Own the source code. Deploy on your infrastructure. Run autonomous agents across operations, compliance, and workforce — with zero vendor dependency.
Energy and utility operators face a challenge no SaaS AI vendor can solve: critical infrastructure cannot depend on external systems. ibl.ai delivers a production-grade AI platform as full source code — deployed on your infrastructure, air-gapped from the internet, and running without any dependency on us.
This is not a pilot project or a chatbot integration. ibl.ai powers 1.6M+ users across 400+ organizations including NVIDIA's global AI training platform. The same production platform is delivered to energy companies as owned software — configurable, auditable, and permanently under your control.
From SCADA-adjacent workforce training to autonomous compliance monitoring and field operations support, ibl.ai agents reason, act, and execute — not just answer questions. Deploy at headquarters, regional control centers, or remote sites with no connectivity requirements.
ibl.ai serves 1.6M+ users across 400+ organizations including NVIDIA, Kaplan, and Syracuse University. Energy operators receive the same battle-tested platform — not a custom build or a proof of concept.
You receive the complete codebase at handoff. No SaaS subscription. No runtime license. No black-box dependencies. Your team can read, audit, modify, and extend every line of the platform.
Run on your own cloud, on-premises data centers, or fully isolated OT-adjacent environments. ibl.ai operates with zero external dependencies, making it viable for NERC CIP-sensitive and air-gapped deployments.
Connect to Claude, GPT-4, Gemini, Llama, Mistral, or your own fine-tuned models. Swap or combine models without re-architecting. No lock-in to any single AI provider or inference endpoint.
If you never call ibl.ai again after delivery, the platform keeps running. No license checks, no phone-home telemetry, no forced upgrades. Your operations are never held hostage to a vendor relationship.
Every capability is accessible via RESTful APIs. Model Context Protocol (MCP) connects agents to SCADA historians, ERP systems, asset databases, and compliance repositories without custom middleware.
An agent continuously queries regulatory databases, internal policy repositories, and operational logs to detect NERC CIP, FERC, and EPA compliance gaps. It files draft remediation reports, assigns tasks to responsible teams, and tracks resolution — without human initiation.
Deployed on tablets or local servers at remote substations, the agent delivers personalized training sequences based on each technician's certification gaps, recent incident history, and upcoming maintenance schedules — fully offline if needed.
When an operational anomaly is detected, the agent autonomously pulls relevant SOPs, cross-references equipment maintenance history, drafts an incident report, notifies the correct personnel via API, and logs every action with timestamps for post-incident review.
The agent ingests FERC orders, state PUC filings, and internal tariff documents, then autonomously extracts obligations, maps them to operational units, and surfaces upcoming deadlines — querying live databases rather than relying on static summaries.
Connects via MCP to asset management systems and maintenance logs. The agent autonomously identifies aging equipment approaching end-of-life thresholds, generates prioritized replacement recommendations, and drafts capital planning summaries for engineering review.
Tracks operator certifications, license expiration dates, and mandatory training requirements across all sites. The agent autonomously enrolls workers in required courses, sends escalating reminders, and generates compliance rosters for regulators on demand.
Traditional chatbots answer questions. Autonomous AI agents take action, reason over context, and deliver measurable outcomes.
ibl.ai deploys autonomous AI agents that go beyond simple Q&A. Our agents reason, plan, and execute multi-step workflows while you retain full code ownership and infrastructure control.
ibl.ai runs entirely on customer-controlled infrastructure with zero external network dependencies. Suitable for OT-adjacent environments, NERC CIP-sensitive control zones, and remote sites with limited or no internet connectivity.
ibl.ai collects no usage telemetry, no model interaction data, and no operational information. Every query, agent action, and training interaction stays within your infrastructure boundary — permanently.
Every agent action — tool calls, database queries, API requests, decisions, and outputs — is logged with full timestamps and actor attribution. Audit logs are stored locally and exportable for regulatory review.
Built-in multi-tenant architecture supports strict isolation between business units, sites, and user roles. Control center operators, field technicians, compliance officers, and executives see only what their role permits.
