DatacentrIQ converts raw schemas and business intent into approved enterprise artifacts: ontology, KPIs, Control Towers, causal claims, decision policies, workflows, and outcome learning. AI drafts. The system tests. Humans approve. The platform learns.
Anyone can wire an LLM to a database. What compounds over time is an enterprise registry of approved ontology, metric lineage, causal claims, Control Tower specs, decision policies, execution workflows, and outcome feedback loops.
AI proposes. The system tests and traces. Humans approve. The platform learns from outcomes.
Core principle · DatacentrIQ
Business entities, relationships, attributes, and semantic mappings derived from schemas, query logs, and human review.
Approved metrics with formulas, owners, lineage, grain, refresh cadence, and access policies. Versioned.
Use-case-driven cockpit: objective, KPIs, signals, priority cases, causal model, decisions, workflows, outcomes.
Tested logic for when a KPI movement, anomaly, or segment warrants attention — with backtest quality scores.
A specific causal edge with evidence, confidence, valid population, valid range, assumptions, and review status.
Rules defining what actions can be recommended, who approves them, what constraints apply, and how rollback triggers.
Steps, owners, SLAs, escalation rules, integration hooks, and completion tracking — bound to decisions.
Measurement design comparing expected vs actual impact. Variance feeds back into future recommendations.
Every artifact passes through a governed lifecycle. Nothing reaches production without being tested, traced, reviewed, and approved.
Every published artifact carries provenance — data source, owner, confidence, last refresh, and human approver.
Three guided workflows produce the most important artifacts. Each one is a sequence of inspectable workspaces, not a single LLM call.
Converts raw database schemas into a business-level ontology that business users can think in — entities, relationships, attributes, confidence, PII classification.
loan_account_master cust_mst coll_visit txn_hist
Entity: Loan Account has: Customer has: Repayment Events has: Collection Actions belongs to: Branch
Converts human intent into a governed operating tower through a sequence of custom workspaces — every artifact inspected before publish.
“Create a tower to reduce stockout-driven revenue loss in North Region.”
Production-grade causal graphs are not LLM one-shots. The platform assembles, falsifies, revises, and reviews — claims carry evidence, confidence, valid population, and valid range.
Each module is a first-class product surface — visible, inspectable, bound to the artifact registry. Nothing operates as a black box.
The daily decision agenda — what to focus on, what changed, what's blocked, what to simulate, what we learned. Not a dashboard of dashboards.
Every published and draft Control Tower with value at risk, trust score, active cases, pending decisions, blockers, and recommended next step.
The operating page for a specific tower: today's focus, signals, drivers, scenarios, decision queue, execution blockers, outcome learning.
Guided construction. Every step a custom workspace — intent, ontology, filters, KPIs, signals, cases, DAGs, scenarios, decisions, workflows, outcomes, publish.
Enterprise reasoning grounded on approved artifacts. Not a generic chatbot — every answer references ontology, metrics, causal claims, and evidence.
Root cause, driver decomposition, counterfactuals, what-if simulation, mediation, saturation, drift monitoring — with confidence boundaries.
Decision objects: linked tower, expected impact, confidence, causal reasoning, approvals, owner, deadline, constraints, rollback, measurement window.
Tasks, owners, SLAs, escalations, integration hooks, blockers, completion measurement, impact decay tracking.
Expected vs actual. Variance. Root cause of the variance. Learning that flows back into future simulations and recommendations.
Freshness, coverage, source quality, lineage, PII compliance, causal claim status, model drift, signal backtest quality, execution reliability.
Regulated sectors don't need to export raw data. DatacentrIQ can start with metadata, expand to aggregates, and run production-grade query-in-place compute through a secure data-plane agent.
The control plane is hosted by DatacentrIQ. The data plane runs inside the client's VPC. Outbound-only secure connections. Read-only credentials. No public IP. Query firewalls, PII masking, aggregation thresholds, audit logging.
A pilot starts with one decision domain and ships a governed Control Tower in 8–12 weeks. Talk to us about pricing and pilot terms.