Agents that do the routine work across a consumer business — scoring brand and regulatory signals, reconciling secondary sales and GST, validating distributor claims, writing store briefings, keeping listings right on every app — on the governed data fabric. Policy decides what goes straight through; a named person approves everything else, and no price, claim, refund or customer message is decided by an agent alone.
These are agent designs from our AI & Agentic Engineering practice, mapped to the retail and consumer value chain in India — FMCG makers, retail chains, D2C and quick commerce, food service and travel retail. Agents linked to an accelerator mirror platforms we have built; the live agent demo runs on banking sample data under the same controls.
Brand and category teams see what India is saying about their brands — in English and Indian languages — joined to the sales it could move.
Ingests news and social mentions in English and Hindi (extensible to Tamil, Telugu and more) and scores each for sentiment, brand, region, impact and a one-line "so what".
See the accelerator →Watches for packaging, labelling, health-claim and food-safety developments and flags the SKUs and states they touch.
See the accelerator →Explains share, distribution and price movements by state and channel from audits, panels and internal sales, and drafts the monthly category review.
Planners get a demand view that includes the monsoon, the festive calendar and the market — and scenarios that are backtested before anyone trusts them.
Updates the short-term forecast by SKU, depot and state from secondary sales, weather, festivals and market signals.
Runs what-if shocks on the demand–supply twin — a price change, a plant outage, a monsoon delay — and backtests the model against the past before showing results.
See the accelerator →Proposes depot and stockist replenishment within agreed stock norms and flags stock-out risks before the season.
Sales teams see past the distributor — secondary sales, outlet gaps and claims — without waiting for month-end files.
Normalises distributor DMS feeds and reconciles primary, secondary and tertiary sales within tolerance, explaining every break.
Scores each distributor on stock, ageing, fill rate, claims and credit, and flags the ones slipping.
Recommends next-best SKUs and beat changes for each salesperson from outlet history and gaps.
Matches each scheme claim to the scheme terms, the secondary sales behind it and the GST invoices, prices any mismatch and drafts the query.
Store, outlet and kitchen managers get a short, ranked briefing — what changed, why, and what to do — instead of thousands of alerts.
Writes the daily store and region briefing from POS, footfall and the retail calendar, ranking the few changes that matter.
See the accelerator →Scores outlet health and revenue leakage for travel-retail and lounge outlets against flight-driven demand and partner contracts.
See the accelerator →Tracks food cost, wastage and item-level profitability by kitchen and outlet and flags where they drift.
See the accelerator →E-commerce teams keep every SKU listed, priced and in stock across apps, marketplaces, ONDC and dark stores.
Tracks availability and share of shelf by SKU, city and platform, including quick-commerce dark stores.
Enriches product content from the PIM and drafts platform-specific listings and fixes.
Classifies reviews and return reasons in multiple languages and routes product or packaging issues to the owner.
Revenue managers get the evidence behind every scheme and price move before it is repeated.
Post-evaluates each promotion for lift, cannibalisation and ROI by state and channel.
Finds scheme and price leakage across claims, invoices and price lists and quantifies it in rupees.
Models price-pack architecture options — grammage, MRP points, packs — by state and channel when input costs move.
Marketing and service teams act on one consented customer view, in the customer's own language.
Recommends the next offer for loyalty members and app users from purchase history, with DPDP consent and purpose checked first.
Classifies service requests from WhatsApp, email and calls in Indian languages and drafts replies.
Checks every campaign audience against recorded consent and purpose and blocks non-compliant records.
Controllers close faster, with GST, settlements and trade-spend accruals reconciled and every revenue lens labelled.
Reconciles GST input credit, e-invoices and e-way bills against the books and suppliers' filings within tolerance, explaining every break.
Matches marketplace, quick-commerce and distributor settlements to orders, fees and invoices.
Extracts vendor invoices from PDFs and scans and matches them to purchase orders and vendor statements, explaining each exception.
See the accelerator →Answers P&L questions in plain language with guarded text-to-SQL over the finance fact table and drafts the monthly commentary.
See the accelerator →Policy decides what goes straight through; people approve everything else; a kill switch halts all agents. The live demo runs on banking sample data — the same controls apply to every Retail & Consumer squad.