Data & AI services for the public sector — advise, build, transform, run.
DaasLabs teams set your data and AI strategy, build the platforms, transform citizen services, schemes, procurement and public finance, and run what we build — for central and state departments, PSUs and development-finance institutions, and cities. Our Data Fabric Framework, accelerators from delivered builds and a supervised digital workforce, in which officials decide, are how we deliver it faster.
Public-sector data and AI programmes stall because every one starts from zero.
Departmental silos, files and scanned records, and long procurement and implementation cycles mean value arrives late — or not at all.
Patterns we met in delivered public-sector work — not statistics.
1 · No governed data foundation
Departmental systems, registers and files don't agree — identifiers differ and lineage is unknown.
2 · Projects rebuild from scratch
Each tender re-invents pipelines, dashboards and controls, so timelines and costs grow.
3 · Portals alone don't change the process
A new portal digitises the form but leaves reading, checking and deciding to the same overloaded officers.
4 · AI pilots never reach production
Proofs of concept built without governance, privacy controls and a clear decision owner stay on the shelf.
Government is moving from dashboards to agents that prepare the work — with officials deciding.
India Stack changed what citizens expect; the DPDP Act and IndiaAI change how data and AI must be handled. Agents now read, check and draft; officials decide and sign.
So what for government: the operating model moves from “an officer reads every file” to “agents prepare, an officer decides” — which makes autonomy limits, privacy by design and audit trails the deciding capabilities, because every decision must survive audit, vigilance and RTI.
Counts from platforms as built — capabilities, not outcomes. Client names withheld.
A services firm that arrives with its own IP — so government pays for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — delivered by teams who know citizen services, schemes, procurement, public finance and PSU operations.
Data Fabric Framework
The governed foundation: the 4C method, 167+ pre-built connectors, metadata, lineage, data quality and security — on a government cloud or on premises.
Accelerators
Litigation Analytics, a Strategic Command Centre, Tender & RFP Intelligence, the Bid Extractor and Grievance Triage — plus cross-industry accelerators for reconciliation, governance and automation.
Supervised digital workforce
AI agents that read, check and draft, with officials deciding every case that affects a citizen, a vendor or a rupee, and every step logged.
Nine service lines cover the lifecycle, organised the way government buys 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 & DPICatalogue, lineage, DPDP Act readiness, AI governanceEngineer the platforms and the AI that runs on them.
Data Engineering & Platform ModernisationDepartmental systems, registers & documents on one platform AI & Agentic EngineeringAgents that read, check & draft; officials decideChange how a public function works, end to end.
Citizen Services & Grievance RedressalTriage, routing, timelines, replies Schemes, DBT & Revenue AdministrationBeneficiary lists, payments, revenue gaps Procurement, Public Finance & AuditTenders & bids, fund flow, litigation, audit PSU, Urban & Infrastructure OperationsCommand centres, projects, civic assetsKeep 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 government data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect167+ pre-built connectors: departmental systems and MIS, registers, treasury and finance, tenders and contracts, case and grievance records, and scanned files; 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 department and PSU 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
Public-sector and cross-industry accelerators mean no service line starts from a blank page.
Pre-built, configurable starting points from delivered builds, on the framework. Hover or tap a tile.
Public-sector accelerators Secretary · CFO · Procurement
Finance & revenue CFO · FA
Operations & automation PSU · Cities
Data, privacy & DPI CIO · DPO
Four builds turned files and registers into one decision record.
Counts from platforms as built — capabilities, not outcomes. Client names withheld.
Closed-loop action tracking. Analytics that end in an owned, dated action — not another report. Illustrative action shape. See the case study →
31 agent roles across 8 public-sector domains — officials make every decision.
Agents read, check and draft across citizen services, schemes, revenue, procurement, finance, cities, PSUs and data. None decides an entitlement, a penalty, a tender award or a payment.
PII Guard · L3 (masking only)
Reply Drafter · L1
Charter Timeline Watcher · L0
Payment Failure Analyst · L1
Guideline Assistant · L1
Inspection Risk Scorer · L1
Arrears Notice Drafter · L1
Collections Reconciler · L2
Bid Extractor · L1
Bid Compliance Checker · L1
Contract Milestone Watcher · L0
Treasury Reconciler · L2
Litigation Exposure Analyst · L0
Action Tracker · L2
Audit Reply Assembler · L1
Asset Condition Prioritiser · L1
Field Inspection Assistant · L1
Project Slippage Watcher · L0
Procure-to-Pay Reconciler · L2
Board Pack Drafter · L1
Consent & Purpose Checker · L0
Data Quality Monitor · L0
Agent Auditor · L0
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 prepares every case; an official makes the call.
