Data & AI services for developers and asset owners — advise, build, transform, run.
SCIKIQ teams set your data and AI strategy, build the platforms, transform sales, construction, assets and project finance, and run what we build — for residential and commercial developers, REITs and asset owners, infrastructure owners and EPC and facility businesses in India. Our SCIKIQ Data Fabric, pre-built accelerators and a supervised digital workforce of AI agents are how we deliver it faster.
Real estate data and AI programmes stall because every one starts from zero.
Project-by-project systems, knowledge locked in titles, leases and bills, and long implementations mean value arrives late — or not at all.
Patterns we see across real estate, infrastructure and construction work — not industry statistics.
1 · No governed data foundation
ERP, CRM, project tools and bank data don't agree — units, buyers and vendors are duplicated and lineage is unknown.
2 · Services firms rebuild from scratch
Each project re-invents pipelines, models and controls, so timelines and fees grow.
3 · Software alone doesn't change the process
Point products solve one use case and leave integration, data and adoption to the developer.
4 · AI pilots never reach production
Proofs of concept built without data, controls and an operating model stay on the shelf.
Developers and asset owners are moving from dashboards and spreadsheets to agents that read, reconcile and route.
Agents now read titles, leases and bills, reconcile collections and site data, put an amount on what they find and route it to an owner; people decide.
So what for a developer: the operating model moves from “a person reads the reports” to “agents do the work, a person supervises many agents” — which makes validation, autonomy limits and audit trails the deciding capabilities, especially where buyer money, RERA accounts and safety are involved.
Counts from platforms as built; demonstration data is modelled or sample data — capabilities, not client results.
A platform that arrives with industry IP and the team to run it — so developers pay for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — delivered by teams who know project delivery, sales and collections, assets and project finance.
SCIKIQ Data Fabric
The governed foundation: the 4C method, 268 data sources, metadata, lineage, data quality and security.
Accelerators
Portfolio Intelligence 360, the Construction Services Suite, Operations Intelligence and Home-Loan Document Intelligence — plus cross-industry accelerators for reconciliation, close and FP&A.
Supervised digital workforce
AI agents that do the routine work end to end, with people approving exceptions and every step logged.
Nine service lines cover the lifecycle, organised the way developers and asset owners buy 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, RERA & ComplianceGolden records, RERA & tax lineage, DPDP consentEngineer the platforms and the AI that runs on them.
Data Engineering & Platform ModernisationERP, CRM, projects, FM & bank data on one platform AI & Agentic EngineeringAgent squads, GenAI on titles, leases & billsChange how a developer or asset owner works, end to end.
Sales, CRM & Customer ExperienceLeads, channel partners, demand letters, home loans Project Delivery & Construction ControlSchedule risk, contractor bills, equipment, safety Asset, Leasing & Facility ManagementLeases, REIT portfolios, snags, tickets, O&M Finance, Collections & RegulatoryRERA accounts, withdrawals, GST & TDS, FP&AKeep 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 project and portfolio data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect268 data sources: ERP and project accounting, CRM and bookings, schedules and site reports, facility and BMS systems, bank and RERA accounts, and documents; 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 developer and asset owner 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
Real estate and cross-industry accelerators mean no service line starts from a blank page.
Pre-built, configurable starting points from platforms we have built, on the framework. Hover or tap a tile.
Real estate & infrastructure CEO · COO · CFO
Collections, RERA & close CFO
Projects & automation COO
Data Governance & RERA CDO
Four builds turned scattered project, portfolio and site data into one decision system.
Counts from platforms as built; demonstration data is modelled or sample data — capabilities, not client results.
Illustrative shape of one agent case. Certificates read, numbers checked in SQL, gaps flagged and the request drafted — the CFO approves.
26 agent roles across 8 real estate domains — your people supervise the exceptions.
Agents work end to end across land, sales, construction, procurement, handover, leasing, infrastructure and finance. Policy decides what goes straight through; people approve the exceptions — and no payment, waiver, withdrawal or certification is made by an agent alone.
