Phaidra
AI closed-loop control for mission-critical facilities like data centers, using reinforcement learning over existing SCADA/DCS/BMS. Founder ex-Google DeepMind.
Phaidra delivers AI closed-loop control for mission-critical facilities (data centers, pharma, district energy) using reinforcement learning that integrates with existing SCADA, DCS, and BMS without new hardware and self-improves on sensor data. Founded by an ex-Google DeepMind leader behind DC-cooling work, it raised a $92.5M Series B and sells closed-loop contracts.
Vendor details
Canonical URL
https://phaidra.ai
Category
Enterprise operations agent
Subcategory
Supply Chain — AI closed-loop industrial control
Use cases & customers
Primary use cases
Target customers
In practice
Your data center cooling runs on fixed setpoints that can't keep up with shifting load. Phaidra's closed-loop AI controls the system in real time, adjusting as conditions change.
Adopting AI control usually means installing new hardware in a facility you can't disrupt. Phaidra integrates with your existing SCADA, DCS, and BMS, with no new hardware required.
A static control strategy stops being optimal the day after it's tuned. Phaidra learns from sensor data and self-improves, so the control keeps getting better as it runs.
Agentic Index coverage score
7.5 / 14 capabilities · 54%
| Integrations & Tool CallingAgents act on existing facility control infrastructure by issuing setpoint commands to cooling equipment, integrate with data center management systems, and Phaidra is a named contributor to the NVIDIA DSX and Omniverse digital twin blueprints alongside CoreWeave and Applied Digital deployments, Phaidra liquid cooling announcement 2026-03-16 and news page 2026-07-22 | Full |
|---|---|
| Workflow OrchestrationPhaidra's self learning reinforcement learning AI Agent sends optimal setpoint commands directly to the Coolant Distribution Unit to predict and prevent thermal spikes, cutting effective response delay from minutes to under 10 seconds in validated production environments, Phaidra liquid cooling breakthrough announcement 2026-03-16 | Full |
| Knowledge Grounding & RAGAgents are grounded in the individual facility's live sensor telemetry, converting raw sensor data into actions, with domain experts encoding facility specific control objectives, Phaidra engineering role description and liquid cooling announcement 2026-03-16 | Full |
| Human Oversight & GuardrailsDomain experts configure the AI control systems without writing code and define what they want the agents to do, which sets the bounds up front, but Phaidra's stated design point is self improvement without human intervention and runtime approval gates were not documented, Phaidra engineering role description 2026-07-22 | Partial |
| Security, Identity & GovernanceNo security attestations, identity controls, or permission model was retrieved in this pass, Phaidra product pages 2026-07-22 | Unable to verify |
| Observability & AuditabilityPhaidra publishes agent performance comparisons in white papers, but in product run tracing, decision logging, or audit trails were not documented, Phaidra product pages 2026-07-22 | Unable to verify |
| Memory & State PersistencePublished before and after data shows an agent trained in simulation improving further after several hours of live learning on the production system, with Phaidra stating the reinforcement learning agent taught itself to get better without human intervention, so learned state accumulates in production, Phaidra NVIDIA DSX blog 2025-11-03 | Full |
| Deployment & Data ResidencyNo deployment topologies, regional hosting, or data residency controls were retrieved in this pass, Phaidra product pages 2026-07-22 | Unable to verify |
| Prebuilt Agents, Templates & PacksPhaidra ships named offerings including its cooling control agents and Phaidra Prism, an AI assistant built for data center operators and technicians, but each control agent is trained per facility and no template or agent library was documented, Phaidra product and company pages 2026-07-22 | Partial |
| Triggers & Channel CoverageThe agent runs feed forward rather than reactive, using real time rack power data as a leading indicator to act before heat registers in the fluid, operating continuously against live telemetry, Phaidra liquid cooling breakthrough announcement 2026-03-16 | Full |
| Model Flexibility & RoutingThe system runs proprietary reinforcement learning with no customer facing model choice or routing, Phaidra product pages 2026-07-22 | Unable to verify |
| APIs, SDKs & MCP ExtensibilityPhaidra states domain experts configure the AI control systems, meaning the agents, without writing code and define what they want their agents to do, giving a customer facing configuration surface, but no API, SDK, or MCP surface was documented, Phaidra engineering role description 2026-07-22 | Partial |
| Testing, Debugging & OptimizationAgents are trained in simulation before deployment and the methodology underwent rigorous joint A/B testing in live production environments including an NVIDIA DGX SuperPOD cluster running LLM training workloads and CoreWeave GB200 NVL72 environments, with published before and after performance curves, Phaidra liquid cooling announcement 2026-03-16 and NVIDIA DSX blog 2025-11-03 | Full |
| Browser & Computer UseThe agent acts on facility control equipment rather than through a browser or computer interface, and no computer use capability was documented, Phaidra product pages 2026-07-22 | Unable to verify |
The Agentic Index coverage score grades every vendor Full, Partial or Unable to verify against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded
Pricing
Contact sales
flat
What is public
Phaidra (phaidra.ai - reinforcement-learning AI control system / 'virtual plant operator' agents for mission-critical industrial facilities, primarily AI-factory & cooling (liquid cooling, chiller plants, power, workload management), also refineries/pharma/steel; plugs into existing BMS/SCADA via BACnet/OPC-UA, no new hardware) is enterprise/quote-based - no public price tiers.
Billing mechanics
B2B SaaS subscription (sales-led), no hardware purchase required; priced per facility/deployment (custom); typically positioned so energy savings (~10-30%) offset the subscription within months. No public figures.
Additional watchouts
Custom enterprise pricing only (no public tiers); value framed as energy-savings ROI; mission-critical deployment requires work + shadow-mode onboarding
Sales call required
Yes, required for paid access
Commercial notes
Seattle, founded 2019 (Jim Gao ex-Google DeepMind Energy, Veda Panneershelvam ex-AlphaGo, Katie Hoffman ex-Trane); ~$108.5M+ total raised across multiple rounds - incl. $25M Series A (2022) and $50M+ Series B (Collaborative Fund, Oct 2025) with Helena, Index Ventures, NVIDIA, Sony Innovation Fund + individuals Mustafa Suleyman & Mark Cuban; deployments incl. NVIDIA, CoreWeave, ST Telemedia, Khazna, Merck; competes with Honeywell/Johnson Controls/Siemens/Schneider AI-controls
Support SLA / resale
Supervisory software layer over BMS/SCADA; shadow-mode training then closed-loop control; newer products Phaidra Prism (LLM for ops) + Phaidra Factory (monitoring agents); NVIDIA Omniverse DSX partner
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Alternatives to Phaidra
The closest documented capability profiles to Phaidra among enterprise operations agents tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.
- Optimal Dynamics6.0 / 14A lighter documented profile than Phaidra
- Imubit7.5 / 14Adds documented Observability & Auditability and Deployment & Data ResidencyPhaidra vs Imubit →
- o9 Solutions6.5 / 14Adds documented Observability & Auditability
- Blue Yonder8.0 / 14Adds documented Security, Identity & Governance and Observability & Auditability
- Causa Prima8.0 / 14Adds documented Security, Identity & Governance and Observability & Auditability, among others
- Resilinc9.0 / 14Adds documented Security, Identity & Governance and Observability & Auditability, among others
Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded