Services

Nine services, mapped to the retail & consumer value chain.

Consumer businesses don't buy "data and AI" — they buy visibility to the kirana shelf, trade spend that earns its keep, SKUs that are listed and in stock on every app, stores that act on the right alerts, and a clean GST close. Below, each SCIKIQ service line is mapped to the domains where it does that work — for FMCG makers, retail chains, D2C brands and quick commerce, and food service and travel retail in India — with the use cases, the KPIs it moves and the accelerators behind it.

See the value-chain map How we add value, by domain Find your service by role
Chapter 2 · Services on the value chain

Where each service line works on the retail & consumer value chain

Read across a row to see where a service line leads and where it supports. Read down a column to see the team a consumer business gets in that domain. Select any domain to see use cases, the KPIs we help move and the working accelerators.

9
Service lines
8
Value-chain domains
16
Lead roles across the map
34
Supporting roles across the map
Leads the work Supports Hover a dot for detail · select a domain to explore it
01 · Advise
Data & AI StrategyStrategy
Data Governance, Privacy & ComplianceGovernance
02 · Build
Data Engineering & Platform ModernisationData platform
AI & Agentic EngineeringAI & agents
03 · Transform
Sales & Distribution: GT, MT & KiranaGT & MT sales
Omnichannel, D2C & Quick CommerceOmnichannel
Revenue Growth ManagementRGM
Demand Planning & Supply ChainSupply chain
04 · Run
Managed Services: DataOps, MLOps & AgentOpsManaged
Service lines engaged 6 6 8 6 8 5 5 6

The mapping shows where each service line typically leads or supports; every engagement is scoped to the business. The value chain itself is explained on the overview.

How we add value

Domain by domain: from data to a measurable outcome

Each domain follows the same path — source data, a governed data product, AI and agents, an outcome the business measures. KPIs are the measures we help you move and track; we agree targets with you, we don't promise them in advance.

Domain 1 of 8

Brand, category & consumer insight

Consumers switch channels weekly, talk about brands in a dozen languages and change what they buy with the season, while category teams read it all from quarterly syndicated data.

Data
Audits, social, news, reviews & panels
Data product
Brand & consumer signal data product
AI & agents
Multilingual signal-scoring agents
Outcome
Earlier, better-informed brand calls

Retail & consumer use cases

  • Brand and competitor signals scored from English and Indian-language news
  • Regulatory and activist radar for packaging and claims
  • State-by-state sentiment and coverage maps
  • Festive, monsoon and season-driven demand drivers

KPIs we help you move

Share of voiceNet sentiment by stateTime from signal to actionBrand health score

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Growth Command Center
Domain 2 of 8

Demand planning, sourcing & supply

Input costs move monthly, plants and co-packers run hot before the season, and depots hold the wrong stock while stockists run out.

Data
ERP, depot, stockist & commodity data
Data product
Demand & inventory data product
AI & agents
Demand-sensing & twin agents
Outcome
Right stock, right depot, right week

Retail & consumer use cases

  • Demand sensing by SKU, depot and state
  • Demand–supply twin with stress tests and backtests
  • Inventory and replenishment optimisation
  • Procure-to-pay exception handling

KPIs we help you move

Forecast accuracyFill rateDays of inventoryStock-outs at stockists

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Growth Command Center Control Tower
Domain 3 of 8

Distribution: general trade, modern trade & kirana

Goods vanish into distributors, stockists and kirana stores; secondary sales arrive late, outlet masters are duplicated and beat plans are guessed.

Data
DMS, SFA, outlet census & MT sell-out
Data product
Secondary-sales & outlet data product
AI & agents
Distributor-health & next-best-SKU agents
Outcome
Visibility to the shelf

Retail & consumer use cases

  • Secondary-sales visibility and distributor health scoring
  • Outlet-level next-best-SKU and beat plans
  • Retailer-master de-duplication
  • Distributor claim validation

KPIs we help you move

Numeric distributionLines per callDistributor claim cycle timeSecondary-sales visibility

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Enterprise Cockpit 360 CATALYST
Domain 4 of 8

Stores, omnichannel & travel retail

Hundreds of stores across cities, malls and airports, alerts nobody reads, and footfall and conversion read a week late.

