Data & AI services for consumer businesses — advise, build, transform, run.
SCIKIQ teams set your data and AI strategy, build the platforms, transform distribution, stores, digital channels, pricing and supply, and run what we build — for FMCG makers, retail chains, D2C brands and quick commerce, and food service and travel retail in India. Our SCIKIQ Data Fabric, pre-built accelerators and a supervised digital workforce of AI agents are how we deliver it faster.
Consumer-business data and AI programmes stall because every one starts from zero.
SAP, distributor systems, POS, marketplace portals and GST each hold part of the picture, much of it as downloads and spreadsheets, and long implementations mean value arrives late — or not at all.
Patterns we found in delivered consumer-business work — not industry statistics.
1 · No governed data foundation
SAP, distributor systems, POS and marketplaces don't agree — product and outlet masters are duplicated and lineage is unknown.
2 · Services firms rebuild from scratch
Each project re-invents pipelines, models and controls, so timelines and fees grow.
3 · Software alone doesn't change the process
Point products solve one use case and leave integration, data and adoption to the business.
4 · AI pilots never reach production
Proofs of concept built without data, controls and an operating model stay on the shelf.
Consumer businesses are moving from dashboards to signals and agents that watch, quantify and route.
Instead of one more report, agents watch distributors, stores, apps and the market, put a rupee value on what they find and route it to an owner; people decide.
So what for a consumer business: the operating model moves from “a person downloads the reports” to “agents watch every channel, a person supervises many agents” — which makes grounding, autonomy limits, consent and audit trails the deciding capabilities, especially where claims, prices and customers are involved.
A platform that arrives with industry IP and the team to run it — so consumer businesses pay for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — delivered by teams who know FMCG distribution, retail operations, digital commerce and food service.
SCIKIQ Data Fabric
The governed foundation: the 4C method, 268 data sources, metadata, lineage, data quality and security.
Accelerators
The Growth Command Center, Enterprise Cockpit 360, Retail Insight Engine and Travel Retail Intelligence — plus cross-industry accelerators for reconciliation, close and FP&A.
Supervised digital workforce
AI agents that do the routine work end to end, with people approving exceptions and every step logged.
Nine service lines cover the lifecycle, organised the way consumer businesses buy them.
Select a stage on the wheel, or start from your role.
Set direction and the rules the data must meet.
Data & AI StrategyMaturity assessment, AI operating model, business case Data Governance, Privacy & ComplianceProduct & outlet masters, DPDP consent, GST lineageEngineer the platforms and the AI that runs on them.
Data Engineering & Platform ModernisationSAP, DMS, POS, marketplace & GST data on one platform AI & Agentic EngineeringMultilingual signals, grounded assistants, agent squadsChange how a consumer business sells, prices and stocks.
Sales & Distribution: GT, MT & KiranaSecondary sales, distributor health, claims Omnichannel, D2C & Quick CommerceStores, apps, availability, loyalty Revenue Growth ManagementPromotions, schemes, price-pack architecture Demand Planning & Supply ChainDemand sensing, twin, replenishmentKeep it healthy and improving after go-live.
Managed ServicesDataOps, MLOps & AgentOps under SLAsEvery service line runs on the same framework, accelerators and digital workforce. All services in detail →
A repeatable 4C method turns raw consumer-business data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect268 data sources: SAP / ERP, distributor systems, POS, marketplaces and quick commerce, GST, and news and reviews; batch and streaming.
- Step 2 · Week 2–4CurateStandardise, cleanse and enrich: MDM, de-duplication and data quality rules.
- Step 3 · Week 3–5ContextualizeMetadata catalog, critical data elements, multi-hop lineage, ownership and policy.
- Step 4 · Week 4–6ConsumeData products, APIs, BI, NLQ GenAI studio and the agents behind each accelerator.
Twelve capability layers give every consumer business one blueprint — and one way to measure progress.
The same layers we build and score in the maturity assessment. Hover a layer to see what it does.
Runs in your cloud tenancy or on-prem. Code, ontology and agents are handed over — you own what we build. Explore the blueprint →
MIT CISR found enterprises at stages 3–4 perform well above their industry average financially, while those at stages 1–2 perform below it. Source
Retail, consumer and cross-industry accelerators mean no service line starts from a blank page.
Pre-built, configurable starting points from delivered work, on the framework. Hover or tap a tile.
Retail & consumer accelerators CEO · Sales · CMO
Finance, GST & trade spend CFO
Operations & automation COO
Data Governance & Privacy CDO
26 agent roles across 8 retail & consumer domains — your people supervise the exceptions.
Agents work end to end across brand, supply, distribution, stores, digital channels, pricing, loyalty and finance. Policy decides what goes straight through; people approve the exceptions — and prices, claims, refunds and customer messages always stay with people.
Regulatory Radar · L0
Category Insight Analyst · L1
Twin Scenario Runner · L1
Replenishment Planner · L2
Distributor Health Scorer · L0
Beat Plan Advisor · L1
Distributor Claim Validator · L2
Outlet Health Monitor · L0
Food Cost & Wastage Watcher · L0
Listing Content Agent · L2
Review & Return Classifier · L1
Scheme Leakage Detector · L0
Price-Pack Scenario Agent · L1
Multilingual Service Agent · L2
Consent Guardian · L3
Settlement Matcher · L2
Invoice Matching Agent · L2
Finance 360 Assistant · L1
Click a bar to see the agents in it.
