Services

Nine services, mapped to the public-sector value chain.

Government doesn't buy "data and AI" — it buys grievances resolved on time, benefits that reach the right person, own revenue that grows, tenders evaluated fast and fairly, funds and legal exposure accounted for, and data handled the way the DPDP Act requires. Below, each DaasLabs service line is mapped to the domains where it does that work — for central and state departments, PSUs and cities — with the use cases, the KPIs it moves and the accelerators behind it.

Chapter 2 · Services on the value chain

Where each service line works on the public-sector 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 department, PSU or city 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
15
Lead roles across the map
29
Supporting roles across the map
Leads the work Supports Hover a dot for detail · select a domain to explore it
DaasLabs service lines mapped to the eight public-sector value-chain domains
Service line
01 · Advise
Data & AI StrategyStrategy
Data Governance, Privacy & DPIGovernance & DPI
02 · Build
Data Engineering & Platform ModernisationData platform
AI & Agentic EngineeringAI & agents
03 · Transform
Citizen Services & Grievance RedressalCitizen services
Schemes, DBT & Revenue AdministrationSchemes & revenue
Procurement, Public Finance & AuditProcurement & finance
PSU, Urban & Infrastructure OperationsPSU & urban
04 · Run
Managed Services: DataOps, MLOps & AgentOpsManaged
Service lines engaged 6 6 5 5 6 6 5 5

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

Citizen services & grievance redressal

Grievances arrive through many channels and languages, are routed by hand, and urgent cases wait behind routine ones.

Data
Portals, call centres, letters & social
Data product
Citizen-request data product, masked
AI & agents
Triage, routing & drafting agents
Outcome
Faster, consistent redressal

Public-sector use cases

  • Grievance classification, language detection and routing
  • Priority scoring with same-day escalation of urgent cases
  • Draft replies grounded in scheme rules, for officer approval
  • Service-timeline tracking against the citizen charter

KPIs we help you move

Grievance disposal timeGrievances reopenedCases resolved within the charter timelineCitizen satisfaction

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

Domain 2 of 8

Schemes & direct benefit transfer

Beneficiary lists come from many registers; duplicates, ineligible entries and failed payments are found late and fixed by hand.

Data
Registers, payment files & verification
Data product
Consented beneficiary data product
AI & agents
Dedup, eligibility & failure agents
Outcome
Benefits to the right person, on time

Public-sector use cases

  • Duplicate and ineligible-beneficiary detection
  • Payment-failure and return analysis
  • Scheme-outcome dashboards by district and block
  • Guideline Q&A for field staff, citing the clause

KPIs we help you move

Duplicate entries resolvedPayment success rateTime from approval to paymentCoverage of eligible population

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

Domain 3 of 8

Revenue & tax administration

GST, property tax, stamps and fees are collected through different systems, so under-assessment and arrears surface late.

Data
Returns, property records & collections
Data product
Assessment-to-treasury data product
AI & agents
Gap, risk & arrears agents
Outcome
Own revenue up, arrears down

Public-sector use cases

  • Revenue-gap analytics
  • Inspection risk scoring
  • Arrears prioritisation with the notice drafted
  • Collections reconciled with the treasury

KPIs we help you move

Own-revenue growthArrears outstandingCollection efficiencyInspection hit rate

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

Domain 4 of 8

Public procurement & tenders

Long tender documents, scanned bids and hand-typed comparative statements make evaluation slow and hard to defend.

Data
Tenders, bids, GeM & contract records
Data product
Tender and bid data product, page-linked
AI & agents
Requirement, extraction & compliance agents
Outcome
Faster, defensible evaluation

Public-sector use cases

  • Requirement and eligibility extraction
  • Bid extraction to a comparative statement
  • Bid compliance checks against tender conditions
  • Contract and delivery-milestone tracking

KPIs we help you move

Tender cycle timeEvaluation time per bidRe-tenders and complaintsContract milestones on time

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

Domain 5 of 8

Public finance, litigation & audit

Funds are traced by letter, court cases and arbitrations against government carry exposure nobody can total, and audit replies take months.

