The product
What the CDO actually sees
Not wireframes. These are the DataOps copilot screens, running live against 9 pipeline systems with 500+ verified decisions.
You spent $2M on the data stack. Agents still can't tell which sources to trust. Engineers investigate the same pattern from scratch every time it recurs. Half your data estate is dark — available but unused, because nobody computed over it. The system runs the same logic on Day 365 that it ran on Day 1.
RGI DataOps is the first platform where data reasons about itself — trust earned from verified outcomes, fixes that transfer across pipelines, dark data surfaced by cross-graph discovery, and a mathematical guarantee that automation never erodes what the system has learned.
A CDO or VP of Data on Databricks or Snowflake plus SAP, with a 10–15-person DataOps team living on the alert queue — and a mandate to make the estate "AI-ready" or put agents into production. The reasoning layer is the piece that mandate assumes you already have.
You don't buy pillars — you buy outcomes. Each one solves a specific problem you're living with right now. Together they form a closed loop: every verified decision simultaneously improves scoring, data trust, and operational rules. The sections below show each one at work on your pipelines.
Typed edges across 9+ systems. Schema change → join fanout → 7 downstream impacts — traversable in seconds.
Each source makes every other source smarter. +42pp Day-1 accuracy from graph-enriched initialization. Compounds even when learning is paused.
Traverses the causal graph across system boundaries. Root cause <1 min, not 3 hours. Available to every shift.
288 values of institutional intelligence. Per-source, per-column trust learned from verified outcomes — not labeled by a human.
Shadow-test → promote → transfer. No manual config. No deploy. No ticket. ~7 rejected per ~1 promoted — restraint is the point.
Process + ERP + schema data in one causal graph. The fix traces to the source system, not a corrected row pushed back.
A theorem, not a policy. Autonomy cannot erode accuracy. 9,826 gate checks, zero violations. Stand a team down with proof.
6-level hierarchy. Source Trust Card, IKS per product, dark data ratio shrinking, Trust API for every agent. Day 365 ≠ Day 1.
One verified decision → three simultaneous improvements: scoring accuracy (centroids) + data trust (DK weights) + operational rules (AgentEvolver). The pillars aren't independent features — they feed a single closed loop that compounds with every decision.
Monte Carlo, Bigeye, Soda — detect anomalies well. 72% of teams saw >50% faster detection. But detect ≠ fix. Engineers investigate all 400 because the system can't separate a schema version bump from a revenue-breaking join failure. 160 engineer-hours/day on triage. The alert queue is a treadmill — the belt doesn't slow down.
Every data-quality tool runs static rules. January's threshold = December's threshold. Meanwhile: SAP adds 340K new material codes. A vendor renames a field. A Kafka topic changes schema. Yesterday's join fans out 9× today. Your freshness rule fires — doesn't know why the data is late, whether the delay matters, or that the same pattern broke billing_api three weeks ago.
Your best data engineer knows which sources to trust, which joins break on Tuesdays, which alerts are noise. Twelve years of judgment in one person. They leave → six months of ramp. None of that knowledge is in any system. Incident RCA: grep logs + Slack → MTTR ~8 hours. The judgment that could have resolved it in minutes left with the person.
Even firms that DO get agents running hit the next wall. 327% growth in multi-agent systems. 80% of agent-built databases consume sources without knowing which lead to correct decisions. Your catalog labels a source "governed" — but that source led to 15 wrong decisions last quarter. Governance is a label. Trust is a measurement. No harness provides an outcome-conditioned reliability signal — because the harness doesn't have verified outcomes. It has metadata.
You deployed Databricks. dbt models, Airflow DAGs, Monte Carlo monitors, Unity Catalog with lineage. Now build an agent. You can't — not from what you have.
The harness gap: LangChain, CrewAI, AutoGen — new integration layer, new team, new failure modes.
The reasoning gap: the harness queries tables but doesn't know if a table is reliable, whether the join broke last Tuesday, or that the source has been stale for 6 hours.
