Input costs reset price-pack architecture every quarter, quick commerce and ONDC rewrite the route to market, secondary sales disappear into millions of kirana stores, and trade spend is the biggest line with the least evidence. The answer runs across the whole value chain — from how a brand is read to how a claim is settled under GST. DaasLabs is the data and AI services team that helps FMCG makers, retail chains, D2C brands and travel retailers make that shift, one value-chain domain at a time.
Consumer businesses organised around systems — an ERP, a distributor system, a POS, a set of marketplace portals — now compete on decisions made across all of them. The ones pulling ahead treat data as a product and AI as an operating capability, not a set of pilots. These are the pressures they are responding to.
Packaging, commodities and freight move every month, but the ₹10 and ₹20 price points don't. Grammage, packs and schemes become the margin lever — decided with too little evidence.
Data & AI: price-pack architecture scenarios, and SKU-level margin by state and channel.
Dark stores, marketplaces, ONDC and own apps add channels with their own prices, content and stock, while general trade still carries most of the volume. Assortment and availability now have to be managed by city and by hour.
Data & AI: one product record syndicated everywhere, and availability tracked across apps and dark stores.
Goods vanish into distributors, stockists and kirana stores. Secondary sales arrive late in a dozen formats, outlet masters are duplicated, and beat plans are drawn from habit.
Data & AI: DMS-fed secondary-sales visibility, distributor health scoring and outlet-level next-best-SKU.
Schemes multiply across states, channels and festive windows, distributor claims are checked by hand, and promotions are repeated because nobody measured the last one.
Data & AI: promotion post-evaluation, claim validation and scheme-leakage detection.
Demand swings with the monsoon, festivals and weddings, preferences change state by state, and shoppers talk about brands in Hindi, Tamil, Telugu, Bengali and more — not just English.
Data & AI: state-level demand and sentiment maps, and Indian-language signal scoring.
GST e-invoicing and e-way bills touch every movement, packaging and labelling rules touch every SKU, and the Digital Personal Data Protection Act 2023 sets consent rules for every campaign.
Data & AI: GST reconciliation, consent-aware customer data and labelling checks with an audit trail.
SAP, the DMS, POS, marketplace portals and agency data each tell part of the story, and alerts multiply until nobody reads them. AI that acts on this has to be grounded, governed and explainable.
Data & AI: one governed data fabric, ranked alerts, and agents with autonomy limits and audit trails.
The response isn't one platform or one model — it is data and AI applied domain by domain, from the brand to the kirana shelf to the GST return, on a shared, governed foundation.
See where, domain by domain ↓Plan and source, sell through every channel, serve the customer, and the group functions underneath — the same chain for an FMCG maker, a retail chain, a D2C brand or a travel retailer, with different emphasis. Select a domain to see the data it runs on, the AI opportunities, and how DaasLabs adds value there.
Consumers switch between kirana, modern trade, apps and quick commerce in a single week, talk about brands in a dozen languages, and change what they buy with the monsoon, the festive calendar and the wedding season. Category teams still read it all from quarterly syndicated data.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Packaging, sugar, edible oil and fuel costs move every month, plants and co-packers run hot before Diwali and summer, and depots sit on the wrong stock while stockists run out.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Most volume still moves through distributors and stockists to millions of kirana stores. Once goods leave the depot, secondary and tertiary sales arrive late, beat plans are guessed, and every distributor reports in its own format.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Chains run hundreds of stores across cities, malls and airports, each with its own calendar. Store managers drown in alerts, footfall and conversion are read a week late, and airport outlets live by flight schedules and partner contracts.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Quick-commerce dark stores, marketplaces, ONDC and the brand's own app all sell the same SKU at different prices, with different content and stock. Listings drift, availability gaps cost sales by the hour, and every platform reports differently.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Price-pack architecture changes with every input-cost swing, schemes multiply across channels and states, and trade spend is one of the biggest lines on the P&L with the least evidence behind it.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Loyalty members, app users and walk-in shoppers look like different people in every system. Service requests arrive in many languages, and consent under the DPDP Act has to be honoured in every campaign.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
GST e-invoicing, e-way bills and input-credit reconciliation run on every transaction, distributor claims settle months late, and filed revenue, system turnover and brand turnover tell different stories to the board.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Opportunities and approaches are described qualitatively. Shaded chips are DaasLabs service lines; the others open accelerators. See every service line mapped to these eight domains
Nine service lines that advise, build, transform and run — delivered on three pieces of DaasLabs IP, so consumer businesses start from working components rather than a blank page.
Each agent runs the same loop — scan, reason, quantify, route. The arithmetic happens in SQL; the model judges, prioritises and recommends; a named owner decides. Nothing happens off the record.
The agent matches a distributor's scheme claim to the scheme terms, the secondary sales behind it and the GST invoices, prices the mismatch and drafts the query. The area sales manager approves, edits or rejects it.