Run LLM inference entirely on your own hardware using open-weight models like Llama or Mistral. No queries reach external AI APIs. Sensitive operational data never touches a third-party inference endpoint.
Because you own the full source code, your security team can audit every component — authentication flows, data handling, agent logic, and API integrations — without relying on vendor attestations or black-box trust.
You own the codebase outright. No subscription renewal, no license expiration, no vendor relationship required to keep the platform running. Critical infrastructure AI cannot be subject to vendor business decisions.
Your cybersecurity and OT security teams can inspect every line of code. No black-box components, no hidden telemetry, no undocumented network calls. Meets the auditability expectations of NERC CIP and critical infrastructure frameworks.
Integrate with proprietary SCADA systems, custom asset databases, or legacy ERP platforms. Your developers can modify agent logic, add connectors, and extend workflows without waiting for a vendor roadmap or paying for custom development.
Run the same codebase at corporate headquarters, regional control centers, and remote substations. Deploy on AWS GovCloud, Azure, on-premises bare metal, or fully air-gapped servers — the code runs anywhere.
You control the upgrade cycle. Adopt new features on your timeline, freeze a stable version for regulated environments, or fork the codebase for specialized deployments. Your operations are never disrupted by a vendor's release schedule.
ibl.ai delivers the complete platform source code to your team. We configure the deployment for your infrastructure — cloud, on-premises, or air-gapped — and integrate with your identity provider, data sources, and existing systems via MCP and REST APIs.
Working alongside your operations, IT, and compliance teams, we configure agent workflows for your specific use cases — compliance monitoring, workforce training, incident response, or asset management. We train your team to build and extend agents independently.
Your team takes the platform to production with full ownership. You hold the source code, control the infrastructure, and operate the system independently. ibl.ai is available for ongoing support, but the platform runs without us — by design.
Autonomous compliance monitoring and audit preparation agents reduce the manual labor hours required for NERC CIP, FERC, and state regulatory reporting cycles.
Personalized AI-driven training sequences accelerate time-to-competency for new field and substation technicians, reducing the cost of onboarding and the risk of undertrained personnel on critical equipment.
Autonomous incident response agents that retrieve SOPs, draft reports, and coordinate notifications cut mean time to response on documented incident types — reducing operational risk and regulatory exposure.
Agents monitoring FERC orders, PUC filings, and internal tariff obligations automate the identification and assignment of compliance tasks, eliminating the majority of manual regulatory tracking work.
Asset lifecycle agents surfacing equipment risk signals months ahead of manual review cycles reduce unplanned outages and the capital and reputational costs associated with grid reliability events.
NERC CIP standards require strict controls over access to bulk electric system cyber assets, audit logging, and supply chain risk management. AI platforms that phone home or rely on external SaaS infrastructure create direct compliance exposure.
ibl.ai deploys entirely within your controlled infrastructure with zero external dependencies. Full audit trails log every agent action. Source code ownership eliminates third-party software supply chain risk. Role-based access controls enforce CIP-004 and CIP-007 requirements.
FERC orders and tariff obligations require utilities to track, document, and demonstrate compliance across transmission operations, market participation, and reliability standards — a high-volume, high-stakes documentation burden.
Autonomous agents continuously monitor FERC filings, extract obligations, map them to operational units, and generate compliance documentation. Every agent action is logged and exportable for FERC audit response.
NIST guidance for industrial control system security emphasizes network segmentation, minimizing external connectivity, and protecting OT environments from IT-side threats — including AI systems that bridge the two.
ibl.ai's air-gapped deployment model keeps AI operations fully within IT-side boundaries with no OT network access required. On-premises model inference eliminates the need for any outbound connections to AI APIs.
Power generators and utilities face mandatory emissions reporting, environmental compliance documentation, and state-level regulatory filings with strict deadlines and audit requirements.
Compliance agents autonomously track reporting deadlines, aggregate operational data from connected systems, draft required submissions, and maintain a complete audit trail of data sources and calculations used in each filing.
See how ibl.ai deploys autonomous AI agents you own and control — on your infrastructure, integrated with your systems.