The result: your $2M data platform produces labeled data no agent can safely consume autonomously. Every harness vendor fills the gap with plumbing, not reasoning.
Dark data — data available in the firm but used for no analysis of any form. Industry average: 40–55%. If your firm is above 40%, that's not a storage problem — it's a reasoning problem. The data exists. Nobody knows if it's reliable, whether it correlates with anything useful, or what it would be worth to connect it. Your catalog indexed it. Nobody computed over it. Cross-graph attention is the mechanism that turns dark data into discovered value — gold dotted lines on the Intelligence Map: "Connect weather API → demand prediction: +$180K/yr."
$184B detected → $23B acted on. Value leaks between analysis and action. 600 unused dashboards = $120K/yr. 25% of critical metrics disagree across reporting tools — different SQL, different join logic. The CFO's monthly close: 7-day Excel scramble to reconcile numbers that should agree from Day 1.
Skipped the data foundation → went straight to Gen-AI pilots → lost 35–38 weeks retro-fitting. Contract-first pipelines: 70% fewer silent errors. Without: 9-month delay, 2× retrofit cost. Every Gen-AI milestone is gated on a data-reasoning layer you haven't built.
Five things that went wrong this week — the same ones that went wrong last week. Each names what caught it, what it cost, and what happens differently after. Illustrative; modeled from DataOps engagement data, no production customers.
SAP pushes MATKL_V2 — 340K new material codes. Join fans out 1× → 9×. Seven downstream systems silently degrade. Without: freshness alert fires → engineer checks DAG → finds join → traces fanout. Three hours. billing_api three weeks later: same pattern, same three hours. With: context graph traverses schema → join → 7 downstream impacts in under a minute. Engineer confirms pre-join filter. One confirmation. billing_api: auto-resolved.
Your Airflow metadata feed has a catalog label: "governed, reliable." It led to 15 wrong triage decisions last quarter. Without: the label says 0.65 trust — static, human-assigned. Nobody updates it. With: DK weight drops to 0.23 after 500 verified decisions prove it's noisy. Every downstream agent reads the new trust score. Enrichment math reduced σ 23–46% on connected sources — each one makes the others more precise.
Schema-change-causing-join-fanout — seen 8 times across 3 pipelines. Without: investigated from scratch each time. Same engineer, same three hours, same fix. With: AgentEvolver shadow-tests "fanout >3× → pre-join filter" on live decisions. 93% accuracy across 3 batches. Four-gate check passes. Promoted. Transferred to billing_api — auto-resolved in 15 minutes. Nobody asked.
Celonis shows "Match Invoice to GR" takes 42 minutes — 70% of total cycle time. Without: the process mining says WHERE. Nobody says WHY. With: Tech-Process Fusion traverses Celonis activity → SAP schema change → join fanout 9× → variant-to-SKU mismatch. Root cause: MATKL_V2, not the join itself. Fix: pre-join filter at the source system. $1M/yr freight leak found when process met transaction data.
A LangChain agent auto-approves data loads. It reads the catalog: "governed." Without: 15 wrong decisions in Q3 — the catalog was right about the schema, wrong about the outcomes. With: Trust API returns 0.14, AMBER. Agent requires human review. "Source B: quality inconsistent. Column 'amount' has 8% variance. NOT safe for autonomous use." Every enterprise agent checks before acting.
Not wireframes. These are the DataOps copilot screens, running live against 9 pipeline systems with 500+ verified decisions.
Your board won't approve "the AI decides now." Neither would you. The system earns authority — category by category, pipeline by pipeline — through shadow testing, measured outcomes, and a mathematical safety floor. You see the curve before anything goes live.
Watches every triage decision. Builds centroid geometry from confirmed outcomes — 180 cells encoding operational judgment. No automation. Pure observation. Judgment Memory
Pattern identified: "Schema change + join fanout >3× → pre-join filter." Seen 8 times. Confirmed 7 of 8. AgentEvolver proposes rule, starts shadow-testing — records what the rule would have done, without executing.
