Data handling, deployment, reliability, and pricing — the questions compliance and security teams actually ask before a demo.
data phleet's automation modules perform NERC compliance checks directly — reading your equipment documentation, protection settings, and plant schematics, applying the relevant standard's validation logic, and producing audit-ready evidence. That's a different thing from scheduling reminders or storing documents. Your team reviews and approves the output; the platform does the extraction, comparison, and documentation work that used to take hours or days.
Most GRC and coordination tools deliver value by organizing compliance work — scheduling activities, reminding teams when checks are due, tracking completion status, storing evidence. They still rely on people to manually execute the check, interpret the results, and upload evidence. data phleet's automation modules apply NERC validation logic directly to your data and produce the compliance result itself. Traditional tools help you manage compliance. data phleet helps you achieve it.
It isn't a replacement for compliance judgment — it's leverage for it. data phleet doesn't charge by the hour to do work that should be automated; it gives your team (or your existing consultants) the evidence and analysis up front, so the hours spent go toward judgment calls, not manual data collection. Utilities that already work with trusted NERC consultants can run both together — the platform handles the repeatable extraction and validation work, and your consultants focus on the parts that actually need their expertise.
Yes, always. Every check data phleet runs is reviewed and approved by your team before it's treated as final — the platform performs the analysis and produces the evidence; your engineers confirm it. That review step isn't optional busywork left over from the old process; it's where professional accountability stays intact.
That depends on the deployment model you choose. In encrypted cloud hosting, data phleet runs in a secure environment with TLS encryption in transit and AES encryption at rest, and inputs can be de-identified before processing and re-associated using your own keys afterward. In a hybrid model, sensitive data processing stays inside your network while you access reporting through a secure cloud interface. In a fully on-premise deployment, the entire platform — including the AI processing layer — runs inside your firewall and no data leaves your network. You choose the model that fits your environment.
This is exactly the risk purpose-built systems are designed to avoid. Uploading protection settings, facility ratings, relay coordination data, or topology information into a general-purpose AI tool creates real uncertainty under CIP-002, CIP-003, CIP-004, CIP-011, and CIP-013 — where the data is stored, who has access, whether it's retained or used for training, and whether it leaves your controlled environment at all. data phleet runs as a closed-loop, purpose-built system: data stays within the deployment model you choose, validation logic is deterministic, and every input and output is traceable — so you're not introducing an unapproved data-handling workflow to get the benefit of automation.
Three: encrypted cloud hosting for teams that want immediate access without infrastructure overhead; a hybrid model that keeps sensitive processing inside your network while you access reporting through a secure cloud interface; and a fully on-premise, self-contained deployment for facilities with strict air-gap or data-residency requirements, where nothing — including the AI processing layer — ever leaves your infrastructure.
That's the specific problem data phleet is built to solve, and it's also the specific problem general-purpose AI tools aren't. NERC compliance validation requires deterministic, repeatable results — the same input has to produce the same output every time, with a traceable methodology behind it. General-purpose AI is probabilistic by design: outputs often look correct even when they aren't, and there's no defensible way to show why a given answer was correct. data phleet applies fixed, deterministic validation logic per standard, flags missing data instead of guessing, and generates a documented methodology alongside every result — so your team can say “this was validated using defined logic,” not “this looks right.”
Two reasons: reliability and data handling. On reliability — general-purpose models are probabilistic, so the same question can produce different answers with no way to audit why one was chosen, which is the opposite of what a NERC compliance record needs to hold up. On data handling — feeding protection settings, topology, or facility data into a public AI tool raises real CIP questions about storage, retention, and third-party access that most organizations aren't equipped to answer. Purpose-built, closed-loop automation avoids both problems at once.
The same inputs your team already has: one-line and three-line diagrams, protection settings, relay coordination data, maintenance records, manufacturer documentation, and equipment nameplates — structured or unstructured, it doesn't need special formatting. data phleet reads and extracts what it needs directly from what you already produce.
A live demo is 30 minutes and runs against your own standards, not a canned example. From there, timelines depend on which standards and how many sites you're bringing on, but the checks themselves run in seconds to minutes once your data is connected — the work that used to take hours or weeks is the part being replaced.
Pricing is scoped to your site count and which standards you need automated, not a one-size-fits-all license. The fastest way to get a real number is to book a demo and walk through your current footprint — we'll size it to what you actually run, not a generic tier.