the open network — verifiable agent identity, cross-platform policy enforcement, and reasoning capture across ONDC's decentralised commerce protocol.
Continuous monitoring for manipulative agent behaviours — surge pricing exploitation, deceptive recommendation patterns, and unfair ranking algorithms. Consumer Protection Act 2019 compliant.
When agents recommend, price, or decline — the customer gets a vernacular explanation. Reasoning surfaced through existing app interfaces in 22 languages.
Platform Stack
India's next 300 million shoppers speak vernacular.
The agents serving them need governance — not just good recommendations.