Agent designs from our AI & Agentic Engineering practice — not client results. The live control-room demo runs on banking sample data.
An agent does the legwork on every case; a person makes the call when policy says so.
Illustrative replay: a distributor's scheme claim doesn't match the scheme terms and the secondary sales behind it. The Distributor Claim Validator works it.
- 1Source systemsA distributor submits a scheme claim for a festive-season promotion; the claimed volume is higher than the secondary sales on record and one SKU isn't in the scheme.
- 2Data fabricThe claim, the distributor's DMS secondary sales and the GST e-invoices land within minutes, quality-checked, as governed data products.
- 3Metadata & ontologyAll three resolve to the same distributor, SKUs and outlets on the golden master record, with lineage back to each source.
- 4ContextThe scheme terms, eligible SKUs and states, the distributor's claim history and the claims procedure are assembled — only what this manager may see.
- 5AgentPlans and calls tools through the secure AI gateway:
get_claimget_scheme_termsget_secondary_salesget_einvoices; prices the mismatch in rupees and drafts a query with evidence and a confidence score. - 6PolicyKill switch, autonomy level and confidence decide the route — and claims are never approved by an agent, so this one goes to a person.
- 7Human gateThe area sales manager reviews the evidence on one screen and approves, edits or rejects the drafted query.
- 8ActionA governed, typed action sends the query to the distributor and holds the disputed amount — limited, idempotent and reversible.
- 9AuditEvery step, tool call and decision is recorded; outcomes feed the evaluation that decides whether autonomy could ever be raised.
Autonomy is set per agent and raised only on evidence — inside hard guardrails.
Confidence threshold
Straight-through only if confidence, amount limit and evidence checks pass.
Amount limits
Claims, refunds, price changes and settlement adjustments above the limit always go to a person.
Named human owner
Approves, edits or rejects every exception; prices, claims and customer messages always need a person.
Everything logged
Every plan, tool call and decision; overrides feed evaluation. Personal data masked in prompts; DPDP consent checked before any customer use.
Kill switch
One control halts every agent at once.
What could a supervised agent squad free up in your business?
Move the sliders to your own volumes. Agents work the cases; people review only the exceptions.
Estimate only, not a quote or a SCIKIQ 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, shown in lakh (L) and crore (Cr). Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Faster than services alone, a better fit than software alone.
How the SCIKIQ model compares with the usual ways consumer businesses deliver data, AI and agentic automation.
| Criterion | Big-4 / SIservices only | Point productssoftware only | In-house build | SCIKIQplatform + accelerators + delivery |
|---|---|---|---|---|
| Time to production value | 6–12 months | 3–6 months + integration | 12–18 months | 30–45 days |
| Retail & consumer data models & domain depth | Generic methods | One use case | Build | Pre-built |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | SCIKIQ Data Fabric |
| Ready-made accelerators | Varies | Single product | None | Retail & consumer + cross-industry |
| Supervised AI agents in operations | Pilots / PoCs | Copilot features | Build & govern | Agent squads with guardrails & AgentOps |
| Process change & adoption | Yes | Left to the business | Partial | Yes |
| Run & continuous improvement | Separate contract | Product support | Internal team | Managed services |
| Total cost of ownership | High | Medium–High | Very High | Low |
Big-4 / SI (6–12 months), point products (3–6 months + integration) and in-house builds (12–18 months) are compared on larger screens.
Four phases, each ending with something you keep — and three ways to buy it.
Discovery & Planning
You receive a maturity assessment and target blueprint, with a prioritised roadmap.
Gate: pilot scope signed offAnalysis & Design
Target-state design and ontology, mapped to your controls; agent autonomy agreed with sales, supply chain, e-commerce and finance.
Gate: design authorityBuild & Deploy
Landing zone as code, pipelines, accelerators and agents configured and tested on real data.
Gate: go-live readinessSupport & Embed
Runbooks, evaluation suites and knowledge transfer to a trained team.
Gate: handover sign-offStaff Augmentation
Architects, data and AI engineers embedded in your teams, under your delivery lead.
Project Delivery
Outcome-based delivery of an accelerator or platform build by a SCIKIQ pod, with phase gates.
Managed Services
We run and improve the platform, models and agents — DataOps, MLOps and AgentOps under SLAs.
A 30–45 day accelerator pilot proves value on one use case before you commit to scale.
Fixed scope, fixed timeline, agreed success criteria — and a scale-up business case at the end.
One accelerator & its agent squad
Typically secondary-sales visibility and claim validation for one state, the growth command center for one brand, or a Finance 360 for one outlet cluster, with agents starting at “act with approval”.
2–3 source systems
e.g. SAP, the DMS and the scheme master — or POS, inventory and procurement for a set of outlets.
One business unit
e.g. one state, one brand or one cluster of stores or outlets, with a named owner and SMEs for rules and UAT.
SCIKIQ pod
Engagement lead, data engineer, domain SME, AI / agent engineer.
Success-criteria targets are agreed in week 1.
Three steps take us from this conversation to value in production.
Start small with one accelerator, prove it, then scale on the same framework.
Scoping workshop
Walk through your data landscape and pain points, pick the pilot use case and agree success criteria.
1–2 weeksAccelerator pilot
Deploy one accelerator and its agent squad on the framework against live data, and measure the result.
30–45 daysScale & embed
Roll out across business units and further accelerators through project delivery or managed services.
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