Illustrative replay: a citizen's grievance about a delayed scheme payment arrives in a regional language. The Grievance Triage Agent works it.
- 1Source systemsA grievance arrives through the portal, in a regional language: an approved scheme payment has not reached the citizen's account.
- 2Data fabricThe grievance lands as governed data; names, phone and ID numbers are masked before any text reaches a model.
- 3Metadata & ontologyIt resolves to the scheme, district and responsible office, with lineage back to the source.
- 4ContextThe scheme guideline, the payment record's status and similar past grievances are assembled — only what this office may see.
- 5AgentPlans and calls tools through the secure AI gateway:
detect_languagefind_payment_statusget_scheme_rule; proposes routing, a priority and a draft reply citing the rule. - 6PolicyKill switch and autonomy level decide: this agent may only suggest, so the case goes to an official — whatever its confidence.
- 7OfficialThe grievance officer reviews the evidence on one screen, edits the reply and approves it — or reassigns the grievance.
- 8ActionThe approved reply is sent and the payment issue is referred to the scheme finance officer, with the charter timeline running.
- 9AuditEvery step, source and decision is recorded for audit and RTI; the officer's edits feed evaluation of the agent.
Autonomy is set per agent and kept low wherever a citizen is affected — inside hard guardrails.
Officials decide
No agent decides an entitlement, a penalty, a tender award or a payment — whatever its confidence.
Privacy by design
Personal data masked before any AI processing; consent and purpose recorded under the DPDP Act.
Confidence threshold
Even reconciliations prepared for approval need high confidence and complete evidence.
On the record
Every plan, source, tool call and decision logged for audit, vigilance and RTI; overrides feed evaluation.
Kill switch
One control halts every agent at once.
Ministries and public institutions are already using platforms built by DaasLabs teams.
Builds for central ministries, a law ministry and a state development bank, a defence control-tower demonstrator (synthetic data), and tooling reused from industry. Client names withheld.
Exposure nobody could total
Cases against ministries spread across courts, departments and law officers. We built one litigation platform with 8 analysis modules — exposure, court and status, 9 ageing buckets, 43 service-matter types, advocates — and alerts on critical aged and high-value cases.
Insight without follow-through
Arbitration was reviewed in static reports. We built arbitration analytics, a highlights report with an AI executive summary, predictive intelligence, and closed-loop actions: emailed to the owner with an SLA and escalation, updated from a link, flagged when overdue.
Strategic Command Centre
Sector and region mix, strategy progress, monthly disbursements, CFO P&L, portfolio quality and the sales funnel.
A defence service's digital control tower
A commander's dashboard on a taxonomy, ontology and process maps, with clearance-based governance — open-source and synthetic data only.
Tender & bid evaluation
Seven specialist agents read tenders and check compliance; bid PDFs become page-linked line items.
Capabilities as built — we show what each platform does rather than outcomes. Client names withheld.
What could a supervised agent squad free up in your department?
Move the sliders to your own volumes. Agents prepare the cases; officials review every one that needs a decision.
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, shown in lakh and crore. Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs. Decisions about citizens, vendors and money stay with officials.
Faster than services alone, a better fit than software alone.
How the DaasLabs model compares with the usual ways government delivers data, AI and agentic automation.
| Criterion | Big-4 / SIservices only | Point productssoftware only | In-house build | DaasLabsservices + framework + accelerators |
|---|---|---|---|---|
| Time to production value | 6–12 months | 3–6 months + integration | 12–18 months | 30–45 days |
| Public-sector data models & domain depth | Generic methods | One use case | Build | Pre-built |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | Data Fabric Framework |
| Ready-made accelerators | Varies | Single product | None | Public-sector + 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 department | 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 the department, finance and vigilance.
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 DaasLabs 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 grievance triage for one department, tender evaluation for one procurement cycle, or litigation analytics for one ministry, with agents starting at “suggest”.
2–3 source systems
e.g. the grievance portal, the scheme MIS and payment files — or tender documents, bids and contract records.
One department or district
e.g. one department, one district or one procurement wing, with a named nodal officer and SMEs for rules and UAT.
DaasLabs 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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