VALID8A.AI delivers real-time validation and contextualization of process sensor data — so real-time advisory, optimization, and AI applications in Oil & Gas, Refining, and Chemicals run on inputs you can actually trust.
Across decades of operating and optimizing process units, our founders kept hitting the same failure mode — the sensor data was uncertain before the model ever saw it.
Assure the historian app stays available — and ignore the data quality inside it.
Assume instruments are fine until a work order is raised against them.
Assume the historian data feeding their models is already clean.
Assume both — and reconcile errors away so models run on faulty inputs.
Plant sites have a layer between the historian and the AI pipeline that nobody owns. VALID8A.AI is engineered to be exactly that layer — real-time validation and contextualization built specifically for process sensor data.
Score every value against statistical, physical, and contextual rules — a real-time integrity verdict per tag.
Enrich each tag with equipment class, process unit, operating mode, and alarm state via ISO 14224.
Write the verdict back into the historian with an immutable, audit-ready trail behind every decision.
Validation results don’t stay siloed in a side tool — they’re written into the data historian itself, so every downstream consumer reads each tag with its quality verdict attached.
Every value lands in the historian with its quality flag already attached, so downstream applications consume data they can defend. Asset-management integration folds sensor hardware health into the verdict, yielding combined hardware + sensor validation that asset-care, reliability, and AI teams can all trust.
Designed alongside process and automation engineers who know that bad sensor data kills industrial automation and AI projects before they start.
Upstream and midstream sensor validation across wellheads, pipelines, compressor stations, and SCADA networks operating in remote and intermittent-comms environments.
Continuous validation of asset- and unit-level instrumentation across integrated facilities and plant sites.
Batch and continuous process data integrity for reactors, separators, polymerization trains, utility systems, and on-line analyzer networks.
A four-stage pipeline engineered for plant-floor scale and operator confidence — running on-prem, edge, or private cloud.
Loaded directly from control-system configuration files, so every tag arrives with its equipment context, hierarchy, and validation profile.
DCS Config, Asset HierarchyPull live and historical data from historians, DCS, SCADA, and edge devices via certified protocol adapters.
OPC-UA · PI · IP.21Validation algorithms detect anomalies, degradation, and ultimate failure — every tag receives a real-time integrity score.
SPC · Anomaly DetectionPublish to dashboards and third-party HMIs, or write back to the system of record — each tag carries its quality verdict.
Dashboards, Historian, RESTA walk-through of what happens to a single sensor value as it moves from a transmitter on the unit through VALID8A and out to the consuming AI model or optimizer.
Certified protocol adapters pull live values from DCS controllers, SCADA front-ends, OPC-UA devices, and read historian backfill in parallel. The ingestion tier is stateless and horizontally scalable.
Each value is checked against multiple statistical envelopes — range, rate-of-change, CUSUM / EWMA drift, cross-tag correlation, and frozen-value detection. Failures are aggregated into a single health score (0–100) per instrument so engineers see one number, not twelve alarms.
Trusted rules handle the obvious: drop frozen values, suppress spikes outside instrument range, gap-fill short comms drop-outs. ML models then handle the subtle cases — analyzer drift inside the calibrated range, persistent low-magnitude bias, multi-tag inconsistencies. Every transformation is reversible.
Each tag is enriched with its equipment class, process unit, operating mode, and alarm state, so downstream models can distinguish "the column is in a startup transient" from "the temperature transmitter is broken." Context is sourced from your existing tag dictionary and AMS hierarchy.
The verdict (VALIDATED · QUESTIONABLE · BAD) and its confidence score are written back into the historian alongside the value — using each historian’s native quality-flag mechanism (PI digital states, IP.21 status, etc.). Every downstream consumer reads the value with the quality verdict already attached.
Every transformation is logged with before / after value, rule that fired, model confidence, and analyst override (if any). Certified-clean streams are published to AI/MLOps pipelines, optimizers, MES, LIMS, and reliability platforms via REST, Kafka, or direct historian write — whichever your stack expects.
Five engineered subsystems that turn raw plant tag streams into a certified, AI-ready data product — with full audit trail.
Continuous instrument health via SPC, range, rate-of-change, and cross-tag checks — flags degraded sensors before they corrupt downstream inputs.
Enriches raw tags with process unit, equipment class, operating mode, and alarm-state metadata.
Native connectors for AVEVA PI, AspenTech IP.21, Honeywell PHD and leading DCS — OPC-UA and OCF built in.
Immutable, timestamped log of every validation decision with confidence scores and analyst-override tracking.
Every tag inherits a priority from the equipment and unit operation it serves — critical tags get tighter thresholds and faster escalation.
Real-world operations, reliability, control, and performance outcomes — driven by data your engineers can stand behind.
Give console operators and unit-operations teams validated real-time values and early event detection — so interventions respond to genuine process events, not instrument faults masquerading as upsets.
Ensure vibration, temperature, and pressure sensors feeding rotating-equipment models are free of drift and comms errors that drive false alarms.
Validate feed-forward and feedback sensor inputs for APC controllers and MPC models — preventing bad-data-induced upsets and unplanned shutdowns.
Validate the sensor inputs feeding performance models — heat-exchanger duty, pump efficiency, KPI calculations — so plant performance benchmarking reflects real equipment behavior, not analyzer drift.
VALID8A’s validated tag streams and integrity flags surface in the tools each role already lives in — control-room HMIs, instrumentation work-queues, maintenance backlogs, and engineering studies.
Founded by veteran process and automation engineers — every design decision is informed by what actually works on a plant floor at 3 a.m.
Developed with veteran O&G and refining engineers — not generic data scientists who’ve never seen a P&ID.
Run on-prem, at the edge on industrial hardware, or in a private cloud.
Every validation decision is explainable. Automation engineers retain full override control and visibility into each transformation.
Typical integration to first validated data stream in under two weeks. Pre-built templates accelerate common process-unit types.
VALID8A.AI is engineered to align with the regulatory and technical standards that govern industrial operations across the global energy sector.
Real-time advisory, optimization, reliability platforms, AI initiatives — your downstream applications are only as good as the sensor data feeding them. Reach out to find out how VALID8A’s validated, certified-clean process data will enhance your initiatives.
Contact Us