Data
POS, footfall, calendar & flight data
Data product
Store & outlet data product
AI & agents
Insight-ranking & briefing agents
Outcome
Fewer, better store decisions

Retail & consumer use cases

  • Automated store insights that rank what changed and why
  • Alert prioritisation for store managers
  • Flight- and event-driven footfall and staffing forecasts
  • Outlet health, revenue-leakage and contract analytics

KPIs we help you move

Sales per sq ftConversion rateAlerts acted onOutlet health score

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Retail Insight Engine Travel Retail Intelligence
Domain 5 of 8

D2C, marketplaces & quick commerce

Dark stores, marketplaces, ONDC and own apps sell the same SKU at different prices with different content and stock.

Data
Platform reports, app orders & PIM
Data product
Channel & product data product
AI & agents
Availability, content & review agents
Outcome
Always listed, always in stock

Retail & consumer use cases

  • Availability and share-of-shelf across apps and dark stores
  • Product content enriched and syndicated to every channel
  • Price and discount monitoring
  • Review and return-reason classification

KPIs we help you move

On-shelf availability by cityContent complianceReturn rateDigital share of sales

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Product Information Management Fashion Commerce Command Center
Domain 6 of 8

Pricing, promotions & trade spend

Price-pack architecture resets with input costs, schemes multiply across states and channels, and trade spend has the least evidence of any big P&L line.

Data
Price lists, schemes, claims & margins
Data product
Trade-spend data product
AI & agents
Promotion-evaluation & leakage agents
Outcome
Trade spend that earns its keep

Retail & consumer use cases

  • Promotion post-evaluation: lift, cannibalisation, ROI
  • Scheme and claim leakage detection
  • Price-pack scenarios by state and channel
  • Competitor price monitoring

KPIs we help you move

Trade-spend ROIScheme leakage recoveredPrice realisationGross margin by SKU

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Revenue Assurance COMPASS
Domain 7 of 8

Customer, loyalty & service

Loyalty, app and walk-in customers look like different people, service requests arrive in many languages, and consent must be honoured in every campaign.

Data
Loyalty, CRM, app & contact data
Data product
Consented customer data product
AI & agents
Next-best-offer & service agents
Outcome
Relevant, consented engagement

Retail & consumer use cases

  • Customer 360 and next-best-offer with consent enforced
  • Membership-conversion and churn signals
  • Multilingual service classification and replies
  • City- and language-aware recommendations

KPIs we help you move

Repeat purchase rateMembership conversionFirst-contact resolutionConsent coverage

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working CATALYST City & Language Marketplace
Domain 8 of 8

Finance, GST & compliance

GST e-invoicing and e-way bills touch every movement, distributor and marketplace settlements arrive late, and different revenue lenses tell different stories.

Data
GL, GST returns, settlements & accruals
Data product
Governed finance data product
AI & agents
Reconciliation & commentary agents
Outcome
A faster, cleaner close

Retail & consumer use cases

  • GST input-credit and e-invoice reconciliation
  • Distributor and marketplace settlement matching
  • Brand, channel and state P&L with AI commentary
  • Labelling and packaging-compliance checks

KPIs we help you move

Days to closeGST input-credit mismatchesUnreconciled settlementsTrade-spend accrual accuracy

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working CLARION COMPASS NARRATOR
Find your service

Start from your role

Each service line has a clear owner on the client side. Pick yours to jump to the services most teams like yours start with.

CEO
Chief Executive & business heads
Sales
Head of sales, distribution & modern trade
CMO / E-com
Marketing, e-commerce & D2C heads
Supply chain
Head of supply chain, planning & procurement
CFO
Chief Financial Officer
CIO / CDO
Chief Information & Chief Data Officer
01

Advise

Set direction, make the business case and put the rules in place that data and AI must meet.

Advise · Service line

Data & AI Strategy

ForCEOCIO / CDOCFO
On the value chainBrand & consumerDemand & supplyGT, MT & kiranaStores & omnichannelD2C & quick commercePricing & trade spendLoyalty & serviceFinance & GST

The client problem

Pilots multiply across brands, channels and regions, but few reach production. There is no shared, evidence-based view of where the business stands on data and AI, which use cases pay back, or who owns them.