Data
Sanctions, treasury, cases & audit paras
Data product
Fund-flow and case data product
AI & agents
Reconciliation, case & audit agents
Outcome
Money and exposure accounted for

Public-sector use cases

  • Fund-flow tracking from sanction to payment
  • Treasury and bank reconciliation
  • Litigation and arbitration exposure, ageing and outcomes
  • Audit-reply preparation with evidence

KPIs we help you move

Unspent balancesReconciliation breaks openCase pendency and ageingAudit paras settled

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

Domain 6 of 8

Urban services & smart cities

Water, waste, roads and permissions run on separate systems; complaints and assets are still inspected and fixed on paper.

Data
Complaints, assets, GIS & readings
Data product
City operations data product
AI & agents
Routing, condition & inspection agents
Outcome
Better services, ward by ward

Public-sector use cases

  • Civic-complaint routing and repeat-issue detection
  • Asset condition and maintenance prioritisation
  • Offline field inspection with GPS and photos
  • Permission checks against the rules

KPIs we help you move

Complaint resolution timeRepeat complaintsAssets inspected on schedulePermission turnaround

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

Domain 7 of 8

PSU operations & public enterprises

PSUs and development-finance institutions report to a board and a ministry, with targets, projects and cash in separate systems.

Data
ERP, projects, portfolio & targets
Data product
Enterprise data product
AI & agents
Command-centre & reporting agents
Outcome
Targets met, boards informed

Public-sector use cases

  • Strategic command centre: targets, portfolio, projects and cash
  • Project, capex and readiness slippage early warning
  • Budget-to-consumption and procure-to-pay tracking
  • Board-pack and ministry-report drafting

KPIs we help you move

Targets achievedProject slippageCapex utilisationBoard-pack preparation time

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

Domain 8 of 8

Data governance & digital public infrastructure

Personal data sits in dozens of databases, the DPDP Act 2023 changes how it may be used, and AI that touches citizens needs governing.

Data
Departmental databases & registries
Data product
Catalogued, consented data
AI & agents
Masking, quality & governance agents
Outcome
Trusted, lawful data use

Public-sector use cases

  • Data catalogue, glossary, lineage and quality
  • Consent and purpose controls
  • Masking before any AI processing
  • AI and agent inventory, evaluation and audit

KPIs we help you move

Critical data elements with ownersData-quality scoresAccess reviews completedAgents with documented evaluation

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

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.

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

ForSecretary / Mission DirectorCEO-PSUCIO / State IT

The client problem

Dashboards and AI pilots multiply across departments, missions and PSUs, but few change how decisions are made. There is no shared, evidence-based view of where the organisation stands on data and AI, which use cases matter most, or who owns them.

Outcomes

  • A maturity baseline across 12 capability layers and five stages, scored on evidence rather than opinion
  • A prioritised roadmap across citizen services, schemes, revenue, procurement and finance, with a business case for each
  • An AI operating model that fits government: ownership, funding, procurement and controls

What we do

  • Data & AI maturity assessment
  • AI strategy & use-case prioritisation
  • Outcome and scheme-performance frameworks
  • 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

Advise · Service line

Data Governance, Privacy & DPI

ForCIO / State ITData protection officerSecretary

The client problem

Personal data sits in dozens of departmental databases. The DPDP Act 2023 changes how it must be collected, used and shared, data-sharing between departments has no common rules, and AI that touches citizens needs a governance model of its own.

