Land and input costs climb, cash sits in project accounts, delays cost interest and trust, buyers expect transparency, and RERA, GST and the DPDP Act ask for numbers that can be defended unit by unit. The answer runs across the whole value chain — from how a plot is evaluated to how a tower is handed over, leased and maintained. DaasLabs is the data and AI services team that helps developers, REITs, infrastructure owners and EPC contractors make that shift, one value-chain domain at a time.
Developers and infrastructure owners organised around projects — an ERP for cost, a CRM for buyers, a scheduling tool for site, a helpdesk for residents — now compete on decisions made across all of them. The ones pulling ahead treat data as a product and AI as an operating capability, not a set of pilots. These are the pressures they are responding to.
Land, steel, cement and labour costs move faster than launch prices can follow, and a project priced at one margin is delivered at another once delays and cost overruns land.
Data & AI: feasibility and cost-to-complete analytics, contractor-bill checks and material reconciliation.
RERA ring-fences collections in each project's designated account, construction-linked plans tie inflows to progress, and lenders want project-level visibility before they disburse.
Data & AI: collections reconciliation, withdrawal checks and 13-week project cash forecasting.
Every month of slippage adds interest and erodes buyer trust, and the completion date registered with RERA is a public commitment. Most delays were visible in site data weeks earlier.
Data & AI: schedule-risk signals, site photos read with vision, and earned-value tracking by package.
Buyers research online, book through portals and channel partners, finance with home loans and expect construction updates and payment status without calling anyone.
Data & AI: one buyer record, lead and channel-partner analytics, home-loan document checks and AI-assisted service.
RERA project disclosures and withdrawal certificates, GST and TDS on property, SEBI's REIT and InvIT regulations, ESG disclosure for listed developers and the DPDP Act all ask for traceable data, delivered faster.
Data & AI: lineage from receipt to RERA account to ledger, and AI-assisted regulatory reporting.
Every project, joint venture and acquired portfolio brings its own spreadsheets, ERP instance and filing system. Titles, leases, drawings and contracts are documents, not data.
Data & AI: one governed data fabric, GenAI document extraction, and agents with autonomy limits and audit trails.
Roads, metro, airports, ports and power transmission are planned across agencies and run for decades. Owners, EPC contractors and InvITs need asset data that holds up from survey to concession reporting.
Data & AI: order-book and site cockpits for EPC, offline field inspections and condition-based O&M.
The response isn't one platform or one model — it is data and AI applied domain by domain, from the land deal to the leased asset, on a shared, governed foundation.
See where, domain by domain ↓Develop and sell, build, operate, and the group functions underneath — the same chain for a residential developer, a REIT or an infrastructure owner, with different emphasis. Select a domain to see the data it runs on, the AI opportunities, and how DaasLabs adds value there.
Land in Indian cities is scarce, titles are layered and approvals run through many authorities. Feasibility is still a spreadsheet of assumptions about absorption, pricing and FSI, and design changes surface in cost late.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Bookings come through property portals, channel partners and walk-ins, each with its own lead list. Brokerage claims, cancellations and demand letters live in spreadsheets, and buyers expect transparency on construction progress and payments.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Projects slip in increments nobody sees until a milestone is missed. Progress lives in site reports and photos, cost-to-complete is re-estimated by hand, and RERA-registered completion dates leave little room.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Steel and cement prices swing, contractor bills arrive with measurement disputes, and equipment such as scaffolding, formwork and cranes is hired, idle and re-hired across sites.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Possession is where trust is won or lost: snag lists, registration paperwork and defect-liability claims pile up, and once residents move in, facility teams juggle tickets, building assets and maintenance charges.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Offices, malls and warehouses are run as portfolios, often inside REITs that report to unitholders. Lease terms, escalations and occupancy sit in documents, and portfolio questions take days of spreadsheet work.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Roads, metro, airports, ports and transmission lines earn over decades. Traffic data, inspection reports and O&M contracts must prove availability and safety to authorities, lenders and InvIT unitholders.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Under RERA, 70% of amounts collected for a project go into a separate account and withdrawals must be certified against completion. GST, TDS on property and stamp duty add more reconciliation, and lenders and investors want project-level cash visibility.
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 developers, asset owners and contractors start from working components rather than a blank page.
Each agent runs the same loop — read, reconcile, quantify, route. The arithmetic happens in SQL; the model reads the documents and drafts; a named owner decides. Nothing is paid, waived or certified off the record.