Shadow results after 3 independent batches: 93% accuracy. Four-gate check:
✓ Superiority — 5+ pp improvement over baseline
✓ Correctness floor — meets minimum accuracy for this category
✓ Conservation check — won't erode accumulated knowledge Conservation Gate
✓ Variance stability — consistent, not lucky on one batch
All four gates pass. Rule promoted to active.
Three weeks later, billing_api: same pattern. Auto-resolved in 15 minutes. Engineer notified: "Resolved. Here's what happened." Fix transferred across pipelines — nobody asked.
AgentEvolver shadow-tested on 15 decisions. Accuracy: 45%. Below the correctness floor. Some timeouts were transient (resolve on retry). Others were symptoms of upstream schema changes that needed investigation.
Not promoted. The rejection log shows per-category accuracy deltas: scheduling +8%, permission −12% → net unstable. The system that admits failure is the system you trust. Of every ~10 configurations tested, ~7 are rejected. The restraint is the proof.
10:1 penalty ratio. The system treats an incorrect autonomous action as 10× more costly than an incorrect human action. This is a theorem, not a policy — a mathematical guarantee that the system's accumulated knowledge is never eroded by a run of unusual decisions. 9,826 gate checks. Zero violations. The conservation law is what lets you bank the headcount reduction without the team coming back.
400 alerts/day. All manual. IKS 0. All sources undifferentiated — SAP, Airflow, Kafka, SFTP all sit at trust 0.45–0.65. The system knows nothing. It watches everything. Judgment Memory
Alert noise −70%. MTTR −25–40%. First 3 source profiles visible. Situation Analyzer Signal separated from noise — 45% were schema bumps, partition changes, seasonal drops. 220 alerts/day instead of 400.
Trust diverges: SAP 0.94, Airflow 0.23 — earned from 500 verified decisions. Cross-Graph Attention Enrichment reduced σ 23–46% on connected sources. First pattern transfer — schema-change fix learned in revenue pipeline, auto-applied in billing_api. AgentEvolver Manual investigation ↓ 30–40%.
180 alerts/day. Conservation GREEN across major categories. Conservation Gate Self-healing covers recurring 80%. Team handles novel 20% — every novel incident resolved teaches the system. Data Intelligence Source Trust Cards, IKS per product, Learning Forecast. Manual investigation ↓ 60–70%. $340K/yr from one learned pattern.
data_freshness is the cleanest signal — when data is late, the system's triage is most confident. recurrence is the noisiest — recurring patterns look similar but have different root causes. This is what your best engineer learned over twelve years. The system learned it from 500 decisions in three months — and it'll still be here when they're not.
Scattered gray dots. No lines. A silent, passive data estate. IKS = 0 everywhere. Every source looks the same — undifferentiated. The system knows nothing about what to trust.
Rich network. Bright nodes = trusted sources (SAP 0.94). Dim nodes = noisy (Airflow 0.23). Lines = discovered correlations. Cross-Graph Attention Gold dotted lines = suggested data combinations worth $1.2M — weather API + SAP delivery data predicts spoilage 3 days earlier. Nobody asked. The graph found it.
Conservation Gate is what lets you bank the reduction without the team coming back. Modeled at $110K loaded/FTE.
Static tool: same capability forever. RGI: three improvement channels compound. The gap widens every quarter. Modeled/illustrative.
Monte Carlo 2024 · Gartner MQ G00786244 · McKinsey "DataOps at Scale" 2023 · bank engagement data
You've spent the money. The infrastructure works. What's missing is the reasoning layer on top — the part that turns "data is available" into "data is trustworthy, actionable, and improving." Everything you've deployed feeds the context graph. Nothing gets replaced.
Not a data model — a causal graph. Warehouse joins tables by key. Context graph connects by causation: schema change in SAP → join fanout → freshness alert from Monte Carlo → 3 degraded data products. Typed edges (SCHEMA_CHANGE, PIPELINE_FAILURE, ALERT_FIRED, DECISION_VERIFIED). The graph reasons about why, not just what.