Outcomes

  • A data-governance office and operating model departments can run
  • Consent, purpose and access controls for personal data, with masking before any AI use
  • AI and agent governance: inventory, evaluation, approval, audit and a kill switch

What we do

  • Data-governance office & operating model
  • DPDP Act readiness: consent, purpose & access controls
  • Data catalogue, glossary, lineage & quality
  • Inter-departmental data-sharing frameworks
  • AI & agent governance

Typical engagements

  • AssessmentData-governance and DPDP readiness review
  • Pilot · 30-45 daysCatalogue, lineage and masking for one department
  • BuildGovernance office, platform and controls, government-wide
  • Managed runStewardship, DQ monitoring and access reviews

Delivered with

02

Build

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

Build · Service line

Data Engineering & Platform Modernisation

ForCIO / State ITMission Director

The client problem

Each department, scheme and PSU runs its own system, and much of the record is scanned paper. Every new dashboard starts with a data-request letter and a one-off extract that nobody can trace.

Outcomes

  • One governed, cloud-agnostic data platform on a government cloud or on premises
  • Pipelines with lineage from departmental systems, registers and documents
  • Scanned files and forms turned into structured data with OCR and GenAI

What we do

  • Pipelines, batch & streaming
  • Departmental system & register integration
  • Document digitisation: OCR & GenAI extraction
  • Data warehouse & MIS modernisation
  • Infrastructure as Code & legacy migration

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

Build · Service line

AI & Agentic Engineering

ForCIO / State ITSecretaryCEO-PSU

The client problem

GenAI pilots stall at the question nobody can answer: who decides, and how do we explain it? In government, AI that affects a citizen, a vendor or a rupee has to prepare the work and leave the decision to an official.

Outcomes

  • Supervised agent squads that read, classify, check and draft, with officials deciding
  • Grievances, tenders and case files processed with every source cited
  • Every agent step logged for audit, vigilance and RTI

What we do

  • Agent design: roles, squads & autonomy levels
  • GenAI document intelligence for files, tenders & bids
  • Grievance and correspondence triage in Indian languages
  • Evaluation & guardrails: policy, limits, kill switch
  • Grounded assistants for officials, citing the rule

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

03

Transform

Domain practices that change how a public function works, end to end, with our accelerators as the starting point.

Transform · Service line

Citizen Services & Grievance Redressal

ForSecretaryGrievance officerMission Director

The client problem

Grievances and service requests arrive through portals, call centres, letters and social media, in many languages. They are read and routed by hand, urgent cases wait behind routine ones, and replies are slow and inconsistent.

Outcomes

  • Every grievance read, classified and routed to the right office the day it arrives
  • Urgent and repeat grievances prioritised, with escalation before the timeline lapses
  • Draft replies grounded in the rules, approved by the grievance officer

What we do

  • Grievance triage, routing & escalation
  • Service-timeline tracking against the citizen charter
  • Multilingual reply drafting
  • Citizen 360 across departments, with consent
  • Feedback and sentiment analytics

Typical engagements

  • AssessmentGrievance and service-delivery diagnostic
  • Pilot · 30-45 daysTriage and routing for one department’s grievances
  • BuildDepartment-wide triage, tracking and drafting
  • Managed runTriage operations and model monitoring

Delivered with

Transform · Service line

Schemes, DBT & Revenue Administration

ForMission DirectorCommissioner (tax / revenue)Secretary

The client problem

Beneficiary lists are built from many registers, so duplicates and ineligible entries are found late and payment failures sit unowned. On the revenue side, GST, property tax, stamps and fees are assessed and collected in separate systems, and gaps show up only after the year closes.

Outcomes

  • A single, consented beneficiary view, with duplicates and ineligible entries flagged for verification
  • Payment failures analysed and fixed, scheme outcomes visible by district and block
  • Revenue gaps, inspection priorities and arrears surfaced for officers to act on

What we do

  • Beneficiary deduplication & eligibility analytics
  • DBT payment-failure analysis
  • Scheme-outcome dashboards
  • Revenue-gap analytics & inspection risk scoring
  • Arrears prioritisation & collection reconciliation

Typical engagements

  • AssessmentScheme-data and revenue diagnostic
  • Pilot · 30-45 daysOne scheme or one revenue stream end to end
  • BuildBeneficiary and revenue data products, statewide
  • Managed runAnalytics operations under SLA

Delivered with

Transform · Service line

Procurement, Public Finance & Audit

ForProcurement headFinancial adviser / CFOChief Vigilance Officer

The client problem

Tenders run to hundreds of pages and bids arrive as scans, so evaluation is slow and hard to defend. Funds are traced by letter, court cases and arbitrations against government carry exposure nobody can total, and audit replies take months.

