Citizens expect services at UPI speed, every rupee of a scheme has to reach the right person, tenders are evaluated under vigilance scrutiny, and the DPDP Act changes how personal data is handled. The answer runs across the whole of government — from how a grievance is heard to how a fund is released and audited. DaasLabs is the data and AI services team that helps departments, PSUs and cities make that shift, one value-chain domain at a time.
Government organised around departments and files — a scheme MIS here, a treasury system there, a tender committee with a folder of bids — now has to deliver decisions that cross all of them. The bodies pulling ahead treat data as public infrastructure and AI as a supervised capability, not a set of pilots. These are the pressures they are responding to.
Citizens who pay instantly and carry verified documents on their phones expect certificates, licences and grievance replies to be just as quick — and in their own language.
Data & AI: grievance triage and routing, multilingual drafting and service-timeline tracking.
Direct benefit transfer moved money to bank accounts; the hard part now is the list. Duplicates, ineligible entries and failed payments are found late and fixed by hand.
Data & AI: beneficiary deduplication, eligibility checks and payment-failure analysis — an officer decides every case.
States and cities need more of their own revenue — GST, property tax, stamps, fees — while assessment, collection and treasury data sit in different systems.
Data & AI: revenue-gap analytics, inspection risk scoring and arrears prioritisation.
GeM and e-procurement portals digitised buying, but evaluation is still manual: long tender documents, scanned bids and comparative statements typed under vigilance scrutiny.
Data & AI: requirement extraction, page-linked bid comparison and compliance checks — the committee decides.
Audit paras, RTI requests, outcome budgets and the DPDP Act all ask the same thing: where did this number, this decision and this personal data come from — and who saw it?
Data & AI: lineage, consent and access logs behind every report, and audit replies assembled with the evidence.
Each department, scheme and PSU runs its own system; much of the record is still scanned paper in several languages. Every new dashboard starts with another data request letter.
Data & AI: one governed data fabric, with GenAI and OCR turning files into structured, usable data.
The IndiaAI mission is expanding public AI capacity, and AI that touches an entitlement, a penalty or a tender has to be explainable, supervised and auditable. An officer must always own the decision.
Data & AI: agent governance — autonomy levels, approvals, evaluations and a kill switch.
See agent governanceThe response isn't one portal or one model — it is data and AI applied domain by domain, from the citizen's grievance to the audit para, on a shared, governed foundation.
See where, domain by domain ↓Serving citizens, spending and controlling public money, delivering on the ground, and the data foundation underneath — the same chain for a ministry, a PSU or a city, with different emphasis. Select a domain to see the data it runs on, the AI opportunities, and how DaasLabs adds value there.
Citizens who pay by UPI and carry documents in DigiLocker expect a certificate, a licence or an answer to a grievance just as fast — in their own language. Grievances still arrive across portals, call centres, letters and social media, and are routed by hand.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Benefits must reach the right person, once, on time. Beneficiary lists are built from many registers, duplicates and ineligible entries are found late, and failed or returned payments sit in queues nobody owns.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
GST, property tax, stamps, excise, mining royalties, fees and user charges are collected through different systems. Under-assessment, arrears and mismatches show up long after the money should have arrived.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Tender documents run to hundreds of pages, bids arrive as scanned PDFs, and comparative statements are typed by hand under vigilance scrutiny. GeM and CPPP made procurement digital; evaluation is still mostly manual.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Funds move from the centre to states, departments, agencies and vendors, and utilisation certificates are chased by letter. Thousands of court cases and arbitrations against government carry financial exposure nobody can total, and audit queries take months of document hunting.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Water, waste, roads, lighting and building permissions run on separate systems and field teams. Command-and-control centres show the city on screens, but complaints and assets are still inspected and fixed on paper.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
PSUs, development-finance institutions and defence organisations answer to a board, a ministry and often the market at once. Operations, finance and procurement data sit in separate ERPs and units, and targets, readiness and board packs are compiled by hand every quarter.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
India Stack — Aadhaar-based services, UPI, DigiLocker — changed what citizens expect. The DPDP Act 2023 changes how personal data must be handled, and AI that touches citizens has to be safe, fair and explainable.
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 departments, PSUs and cities start from working components rather than a blank page.
In government, agents prepare and officials decide. Agents read, classify, check and draft; anything that affects a citizen's entitlement, a penalty or a tender award goes to a named officer, and nothing happens off the record.
The agent reads the grievance, masks personal data, finds the responsible office and scheme rule, sets a priority and drafts a reply. The grievance officer edits, approves or reassigns it — against the citizen-charter timeline.
See the public-sector agent squadsAgents read files, applications, bids and statements on the governed Data Fabric and assemble the evidence, with personal data masked.