The agent gathers the engineer's and chartered accountant's certificates, checks cost incurred against completion and prior withdrawals, and drafts the request with every gap flagged. The CFO approves; nothing moves without a person.
See the real estate agent squadsAgents read titles, leases, bills and site reports, and reconcile them with ERP, CRM, bank and project data on the governed Data Fabric.
Each agent has an autonomy level — observe, suggest or act within limits. Payments, waivers and certifications always wait for a person.
Every finding carries an amount, a confidence, a named owner and an SLA — approve, edit, escalate or reject.
Every document read, check run and decision is logged, so buyers, auditors, lenders and the regulator can see how a number was reached.
Platforms we have built for asset owners and a construction-services business, and a demonstration EPC cockpit, generalised into configurable starting points — plus cross-industry accelerators our teams configure to project and portfolio data.
Built with an alternative asset manager and a scaffolding & access services provider, plus a demonstration cockpit for an Indian power-transmission EPC; each runs on the Data Fabric Framework.
Portfolio and company 360s, a market-to-cash funnel from addressable market to cash collected, 24-month collections history with a 13-week cash forecast, a knowledge graph and a copilot — with a role-based 360 for the CEO, CFO, COO, CRO, collections head and CPO.
An executive cockpit for an infrastructure EPC: segments, order book, customers from central and state utilities to private developers, project-site geography, cash and finance 360s, a control tower and AI agents — on an enterprise operating model, ontology and knowledge graph.
Micro-apps for market and competitive intelligence, pursuits and quotes, pricing and billing, fleet and project 360s, scheduling and safety — with AI agents for rental pricing, equipment utilisation, customer loyalty, safety intelligence and geographic expansion.
Cross-industry accelerators below are configured to project and portfolio data in an engagement; their demo pages run on banking sample data.
Working platforms we have built across the value chain. Client names are withheld; the figures below are counts from those builds, not client results. Read the case study.
Portfolio, industry and company 360s with a market-to-cash funnel, collections history, a 13-week cash forecast and a business case per value-creation lever.
Segments, order book, utility and private customers and project sites on one enterprise model, with cash, finance and board views and a control tower.
Five AI agents for pricing, utilisation, loyalty, safety and expansion, plus pursuits, quote-to-cash, fleet, project and safety apps.
Ticket, SLA, recurring-issue and ownership analytics with risk and audit dashboards for executives, managers and auditors, and agent workflows.
Loan estimates, closing disclosures and legacy forms extracted; affordability modelled; property cross-checked; reports released through maker-checker.
Four platforms we have built — for a global alternative asset manager, a scaffolding and access services provider and a Gulf real-estate developer, plus a demonstration cockpit for an Indian power-transmission EPC. Different businesses, the same pattern: one governed data model, role-based 360s on top, and AI that shows its working and leaves the decision with a person.
Counts from the platforms as built. Demonstration data is modelled, estimated or sample data, so we show capabilities rather than client results.
The challenge
A large portfolio, seen one spreadsheet at a time.
What we built
A portfolio 360 that answers from evidence.
The challenge
Order book, sites and cash on different desks.
What we built
A cockpit from order book to cash, in rupees.
The challenge
Equipment, crews and safety across many branches.
What we built
Agents and micro-apps from pursuit to cash.
The challenge
Tickets everywhere, ownership nowhere.
What we built
Ticket, SLA, risk and audit intelligence with agents.
For a US home-loan advisory: loan estimates, closing disclosures, notes and legacy forms are extracted with OCR and vision; affordability and debt-to-income are modelled; the property is cross-checked; a rules engine scores each case; and reports are released only through maker-checker approval, with a personal-data access log and consent records.
What it means for an Indian developer
Platforms
Agent framework
Client names are withheld. Built across real assets, infrastructure, construction services and developer operations; demonstration data is modelled or sample data.
Enter your own volumes. The estimate compares today's manual handling with agents working the cases and people reviewing only the exceptions.
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 (× 12 for a year), shown in ₹ lakh and crore. Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Tell us the problem — a project slipping without warning, collections that don't reconcile to the RERA account, contractor bills you can't check fast enough, a portfolio you can't see in one place, a platform to modernise. We'll propose an assessment or a 30-45 day pilot, delivered by DaasLabs teams on our framework and accelerators.