Turn signals into action.

• Signal-to-noise overload

  10 dashboards, 200 KPIs, zero clarity on what to act on first.

  Decision risk

• Manual reporting cycles

  Monthly board pack takes 3 days to assemble. Half the data is already stale.

  Time drain

• No forecasting

  Cash flow plans built on last quarter's data. No predictive lift.

  Planning risk

• Anomalies caught late

  Water leak detected when the bill hits. Diesel theft caught at audit.

  Loss risk

• AI feels gimmicky

  Generic chatbots that don't know your data. "Insights" that aren't.

  Adoption risk

• Anomaly detection on water, energy, revenue, vendor cost — out of the box
• Cash flow + receivables + occupancy forecasting (30 / 60 / 90 day)
• Cross-product dashboards — Operations + Financial + IoT in one view
• AI copilot for queries: "Which sites had > 15% water spike last week?"
• Scheduled board packs — PDF / Looker / Slack delivery
• Open API + BigQuery + Looker Studio + Grafana integrations

• Anomaly detection

  Real-time alerts on revenue, cost, IoT, vendor signals.

• Forecasting

  Cash flow, receivables, occupancy — 30 / 60 / 90 day.

• AI copilot

  Natural-language queries on your data. Grounded, not generic.

• Anomaly detection

  Watches every metric. Alerts before it shows up in the P&L.

  • Water / energy spike detection
  • Revenue drop alerts
  • Vendor cost variance
  • IoT condition triggers
  • WhatsApp + Slack alerts

• Forecasting

  Predictive lift on the metrics that drive cash flow planning.

  • Cash flow 30 / 60 / 90 day
  • Receivables ageing forecast
  • Occupancy / booking forecast
  • Demand forecast (commerce)
  • Confidence intervals + drivers

• Cross-product dashboards

  Operations + Financial + IoT + CX in one executive view.

  • Pre-built executive dashboards
  • Drag-drop custom dashboards
  • Drill-down to row level
  • Scheduled email + Slack delivery
  • Mobile-first view

• AI copilot

  Ask in English. Get a chart, a table, or a follow-up question.

  • Natural-language query
  • Grounded in tenant data only
  • Source-citation answers
  • Workflow trigger (create ticket, broadcast)
  • Hindi + Tamil + Kannada support

1. 1

   Detect

   Anomaly detector fires on water spike, revenue drop, vendor variance.

   Signal captured

2. 2

   Diagnose

   AI copilot shows the likely drivers — which site, which meter, which day.

   Root cause

3. 3

   Act

   One click creates a work order, a ticket, or a broadcast. Auto-assigned.

   Action taken

4. 4

   Verify

   Metric watched. Forecast updated. Loop closed.

   Resolved

AI is grounded in your tenant data only — no cross-tenant leakage.

• Postgres (your data)

  Data

• BigQuery

  Warehouse

• Snowflake

  Warehouse

• Looker Studio

  BI

• Grafana

  BI

• OpenAI

  LLM

• Anthropic

  LLM

• AWS Bedrock

  LLM

• Slack alerts

  Channel

• WhatsApp alerts

  Channel

• Per-tenant grounding

  AI sees only this tenant's data. Vector stores partitioned per tenant.

  Zero cross-tenant

• Prompt + response logging

  Every AI interaction logged with actor, prompt, response, citations. Auditable.

  7-year retention

• PII redaction at inference

  PII redacted before LLM call. Original data never leaves the platform unmasked.

  Configurable redaction

• BYO LLM keys

  Use your OpenAI / Anthropic / Bedrock account. Your data, your contract.

  No vendor lock-in

Anomalies caught early

+340%

Vs manual review

Cash flow forecast accuracy

94%

30-day horizon

Water + energy loss recovered

₹12L / yr

Per 500-flat society

Board pack prep time

-87%

From 3 days to 4 hours

Copilot queries / month

1,400 avg

Enterprise tenant

Mean time to detect

-91%

Anomaly vs manual

• Executive dashboard — revenue, cost, NPS, occupancy at a glance
• Anomaly feed — by metric, site, severity, action taken
• Forecast vs actual — accuracy tracked weekly
• AI copilot usage — top queries, citations, action conversion

• “We caught a ₹4.2 lakh water leak in 36 hours. Old workflow would have caught it on next month's bill.”

  Rajan Iyer

  Estate Director · Brigade Cosmopolis

  ₹4.2L leak caught in 36h

• “My CFO trusts the cash forecast for the first time in 8 years. 94% accuracy on 30-day.”

  Meera Bhattacharya

  CFO · Sattva Group

• “Board pack went from 3 days to 4 hours. The copilot does the chart-pulling I used to do.”

  Anish Verma

  Head of Strategy · Embassy Group

  Board prep -87%