Each agent has an autonomy level. In public-sector squads most only observe or suggest; none decides an entitlement, a penalty or an award.
Every draft lands with a named officer or committee, with the rule, the evidence and the page it came from — approve, edit or reject.
Every step, source and decision is logged, so a vigilance query, an audit para or an RTI request can be answered from the record.
Platforms built for central ministries and a development bank, a defence control-tower demonstrator (synthetic data), and builds from other sectors our teams reuse for tenders and grievances — plus cross-industry accelerators configured to government data, rules and controls.
Some built for government, some reused from other sectors — each card says which. All run on the Data Fabric Framework.
Built for central-government legal work: every case against a ministry by court, status, outcome, category, advocate and financial exposure, with ageing and pendency alerts — plus arbitration analytics a highlights report and closed-loop action tracking for a law ministry.
Specialist agents read a tender end to end: they extract requirements and eligibility criteria, check compliance, research the knowledge base with citations and assess risk — under an orchestrator, with role-based access and approval limits.
Turns vendor bid PDFs — including scans, through OCR — into structured vendor details and line items with unit prices, quantities and totals. Every value links back to the page it came from, and evaluators can question the bid in chat.
Built as a complaints-triage engine for a Fortune 500 insurer: frustration and impact scoring, in-scope classification, four priority levels with same-day escalation for the most critical, and a human-in-the-loop review screen — with personal data masked first.
Cross-industry accelerators below are configured to public-sector data in an engagement; their demo pages run on banking sample data.
Platforms built for central ministries and a state development bank, a defence control-tower demonstrator (synthetic data), and builds from other sectors that our teams reuse for tenders and grievances. Client names are withheld; the figures are counts from those builds, not outcomes. Read the case study.
Every case against a ministry by court, status, outcome, category and advocate, with financial exposure, ageing and pendency, and alerts on critical aged and high-value cases.
Arbitration analytics, a leadership highlights report and predictive intelligence — with actions emailed to the owning ministry, tracked to an SLA, escalated when overdue and closed from a link.
Overview by sector and region, strategy progress and monthly disbursements, CFO P&L and portfolio quality, and the sales funnel by product and relationship manager.
A commander's dashboard built on a taxonomy, an ontology and process maps, with clearance-based data governance and a budget-to-consumption thread — on open-source and synthetic data only.
Agents extract requirements, check compliance and assess risk; the extractor turns bid PDFs into page-linked line items for a comparative statement.
Government decisions are documented in files — court cases, arbitration awards, tenders, bids, sanctions. We build platforms that turn those files into governed data, put the exposure and the ageing in front of the people who own them, and track every action to closure. Client names are withheld.
Cases and arbitrations against government run for years across courts and tribunals. Without one record, financial exposure and ageing are guessed, not known.
A decision is taken in a review meeting, then waits for a letter, a file movement and a reply. Nobody sees which actions are overdue until the next review.
Tender evaluation has to be fast and defensible at the same time. Every number in a comparative statement must trace back to the bid.
The DPDP Act 2023 and India's AI governance direction mean personal data must be protected and every AI-assisted step explained and supervised.
The challenge
Thousands of cases, many courts, no single view of exposure.
What we built
One analytics platform over every case.
The challenge
Insight without follow-through.
What we built
Analytics that turn into owned, dated actions.
The challenge
A public mandate measured in spreadsheets.
What we built
One cockpit for the mandate, the portfolio and the P&L.
The challenge
Long tenders, scanned bids, typed comparative statements.
What we built
Agents that read; evaluators who decide.
Deployable on a government cloud or on premises; the Data Fabric Framework is cloud-agnostic.
An architecture demonstrator: a commander's dashboard built on a taxonomy, an ontology and process maps, closing the loop from sense and analyse to recommend, decide and act — with clearance-based data governance and a budget-to-consumption thread.
Built only from open-source material and synthetic data; contains no operational detail.
Personal data is detected and masked before any text reaches a model; access follows role and clearance; every agent step is logged for audit and RTI. Officials decide everything that affects a citizen, a vendor or a rupee.
Designed around
Enter your own volumes. The estimate compares today's manual handling with agents preparing the cases and officials reviewing every one that needs a decision.
Estimate only, not a quote or a DaasLabs result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − straight-through 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 and crore. Excludes platform and run costs. Decisions about citizens, vendors and money always stay with officials.
Tell us the problem — a grievance backlog, a beneficiary list you can't trust, tenders evaluated by hand, funds you can't trace, a MIS nobody believes. We'll propose an assessment or a 30-45 day pilot, delivered by DaasLabs teams on our framework and accelerators.