See the retail & consumer agent squadsDeterministic scanners run over SAP, DMS, POS, marketplace and GST data on the governed Data Fabric and surface signals with hard numbers.
Each agent has an autonomy level — observe, suggest or act within limits. Anything outside policy waits for a person.
Every finding carries a rupee impact, a confidence, a named owner and an SLA — accept, assign, escalate or dismiss.
Every plan, tool call, guardrail check and decision is logged; overrides feed evaluation, and a kill switch halts all agents.
Platforms we have built for FMCG, retail and travel-retail businesses, generalised into configurable starting points — plus cross-industry accelerators our teams configure to consumer data, rules and controls.
Built with Indian FMCG and apparel makers, retail and fashion-commerce groups and an airport hospitality operator; each runs on the Data Fabric Framework.
Ingests Indian news in English and Hindi, scores every mention with an LLM for sentiment, brand, region and impact, and runs a signal engine — watchlist alerts, negative-spike detection and a regulatory radar — with an India signal map, a demand–supply twin and what-if scenarios.
CEO, CFO and board views over brand, customer, supplier, site, cash and finance 360s, a control tower, scenario planner and grounded AI chat — showing filed revenue and system turnover side by side, each labelled.
An executive one-pager over a multi-banner store network with a full retail calendar — festivals and sale events included — that generates the insight, ranks alerts and keeps only the meaningful ones.
Cross-industry accelerators below are configured to retail and consumer data in an engagement; their demo pages run on banking sample data.
Delivered outcomes from a food-service group, and platforms we have built across FMCG, retail and travel retail. Client names are withheld; the first three figures are client results, the last is a count from a build. Read the case study.
Real Indian news in English and Hindi, scored by an LLM, feeding watchlist alerts, negative-spike detection, a regulatory radar and an India signal map.
Brand, customer, supplier, cash and finance 360s with filed revenue and system turnover shown side by side and clearly labelled.
An executive one-pager over 400+ stores with a full retail calendar — festive and sale events included — and alerts filtered to the meaningful ones.
Airport, partner and outlet drill-downs, outlet health scores, revenue-leakage and contract analytics, flight-based load prediction and a board deck generated on demand.
Inventory, procurement and operations joined in one Finance 360, with real-time P&L and item- and location-level profitability for frontline managers and controllers.
Six engagements — for a food-service and catering group, an Indian FMCG beverages maker, FMCG and apparel groups, multi-format retail and distribution groups, an airport hospitality operator and a QSR franchise. Different businesses, the same pattern: one governed data model, signals scored by AI and joined to the money, and every answer showing where it came from.
The first three figures are delivered client results. The other platforms run on modelled or sample data, so for them we show capabilities and counts from the build, not client results.
The challenge
What the market said about the brands reached leadership too late.
What we built
A live early-warning system grounded in the company's own data.
The challenge
Leadership saw the business one report at a time.
What we built
A persona-driven command centre on one enterprise model.
The challenge
Too many alerts, too little insight.
What we built
An executive one-pager that writes the insight, and 360s behind it.
The challenge
Airport outlets live by departure-driven footfall and partner contracts.
What we built
Network-to-outlet intelligence with an AI advisor.
The challenge
Rapid growth created operational blind spots.
What we built
One view of what makes money, and why.
The challenge
Vendor invoices matched to orders and statements by hand.
What we built
An agentic pipeline from document folder to matched statement.
Every metric on a map or a briefing states whether it comes from internal sales data or from scored external signals.
A story in Hindi about a packaging rule is fetched, scored and placed on the map — then joined to the brand's sales in that state:
{
"language": "hi",
"sentiment": "negative",
"category": "regulatory",
"brands": ["<brand>"],
"region": "<state>",
"impact": "high",
"so_what": "Check pack labelling for affected SKUs; brief the state sales head."
}
Illustrative record in the shape the enrichment step produces.
Two revenue lenses. An FMCG brand's filed entity revenue and its system turnover (including franchise bottlers and co-packers) are both real but measure different things. The cockpit shows both, labelled, and never blends them.
Verified maps. The India map was built from boundary data checked for the full territorial extent, and each metric states which source it draws on.
Technology stack
Enter your own volumes. The estimate compares today's manual handling with agents working the cases and people reviewing only the exceptions.
Estimate only, not a quote or a DaasLabs result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − STP share) × review minutes ÷ 60. Hours saved = manual − supervised. FTE = hours saved ÷ 150. Cost saved = hours saved × cost per hour (× 12 for a year), shown in lakh (L) and crore (Cr). Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Tell us the problem — secondary sales you can't see, trade spend you can't prove, listings that drift across apps, store alerts nobody reads, food cost and wastage you see too late, GST breaks at month-end. We'll propose an assessment or a 30-45 day pilot, delivered by DaasLabs teams on our framework and accelerators.