Outcomes

  • A maturity baseline across 12 capability layers and five stages, scored on evidence rather than opinion
  • A prioritised roadmap of use cases across brand, distribution, stores, digital and finance, with a business case for each
  • An AI operating model that works across brands, states and channels

What we do

  • Data & AI maturity assessment
  • AI strategy & use-case prioritisation
  • Brand, channel and state P&L analytics
  • AI operating model & centre of excellence design

Typical engagements

  • AssessmentMaturity assessment and roadmap
  • Pilot · 30-45 daysProve the top-ranked use case on your data
  • BuildRoadmap delivered through our Build and Transform services
  • Managed runValue tracking and roadmap refresh

Delivered with

Maturity assessment Enterprise Cockpit 360 Value calculator
Advise · Service line

Data Governance, Privacy & Compliance

ForCDODPO / legalCFO
On the value chainGT, MT & kiranaD2C & quick commerceLoyalty & serviceFinance & GST

The client problem

Product, outlet, distributor and customer masters differ by system and state. The DPDP Act 2023 sets consent rules for every campaign, GST touches every invoice, and labelling rules touch every SKU — and AI adds a new layer to govern.

Outcomes

  • Golden records for products, outlets, distributors and customers
  • Consent and purpose enforced in the data and the workflow under the DPDP Act
  • AI and agent governance: policy, approvals, evaluation and a kill switch

What we do

  • Product, outlet & retailer master-data management
  • DPDP consent & purpose management
  • GST, e-invoice & e-way bill data lineage
  • Labelling & packaging-compliance data
  • Metadata catalogue & AI & agent governance

Typical engagements

  • AssessmentMaster-data, consent and compliance-data gap review
  • Pilot · 30-45 daysGolden record and DQ rules for one domain
  • BuildMDM, consent and catalogue across brands and channels
  • Managed runOngoing DQ monitoring and stewardship

Delivered with

Data Governance Hub SCIKIQ Data Fabric Agent governance
02

Build

Engineer the data platforms and the AI that runs on them, with governance built in.

Build · Service line

Data Engineering & Platform Modernisation

ForCIOCDO
On the value chainBrand & consumerDemand & supplyGT, MT & kiranaStores & omnichannelD2C & quick commercePricing & trade spendLoyalty & serviceFinance & GST

The client problem

SAP, distributor systems, POS, marketplace and quick-commerce portals, agency data and GST returns each hold part of the picture, in different formats. Every new report or model starts with another download.

Outcomes

  • One governed, cloud-agnostic platform for consumer data on Azure, AWS or hybrid
  • Pipelines from SAP, DMS, POS, marketplaces and GST with lineage from source
  • An enterprise model and ontology for brands, outlets, stores and partners

What we do

  • Pipelines, batch & streaming
  • SAP, DMS, POS & marketplace connectors
  • Retail & FMCG data models, ontology & knowledge graph
  • Lakehouse / warehouse on Azure or AWS
  • Infrastructure as Code & legacy modernisation

Typical engagements

  • AssessmentData estate and target-architecture review
  • Pilot · 30-45 daysConnect and curate priority sources end to end
  • BuildPlatform build in sprints, tested with real data
  • Managed runDataOps under agreed SLAs

Delivered with

SCIKIQ Data Fabric 268 data sources Enterprise Cockpit 360
Build · Service line

AI & Agentic Engineering

ForCIOCOOCMO
On the value chainBrand & consumerDemand & supplyGT, MT & kiranaStores & omnichannelD2C & quick commercePricing & trade spendLoyalty & service

The client problem

GenAI pilots stall at the controls review: no grounding, no autonomy limits, no named owner for the exceptions — and models that only read English miss most of what India says about a brand.

Outcomes

  • Supervised agent squads in production, with autonomy and guardrails set per agent
  • GenAI that reads Indian-language news, reviews and service requests and scores them consistently
  • People review only the exceptions, with the agent's draft and evidence in front of them

What we do

  • Agent design: roles, squads & autonomy levels
  • Multilingual signal scoring & classification
  • Grounded assistants over enterprise data
  • Demand twins, scenarios & backtests
  • Evaluation & guardrails: policy, limits, kill switch

Typical engagements

  • AssessmentAgent opportunity and controls review
  • Pilot · 30-45 daysOne agent squad working real cases under your controls
  • BuildSquads integrated with your systems and scaled
  • Managed runAgentOps: monitoring, overrides, drift

Delivered with

Growth Command Center Run an agent live Governance & controls
03

Transform

Domain practices that change how a consumer business sells, prices and stocks, end to end, with our accelerators as the starting point.