Outcomes

  • Tender requirements extracted and bids compared with every value linked to its page
  • Fund flow traced from sanction to payment, with reconciliation breaks explained
  • Litigation and arbitration exposure, ageing and actions in one view, tracked to closure

What we do

  • Tender requirement & eligibility extraction
  • Bid extraction & comparative statements
  • Fund-flow tracking & treasury reconciliation
  • Litigation & arbitration analytics with action tracking
  • Audit-reply and RTI evidence assembly

Typical engagements

  • AssessmentProcurement, finance or litigation diagnostic
  • Pilot · 30-45 daysOne tender cycle, one fund flow or one ministry’s cases
  • BuildEvaluation, fund-flow and litigation platforms
  • Managed runPlatform and agent operations under SLA

Delivered with

Transform · Service line

PSU, Urban & Infrastructure Operations

ForCEO / CMD-PSUMunicipal CommissionerCEO, smart-city SPV

The client problem

PSUs, development-finance institutions and cities answer to a board or council and a ministry at once. Targets, projects, assets and complaints live in separate systems, and board packs and ward reports are compiled by hand.

Outcomes

  • A command centre for targets, portfolio, projects, cash and risks
  • Projects, capex and civic assets with slippage and condition flagged early
  • Board, ministry and council reporting drafted from governed data

What we do

  • Strategic command centres & enterprise cockpits
  • Project, capex & asset monitoring
  • Civic complaints, assets & GIS
  • Field inspection capture
  • Board-pack & MIS automation

Typical engagements

  • AssessmentOperations-data and reporting diagnostic
  • Pilot · 30-45 daysOne command-centre view or one civic service
  • BuildEnterprise or city data products and cockpits
  • Managed runPlatform operations under SLA

Delivered with

04

Run

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

Run · Service line

Managed Services: DataOps, MLOps & AgentOps

ForCIO / State ITMission Director

The client problem

After go-live, departmental feeds change, schemes are revised, models drift and agents need someone watching overrides and limits — often with a small in-house team and a contract that must show service levels.

Outcomes

  • Platforms, pipelines, models and agents run under agreed SLAs
  • Continuous improvement driven by override and evaluation data
  • Knowledge transferred so the department can own the platform

What we do

  • Run & L2/L3 support
  • DataOps
  • MLOps
  • AgentOps: logs, overrides, drift, kill switch
  • Capacity building & handover

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

Our assets

What makes our services faster

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

1
Data Fabric Framework

The governed foundation every engagement runs on: the 4C method (Connect, Curate, Contextualize, Consume), 167+ pre-built connectors, 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

Public-sector accelerators from platforms we have built — Litigation Analytics, the Strategic Command Centre, Tender & RFP Intelligence, the Bid Extractor and Grievance Triage — plus cross-industry accelerators such as CLARION, the Data Governance Hub and NARRATOR, tailored to your rules, data and controls.

Accelerators by service line The case study
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 departments and PSUs 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 Data Fabric Framework, integrated with departmental systems, registers, treasury, e-procurement and GIS; deployable on a government cloud or on premises; tested on real data.

Phase 4
Deploy & adopt

Go-live, people and process change, agent autonomy limits agreed with operations, quality, procurement 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 Data Fabric Framework we build on

Engagement models

Staff Augmentation

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

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.

Next chapter · 3 of 6
The foundation

Every domain above runs on the same governed data fabric — departmental systems, registers, treasury and documents connected, curated, contextualised and consumed, with lineage from source to regulatory report.

Next chapter: The foundation