# Renacore MOC > [!abstract] Thesis > Renacore is building the **intelligence layer for energy infrastructure** — an asset-centric, physics-informed AI that sits read-only over existing SCADA/PLC/historian systems and forecasts failure risk on critical LNG rotating equipment 30/60/90 days out. It is the clearest live instance of [[The OT Intelligence Layer]] thesis playing out in a single company. Founded 2025. Pivoted from a horizontal enterprise-AI platform into LNG predictive intelligence on the back of Qatar energy relationships. **Pre-revenue** on the LNG product; relationship-stage pipeline only. Raising a $3M seed at $12M pre. ## Company & Strategy - [[Renacore/Company Overview and Pivot]] - [[Renacore/Business Model and Go-To-Market]] - [[Renacore/Investment Snapshot]] ## Problem & Solution - [[Renacore/The Reactive Maintenance Gap]] - [[Renacore/Asset-Centric Intelligence Layer]] - [[Predictive Maintenance in O&G]] ## Product & Technology - [[Renacore/Architecture and Infrastructure]] - [[Renacore/Physics-First Quality and Governance]] - [[Renacore/Security and Zero Trust]] ## Commercial - [[Renacore/Pilot Structure and KPIs]] - [[Renacore/Traction and Pipeline]] - [[Renacore/Buying Committee and Personas]] --- ### Directional Arrows of Progress **Deployment Model** `Rip-and-replace hardware → Parallel historian export → Read-only edge connector over existing OT` Renacore never touches the control loop. An Edge Connector sits at Purdue L3/DMZ, speaks OPC UA, and makes outbound HTTPS 443 only — no inbound ports. This is the entire wedge: integration in <30 days with zero process disruption, because nothing in the plant has to change. See [[Renacore/Architecture and Infrastructure]] and [[Renacore/Security and Zero Trust]]. **Modeling Approach** `Pure black-box ML → Statistical features + GBM → Physics-constrained hybrid` Not a neural net guessing. The core blends statistical features and gradient-boosted trees with deterministic thresholds, then validates every prediction against physics constraints. This is the practical, deployable cousin of [[Physics Informed Neural Operators]] — physics as a guardrail, not just a loss term. Outputs: failure probability, Remaining Useful Life, health score, Trip Risk %, Latent Degradation Index. **Trust & Autonomy** `Autonomous actuation → Agentic triage → Human-in-the-loop work orders` The agent drafts work orders into the CMMS (SAP PM, IBM Maximo); a human approves. No autonomous control action ever. This is the correct posture for [[Autonomous Agents]] in a safety-critical OT environment — where [[AI Verification]] and [[Where Domain Evals Matter Most]] decide whether the system is trusted at all. **Quality Gating** `Ship model → Backtest → Model Release Certificate` A model only goes live past a gate: >85% backtest precision, alert flicker <1/hr, zero physics violations, and an explainability/RCA trace. Three validation layers (Input Hygiene → Golden Batch core → Logic Sanity Check). See [[Renacore/Physics-First Quality and Governance]]. **Commercial Motion** `Self-serve SaaS → High-touch enterprise → Land-Prove-Expand` No product-led growth here. Discovery → technical workshop → data review → narrow paid pilot → ROI report → multi-year expansion. The wedge is one or two critical assets; the prize is the whole train, then the fleet. See [[Renacore/Business Model and Go-To-Market]] and [[Selling AI MOC]]. --- ### Key Principles 1. **Read-only is the whole strategy, not a feature** — Refusing to touch the control loop is what makes a <30-day, zero-disruption install possible. The constraint *is* the go-to-market. It collapses the buyer's risk and the security review at the same time. 2. **Physics is the moat against hallucination** — In OT, a confidently wrong alert is worse than no alert. Constraining predictions to physical reality is how Renacore earns the right to be trusted, and it's the durable differentiator a generic ML vendor can't copy cheaply. See [[Bespoke Engineering in Industrial AI]]. 3. **The data is the moat, the relationship is the door** — Echoes [[Predictive Maintenance in O&G]]: "the moat is in the data." Renacore's edge isn't only the model — it's privileged access to Qatar LNG operators (Qatar Energy, Shell Qatar, Dolphin, Nakilat) that a cold-outreach competitor cannot replicate. See [[WebSummit Qatar x Regional Go To Market Reflections]]. 4. **Cost-of-too-late, not cost-of-maintenance** — The pitch isn't "spend less on upkeep," it's "one unplanned LNG shutdown costs millions per day." Selling avoided catastrophe to a CFO is a different motion than selling efficiency. See [[Renacore/The Reactive Maintenance Gap]] and [[Maintenance CapEx]]. 5. **Standards alignment ≠ certification (diligence flag)** — Renacore is *aligned* to ISO 9001 / 14224 / 27001 / 31000 and is SOC2/ISO-27001 *ready*, but holds none of these certifications yet. For a buyer whose procurement gate requires certs, this is a real timeline risk, not a checkbox. --- ### Related - [[The OT Intelligence Layer]] - [[Industrial AI MOC]] - [[Predictive Maintenance in O&G]] - [[The Age of Vertical Models]] - [[AI Eats Services Not Software]] - [[Defensibility Principles MOC]] - [[Selling AI MOC]] - [[Natural Gas Value Chain]] - [[History of Qatar]]