Transform · Service line

Sales & Distribution: GT, MT & Kirana

ForHead of salesDistributionModern trade
On the value chainDemand & supplyGT, MT & kiranaPricing & trade spendFinance & GST

The client problem

Volume moves through distributors and stockists to millions of kirana stores, but secondary and tertiary sales arrive late in a dozen formats. Beat plans are drawn from habit and distributor claims are checked by hand.

Outcomes

  • Primary, secondary and tertiary sales on one governed model
  • Distributor health scores and outlet-level next-best-SKU in the sales team's hands
  • Distributor claims validated against schemes, sales and GST invoices

What we do

  • DMS & secondary-sales integration
  • Distributor health scoring
  • Outlet census, retailer master & beat planning
  • Distributor claim validation
  • Modern-trade sell-out analytics

Typical engagements

  • AssessmentRoute-to-market data and distributor review
  • Pilot · 30-45 daysSecondary-sales view for one state or region
  • BuildNational DMS integration and distributor 360s
  • Managed runDistributor-data operations under SLA

Delivered with

Enterprise Cockpit 360 CATALYST Revenue Assurance
Transform · Service line

Omnichannel, D2C & Quick Commerce

ForHead of e-commerceRetail operationsCMO
On the value chainBrand & consumerStores & omnichannelD2C & quick commerceLoyalty & service

The client problem

Stores, own apps, marketplaces, ONDC and quick-commerce dark stores sell the same SKU at different prices with different content and stock. Store managers drown in alerts and loyalty data sits apart from everything else.

Outcomes

  • Availability, content and price tracked by SKU, city and platform
  • Store and outlet briefings that rank what changed and why
  • One consented customer view across stores, apps and partners

What we do

  • Marketplace & quick-commerce analytics
  • Product information & syndication
  • Store analytics with automated insight
  • Customer 360, loyalty & personalisation
  • Travel-retail & outlet intelligence

Typical engagements

  • AssessmentChannel data and customer-data review
  • Pilot · 30-45 daysOne channel or one store cluster end to end
  • BuildOmnichannel data products and store briefings
  • Managed runChannel-data and model operations

Delivered with

Retail Insight Engine Travel Retail Intelligence Product Information Management
Transform · Service line

Revenue Growth Management

ForCFOHead of RGMCategory heads
On the value chainBrand & consumerGT, MT & kiranaStores & omnichannelD2C & quick commercePricing & trade spendFinance & GST

The client problem

Input costs reset price-pack architecture, schemes multiply across states and festive windows, and trade spend — one of the largest lines on the P&L — has the least evidence behind it.

Outcomes

  • Every promotion evaluated on lift, cannibalisation and ROI before it is repeated
  • Scheme and claim leakage detected and recovered
  • Price-pack scenarios by state and channel, ready before the next cost change

What we do

  • Promotion post-evaluation
  • Trade-spend analytics & leakage detection
  • Price-pack architecture & pricing scenarios
  • Competitor price monitoring
  • Brand and state P&L

Typical engagements

  • AssessmentTrade-spend and pricing diagnostic
  • Pilot · 30-45 daysPost-evaluate one season of promotions
  • BuildRGM data products and scenario tools
  • Managed runMonthly RGM analytics under SLA

Delivered with

Revenue Assurance COMPASS NARRATOR
Transform · Service line

Demand Planning & Supply Chain

ForHead of supply chainCOOProcurement
On the value chainBrand & consumerDemand & supplyGT, MT & kiranaStores & omnichannelD2C & quick commerce

The client problem

Demand swings with the monsoon, festivals and weddings, plants and co-packers run hot before the season, and depots sit on the wrong stock while stockists run out — while market and regulatory news arrives too late to act on.

Outcomes

  • Demand sensing by SKU, depot and state, including external signals
  • Scenarios backtested against the past before anyone trusts them
  • Inventory and replenishment optimised across depots and stockists

What we do

  • Demand sensing & forecasting
  • Demand–supply twin & scenarios
  • Inventory & replenishment optimisation
  • Market & regulatory early-warning
  • Procure-to-pay exception handling

Typical engagements

  • AssessmentPlanning and inventory diagnostic
  • Pilot · 30-45 daysDemand sensing for one category or region
  • BuildTwin, planning data products and control tower
  • Managed runForecast and twin operations under SLA

Delivered with

Growth Command Center Control Tower RPA Automation
04

Run

Keep platforms, models and agents healthy and improving after go-live.