Tech-Process Fusion — the answer to reverse ETL. Tech-Process Fusion Celonis cycle-time KPIs + Signavio throughput + ERP transaction facts in one causal graph. Bottleneck → root cause traces to the source system, not a downstream table. Fix lives where the problem originated. A variant-to-SKU join in one pilot: $1M/yr in freight leaks — invisible until process met transaction data.
Two analysts ask "What's our gross margin?" → different numbers. Different SQL, different joins, different exclusions. 25% of critical metrics disagree. Prompt hub locks numerator, denominator, exclusions as a reasoning-backed KPI definition. Same prompt → same answer.
CFO: "Margin variance waterfall FY-24" → reasoning-backed DAX in <200ms
Planner: "Cycle-time driver by plant" → process × financial KPI correlation
Ops lead: "Late-shipment heat-map last 30 days" → visual + root-cause narrative
Data Eng: "Contract freshness audit" → stale SLAs flagged with lineage links
Any agent — Databricks Genie, LangChain, custom pipeline — calls the Trust API before consuming a source: Judgment Memory
Source A (SAP): trust 0.94, GREEN, 340 verified loads → safe for autonomous use
Source B (spreadsheets): trust 0.14, AMBER → require human review
Source C (third-party): trust 0.71, GREEN for trending only → not safe for financial reporting (8% variance)
Not just our copilot — the reasoning infrastructure every enterprise agent needs. 250+ MCP connectors.
LakehouseIQ, Power BI Copilot, Streamlit, REST/GraphQL APIs — sub-200ms prompt responses from reasoning-backed Metric Views. Voice prompts via Azure Speech. "Show monthly gross-margin trend for FY-24 and highlight any month below 35%." → quality-aware DAX → line chart with red data labels on Feb & Sep → narrative: "FY-24 average margin = 37.2%; February dip driven by freight surge." Same prompt, six personas, one quality-assured answer.
The reasoning layer sits on top of your stack — it doesn't move your data out of it.
• Runs in your VPC or on-prem — not a multi-tenant SaaS
• Your data stays in your estate — no training on your data, nothing exfiltrated
• Connects over your existing credentials and lineage — no new copy of the warehouse
• Aligns to the access controls you already run
• Read-only by default — it observes before it acts
• Write authority is granted per category, and every action is conservation-gated
• PII masking and row-level access inherit your catalog's policy
• Every autonomous action carries its audit trail — proposed, tested, promoted
Context Graph Situation Analyzer Join & dedupe alerts → one business-aware incident per problem. The same alert that fires 40 times across 8 systems becomes one root-cause incident. The noise cut is visible in the first week.
Judgment Memory Data Intelligence Per-source reliability scores earned from verified decisions, not metadata labels. Your agents and your team see which sources lead to correct outcomes — and which don't. SAP 0.94. Airflow 0.23. The score moved because outcomes proved it.
AgentEvolver Conservation Gate AgentEvolver shadow-tests a fix on live decisions, measures across 3+ batches, promotes if it clears the four-gate check. One rule that runs itself — and proves it's safe. The audit trail shows every step: proposed, tested, measured, promoted.
Data Intelligence "What did the system learn today?" — decisions verified, factors stabilized, sources that moved, rules promoted or rejected. The team sees the system learning, daily. Not a black box — observable learning.
| Metric | Baseline | Week 6 | Quarter 1–2 | Pillars |
|---|---|---|---|---|
| Time to production | 6–12 months | Canary in production | 30–60 days | Context Graph |
| Manual investigation | 100% human-driven | ↓ 25–40% | ↓ 60–70% | Situation Analyzer AgentEvolver |
| MTTR | Hours to restore | ↓ 25–40% | ↓ 50–90% | Context Graph Situation Analyzer |
| Pattern transfer | Zero (re-investigate each time) | First transfer | Recurring 80% auto-resolved | AgentEvolver |
| ROI documentation | <5% documented | 100% documented | 100% with evidence packs | Data Intelligence |
Target ranges are modeled/illustrative, drawn from production-AI benchmarks and DataOps engagement data. The evidence pack is what the buyer actually receives. Shadow-first — watch the curve before anything goes live.
