The intelligence layer above SCADA.
Predict. Prevent. Perform.
Aevus is predictive operational intelligence for critical infrastructure — telemetry in, foresight out. We sit beside the SCADA you trust, not inside it. And our AI is architecturally incapable of writing to your field equipment: the IL-9000 boundary is enforced at the cloud account, not by policy a person can override.
Industrial operators don't need a prettier SCADA. They need to know what's failing before it fails — and they need that signal pulled out of the noise of fragmented radios, PLCs, historians, and alarm queues no single tool ties together. Every existing platform competes to be a better SCADA. None of them sit above SCADA.
Why now. Legacy SCADA is being wired to cloud analytics, AI maintenance systems, and remote operations at the exact moment operator headcounts are shrinking and infrastructure is aging. The industry's answer so far has been more dashboards, more alarms, more policy — more of the same noise. Operators are drowning in telemetry and starved of intelligence. Aevus is the layer that turns the noise into a decision: which radio is failing in two weeks, which compressor is degrading, which alarm storm is real and which is a sensor drift. And the AI is architecturally incapable of writing back to your field equipment — IL-9000 enforces that at the cloud account boundary, not by policy a person can override.

Where Aevus fits — Layer 6, above SCADA, never inside it.
The Purdue Enterprise Reference Architecture (PERA) is how every industrial engineer learns the SCADA stack. Aevus adds Layer 6 — the intelligence layer that reads from every layer below and writes to none.
Same failure. Two operator experiences.
A single failed pressure transmitter on a refinery's crude distillation unit. Left: what the operator sees today. Right: what the operator sees with an intelligence layer above SCADA.
1,217 alarms in 90 minutes
One advisory. Root cause identified.
The AI cannot push a command. Not by setting — by structure.
IL-9000 is a service-control policy enforced at the AWS organization boundary. Aevus ingests telemetry, runs inference, and renders dashboards. It has no path — and no way to grant itself a path — to publish a write to your control network.
- Architectural, not procedural. The denial lives above the application layer. Our own code cannot reach it to disable it.
- Survives compromise. A breached Aevus account still cannot issue a control write. The boundary is the safety, not the software.
- Auditable. The policy is a few lines of declarative JSON. Reviewable by anyone with an hour and an AWS background.
- Composable with human-in-the-loop. When automated action is wanted, it routes through a separate, operator-controlled path — never the AI.
Worked example. A compressor station radio quality drops 37% over nine days. Aevus correlates the drift with rising bearing temperature and a falling vibration signature, surfaces an explainable two-week failure window to the operator, recommends a maintenance dispatch, and links to the relevant ISA-101 procedure. What Aevus cannot do is send a shutdown command to the PLC — that path does not exist in the architecture. The operator decides. The boundary holds. That's the difference between an AI co-pilot and an AI that can crash the plant.
A composite score for every site. Watched continuously. Reported honestly.
Aevus computes a real-time health score for every asset in your fleet by fusing radio signal quality, telemetry stability, sensor drift, and network reliability. Below: a modeled view of 8 sites at a midstream operator. Trends are 14-day windows.
Signal & path quality
RSSI, SNR, retry counts, FEC statistics. The drift no one watches until it's a 2 AM call.
Data quality & latency
Stuck values, stale data, communication front-end backlog. Quality flags surfaced where operators look.
Drift & calibration
Compares each instrument against fleet baselines, expected operating ranges, and time-since-last-calibration.
Backbone health
Switch port up/down, redundant path symmetry, latency creep — both primary and standby paths instrumented.
A radio fails in 14 days. Aevus sees it on day 5.
A modeled scenario, walked through second-by-second. RSSI drifts. Packet retries climb. Chassis temperature creeps up. The AI's confidence escalates. The pattern is real — we catch it before the 2 AM call.
Numbers, not promises.
Operational modeling against real-world gas-measurement and field-telemetry scenarios. Every figure below is modeled — Aevus is pre-revenue. The math is published below; the assumptions are auditable.
Calculate your savings.
Adjust the sliders to match your operation. All figures are modeled — Aevus is pre-revenue. The assumptions are published and auditable.
All figures are modeled projections based on published assumptions. Actual results will vary by deployment. See the IL-9000 Technical Brief for full methodology.
Built for the control room, not the demo reel.
A peek at the Aevus operator console. Dense, scannable, honest. Stale data is labeled stale. Predictions are labeled predictions. Confidence is shown, not implied.
Designed to ISA-101 high-performance HMI standards · Explore the live console →
Speaks your stack. Respects your standards.
Aevus sits beside the existing SCADA, not inside it. Telemetry in, intelligence out — no replacement of the systems your operators already trust.
Download the IL-9000 Technical Brief
20-page safety architecture deep-dive: enforcement model, threat analysis, compliance mapping, and deployment assumptions — all auditable.
Pick the perspective you operate from.
The Aevus knowledge hub is the same 23 articles for everyone, but the order you read them in changes by role. Choose yours — we'll curate.
You read alarms at 2 AM. You need quieter screens and predictive context — not more dashboards.
You own the stack. PLC programs, alarm rationalization, integration — and the post-mortem after every trip.
You see the cost of unplanned outages, the PHMSA filings, the customer calls. You need fleet-wide visibility.
You evaluate AI-in-OT vendors. You need to know how Aevus is architecturally incapable of writing back.
You drive to the sites. You diagnose at the cabinet. You need predictive dispatch — not 2 AM emergencies.
Built for the operators who can't afford guesswork.
Aevus is being deployed in real-world infrastructure — starting with the verticals where predictive intelligence saves the most.
Compressor failure prediction, alarm flood collapse, and radio network health scoring — for operators running 50+ remote stations across hundreds of miles.
See the midstream solutionPump station monitoring, distribution network health, and regulatory compliance — coming 2026.
Coming soonThree steps. No risk. No field changes.
Aevus sits beside your SCADA — connecting takes days, not months.
Point Aevus at your existing historian or SCADA telemetry feed. No field equipment changes. No downtime. We read — we never write.
Within the first week, Aevus identifies degradation patterns across your fleet. Radio drift, compressor wear, alarm correlations — surfaced as actionable advisories.
Operators see clear, ISA-101-compliant recommendations with full explainability. They decide. The boundary holds. You avoid the 2 AM trip.
An Intrepid Logic product.
Aevus is built by Intrepid Logic LLC — a Service-Disabled Veteran-Owned Small Business headquartered in Katy, Texas. Founded by Lynn Spencer (USAF veteran, 20+ years in critical infrastructure technology) and David Spencer (CIO, systems architecture).
Bring Aevus to a real site.
We're onboarding a limited number of pilot operators in oil & gas, water, and critical-infrastructure telemetry for Q3 2026. If that's you, tell us what you operate.