Feasibility Analyst · L1
Approval Tracker · L0
Brokerage Claim Validator · L2
Demand Letter Agent · L2
Home-Loan Document Checker · L1
Site Photo Reader · L1
Cost-to-Complete Analyst · L1
Material Reconciler · L3
Equipment Utilisation Analyst · L1
Ticket Triage Agent · L3
Building Asset Watcher · L1
Renewal & Vacancy Risk Agent · L1
Portfolio Analyst · L1
Condition Maintenance Planner · L1
Concession SLA Tracker · L0
Withdrawal Request Preparer · L2
GST & TDS Reconciler · L2
Commentary Writer · L1
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 does the legwork on every case; a person makes the call when policy says so.
Illustrative replay: a contractor's running-account bill doesn't match the measurement book and rate contract. The Contractor Bill Checker works it.
- 1Source systemsA contractor's running-account bill arrives that doesn't match the measurement book — a quantity is higher than measured and one rate differs from the contract.
- 2Data fabricThe bill, the measurement book entries and the work order land from the ERP and site systems within minutes, quality-checked, as governed data products.
- 3Metadata & ontologyAll three resolve to the same contractor, BOQ item and tower on the golden project record, with lineage back to each source.
- 4ContextThe rate contract, tolerance rules, previous bills and the billing procedure are assembled — only what this quantity surveyor may see.
- 5AgentPlans and calls tools through the secure AI gateway:
get_ra_billfind_measurementsget_rate_contract; proposes approved quantities and a deduction with evidence and a confidence score. - 6PolicyKill switch, autonomy level, confidence ≥ 0.90 and a difference within tolerance decide: straight through, or to a person.
- 7Human gateThe quantity surveyor reviews the evidence on one screen and approves, edits or rejects the proposal.
- 8ActionA governed, typed action records the certified amount — or drafts a query to the contractor — limited, idempotent and reversible.
- 9AuditEvery step, tool call and decision is recorded; outcomes feed the evaluation that decides whether autonomy can be raised.
Autonomy is set per agent and raised only on evidence — inside hard guardrails.
Confidence threshold
Straight-through only if confidence, amount limit and evidence checks pass.
Amount limits
Contractor bills, refunds, brokerage payouts and withdrawals always go to a person.
Named human owner
Approves, edits or rejects every exception; certifications, waivers and safety decisions always need a person.
Everything logged
Every plan, tool call and decision; overrides feed evaluation. Buyer personal data masked in prompts under the DPDP Act.
Kill switch
One control halts every agent at once.
What could a supervised agent squad free up in your business?
Move the sliders to your own volumes. Agents work the cases; people review only the exceptions.
Estimate only, not a quote or a SCIKIQ 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.
Faster than services alone, a better fit than software alone.
How the SCIKIQ model compares with the usual ways developers and asset owners deliver data, AI and agentic automation.
| Criterion | Big-4 / SIservices only | Point productssoftware only | In-house build | SCIKIQplatform + accelerators + delivery |
|---|---|---|---|---|
| Time to production value | 6–12 months | 3–6 months + integration | 12–18 months | 30–45 days |
| Real estate data models & domain depth | Generic methods | One use case | Build | Pre-built |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | SCIKIQ Data Fabric |
| Ready-made accelerators | Varies | Single product | None | Real estate + 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 developer | 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 projects, sales, finance and compliance.
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 SCIKIQ 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 collections and designated-account reconciliation for one project, contractor-bill checks for one package, or ticket intelligence for one community, with agents starting at “act with approval”.
2–3 source systems
e.g. CRM bookings, bank statements and the project ledger — or RA bills, measurement books and rate contracts.
One business unit
e.g. one RERA project, one tower or one portfolio, with a named owner and SMEs for rules and UAT.
SCIKIQ 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.
Project or managed servicehello@scikiq.com · Talk to us · Back to the site · © 2026 SCIKIQ