DatacentrIQ
D/01Book a demo

See DatacentrIQ on your own data context.

A demo runs against a synthetic version of your operating model and a representative Control Tower. If you decide to proceed, an 8–12 week paid pilot ships a governed tower on your real data.

30-minute demo

Live walkthrough of a Control Tower runtime, Co-Pilot, decisions, and the artifact lifecycle.

Use-case fit

We review your top decision domain (collections, revenue, growth, productivity) and recommend a starting tower.

Data fit

We discuss your warehouse/lakehouse, PII constraints, and the right data-access mode for production.

Pilot path

If there's a match, we scope an 8–12 week paid pilot creditable toward an annual subscription.

No raw data export required for a demo Metadata-only onboarding available
Request a demo Replies within 24h

Or email us directly · hello@datacentriq.ai

D/02Packaging

Land with one tower. Expand to an operating layer.

Four SKUs across the decision lifecycle. Pricing depends on data volume, deployment mode, and tower count — we'll share it during the demo.

Package
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Starter

AI self-serve analytics without dashboard backlog.

Buyer · Head of Data · RevOps · business ops
  • 3–5 connectors
  • Semantic layer + NLQ
  • Generated dashboards
  • Basic anomaly detection
  • Scheduled summaries
Most common
Package
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Control Tower

One business-critical command center for decisions.

Buyer · COO · business head · transformation lead
  • One published tower
  • Ontology slice + KPI tree
  • Operational dashboard
  • RCA + causal driver view
  • Decision cards + alerts
  • Outcome tracking
Package
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DecisionOps

Governed decisions and HITL workflows.

Buyer · COO · CDAO · risk/ops head
  • Workflow builder + approvals
  • Decision logs + business rules
  • Simulations + scenario design
  • Causal DAG management
  • ROI tracking + auditability
  • Model monitoring
Package
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Enterprise

Multi-tower enterprise decision layer.

Buyer · CIO · CDAO · CEO office
  • Enterprise ontology
  • Multiple towers
  • RBAC / RLS / CLS
  • SSO + SCIM
  • VPC / on-prem · BYO LLM
  • Audit logs + APIs
  • Dedicated success

Pricing architecture · Platform fee + Control Tower fee + AI/agent credits + enterprise add-ons.

D/03Pilot program

One decision domain. 8–12 weeks. Measurable outcome.

A paid pilot ships a governed Control Tower bound to a specific business outcome — recovered revenue, improved collection rate, spend reallocation, productivity lift. Pilot fee is creditable toward an annual subscription.

Recommended first towers
  • · NBFC Collections
  • · Retail Revenue Recovery
  • · Influencer Growth
  • · Sales Productivity
  • · Customer 360
  1. Phase 01

    Discovery

    Identify the decision domain, key questions, and stakeholders. Use-case blueprint.

  2. Phase 02

    Data onboarding

    Connect priority sources. Establish a governed foundation. Metadata-first if needed.

  3. Phase 03

    Metric setup

    Define metrics, formulas, ownership, lineage. MetricIQ catalogue published.

  4. Phase 04

    Intelligence layer

    Enable questions and movement explanation. Business adoption begins.

  5. Phase 05

    Causal & scenario

    Generate candidate DAG, validate claims, configure scenarios. Trust scoring.

  6. Phase 06

    Execution & outcomes

    Wire workflows, assign owners, track decisions. Measure expected vs actual.

  7. Phase 07

    Expansion

    Add towers. Expand users. Build the enterprise artifact registry.

D/04Common questions

Asked before the demo.

If you have an objection that's not here, bring it to the call — we have a written answer for almost every concern enterprise buyers raise.

  1. Q01

    We already have dashboards. Why DatacentrIQ?

    Dashboards show what happened. DatacentrIQ helps teams decide what to do next. It connects metrics to causes, decisions, execution, and outcomes — above your existing BI stack, not a replacement for it.

  2. Q02

    We can't share raw data.

    You don't have to. We can start with metadata or aggregated data, and production runs query-in-place inside your environment through a secure data-plane agent. No raw data export by default.

  3. Q03

    Aren't AI-generated causal DAGs unreliable?

    We agree. The Causal DAG Factory does not treat LLM output as truth. Candidate claims are constrained by ontology, validated structurally, tested against data, challenged by a critic process, reviewed by humans, and published only with explicit confidence and validity boundaries.

  4. Q04

    How is this different from BI + ChatGPT?

    BI + ChatGPT can explain dashboards. DatacentrIQ creates governed operating artifacts — ontology, KPIs, control towers, causal claims, decision policies, execution workflows, and outcome learning. It becomes part of how the enterprise operates, not just how it asks questions.

  5. Q05

    Everyone has AI agents now. What's the moat?

    Agents are not the moat. Governed enterprise artifacts are. Defensibility comes from approved ontology, metric lineage, causal claim registry, Control Tower specs, decision policies, execution workflows, and outcome feedback loops that compound over time.

  6. Q06

    What does pricing look like?

    Platform fee + Control Tower fee + AI/agent credits + enterprise add-ons. Exact numbers depend on data volume, tower count, deployment mode, and term — we'll share them during the demo.

Still reading?

Talk to us. Bring your hardest decision domain. We'll tell you whether DatacentrIQ is a fit — and if not, who is.

Honesty is part of how we package.