Not a slide of claims — the record the system keeps as it learns. What you receive at the end of the pilot:
Every configuration shadow-tested and not promoted, with the per-category accuracy deltas and the reason it failed the gate. ~7 rejected for every 1 promoted — the restraint, documented.
For every promoted rule: superiority, correctness floor, conservation check, variance stability — each with the shadow-batch results that cleared it. Proposed → tested → measured → promoted.
How each source's score moved over verified decisions — SAP 0.45 → 0.94, Airflow → 0.23 — and the outcomes that moved it. Trust you can trace, not a label.
The full gate-check record — 9,826 checks, zero violations — the proof your team stands a function down on, and keeps it down.
Pipeline status, alert volumes, SLA timers. dbt, Great Expectations stop here.
Anomaly detection, root-cause attribution, lineage. Monte Carlo, Bigeye, Soda stop here. Databricks Unity Catalog adds lineage. Still reactive — tells you what happened.
Prioritize decisions, recommend actions, learn from outcomes. Score each incident. Explain the reasoning. Compare to similar past decisions. The system improves with use.
System explains why it recommended this fix, whether to trust its own recommendation, and which factor it's most uncertain about. Process-Tech Fusion: "The bottleneck is 42 minutes because of a schema change that increased join fanout 9×."
Shadow-test → promote → transfer. Fixes proven safe via conservation law. Running in production (SOC Copilot). DataOps = domain configuration. The 10–15 person team becomes 3–4.
Quality-aware NL answers. Trust API for external agents. Source profiles for everyone, not just the DataOps team. Collibra/Atlan adjacent — but with static metadata. We: outcome-conditioned.
"Your data tells you what additional data to buy." Cross-graph discovery + economic valuation. Gold dotted lines on the Intelligence Map: "Connect weather API → demand prediction: +$180K/year. Free."
GraphRAG retrieves a cited path and stops — the READ rung. RGI decides, verifies the outcome, and updates its own trust scores and rules from that outcome — the RESHAPE rung. The gap is measured: an LLM reading surface evidence agreed with the correct call 12% of the time; the graph-enriched investigation, 76%. Retrieval doesn't compound. This does.
They ship metadata and plumbing — governed labels, agent harnesses. The missing piece is trust earned from verified outcomes, which only exists if you run the decision loop and measure what was right. That's an architecture, not a feature a catalog bolts on — and it's the one thing their roadmaps don't claim. Every quarter you wait, the dark-data and alert-noise cost compounds the other way.
That's the whole point of the conservation gate. A wrong autonomous action is penalized 10× a wrong human one, so the system restrains itself at the boundary — 9,826 gate checks, zero violations, ~7 of every 10 proposed rules rejected. Nothing goes live until it clears the four-gate check in shadow, and you watch the curve before granting any authority.
RGI DataOps is one copilot on a shared substrate — the Graph Attention Engine. The same loop that earns source trust and promotes a self-healing rule here runs, unchanged, across four more domains. DataOps comes first because it's the data-reasoning foundation the others stand on — the trust signal every agent, in every domain, calls before it acts.
The reasoning layer under your data estate — trust, self-healing, dark-data discovery. this doc
Security operations — triage, self-healing response, the same shadow-to-promote loop running live.
Source-to-pay — supplier trust, invoice and spec reasoning, autonomy earned category by category.
Purchasing / margin copilot — ordering, usage truth and price shocks reasoned against verified outcomes.
Trading operations — where volatility makes the conservation gate's "know when not to act" the whole game.
One substrate — context graph, judgment memory, conservation law — so judgment learned in one domain becomes an asset the whole platform keeps.