Run · Service line

Managed Services: DataOps, MLOps & AgentOps

ForCIOCOO
On the value chainDemand & supplyGT, MT & kiranaD2C & quick commerceFinance & GST

The client problem

After go-live, portal formats change, distributor feeds break before month-end, festive peaks stress every pipeline, and agents need someone watching overrides, limits and evaluation results.

Outcomes

  • Platforms, pipelines, models and agents run under agreed SLAs, festive peaks included
  • Continuous improvement driven by override and evaluation data
  • Your teams freed from L2/L3 support

What we do

  • Run & L2/L3 support
  • DataOps, including DMS, marketplace and GST feeds
  • MLOps
  • AgentOps: logs, overrides, drift, kill switch
  • Continuous improvement

Typical engagements

  • AssessmentRun-readiness and support model review
  • Pilot · 30-45 daysHypercare for a newly live capability
  • BuildMonitoring, runbooks and SLAs
  • Managed runOngoing service under agreed SLAs

Delivered with

Governance & controls SCIKIQ Data Fabric
Our assets

What makes our services faster

Every engagement starts from SCIKIQ IP rather than a blank page. These assets are how we deliver — they come with the service.

1
SCIKIQ Data Fabric

The governed foundation every engagement runs on: the 4C method (Connect, Curate, Contextualize, Consume), 268 data sources, and governance, lineage, data quality, PII detection and masking, an AI/agent layer and security built in — cloud-agnostic on Azure, AWS or hybrid.

Explore the framework
2
Accelerators

Retail & consumer accelerators from platforms we have built — the Growth Command Center, the Enterprise Cockpit 360, the Retail Insight Engine and Travel Retail Intelligence — plus cross-industry accelerators such as CLARION, COMPASS and NARRATOR, tailored to your rules, data and controls.

Accelerators by service line
3
Supervised digital workforce

AI agents that plan, call tools and gather evidence on the governed data. Policy decides what goes straight through; people approve everything else; every step is logged and a kill switch halts all agents.

How it works Watch one work
How we engage

From a business outcome to measured value

Every engagement starts from the business outcome, not the technology. We frame it through the same business lens each time, then deliver in five phase-gated stages. Most consumer businesses start with a discovery and value case for one value-chain domain, then scale to the next on the same foundation.

How we frame an engagement

  1. 1Business outcome & KPI
  2. 2Value-chain domain
  3. 3Decisions
  4. 4Data
  5. 5AI & agents
  6. 6Governance & adoption
  7. 7Measured value

Delivery phases

Phase 1
Discover & value case

Outcome, domain and KPIs agreed; data and process assessment; baseline and business case.

Phase 2
Design

Decisions, data products, models, agents and controls designed for the chosen domain.

Phase 3
Build & integrate

Sprint delivery on the SCIKIQ Data Fabric, integrated with SAP, distributor systems, POS, marketplace portals and GST; tested on real data.

Phase 4
Deploy & adopt

Go-live, people and process change, agent autonomy limits agreed with sales, supply chain, e-commerce and finance; value tracked against the baseline.

Phase 5
Run & scale

Managed service — DataOps, MLOps and AgentOps under SLA — and the next domain on the same foundation.

Each phase ends with a gate signed off by your steering group; a pilot in one domain typically reaches a production-ready capability in 30-45 days. The SCIKIQ Data Fabric we build on

Engagement models

Staff Augmentation

Data engineers, architects, analysts and AI specialists embedded in your teams, under your delivery lead.

Project Delivery

Outcome-based delivery of an accelerator or platform build, owned end-to-end by SCIKIQ with phase gates.

Managed Services

We run and improve your data platforms, models and agents — DataOps, MLOps, AgentOps and support under agreed SLAs.

Weekly status Bi-weekly steering Phase-gated sign-off 30-45 day pilot → scale

Start with the outcome you need

Pick a domain and a KPI. We'll propose a discovery and value case or a 30-45 day pilot and show you what the first weeks look like.

Talk to us Take the self-assessment
Next chapter · 3 of 6
The foundation

Every domain above runs on the same governed data fabric — SAP, distributor systems, POS, marketplaces and GST connected, curated, contextualised and consumed, with lineage from source to the board pack.

Next chapter: The foundation