AI agentUpstream · Midstream
Agentic GIS
Ask a spatial question in plain language. Get a cited map across GIS, land, permit and well records.
Applied AI across the energy value chain.
Private AI that recognises your assets, links them to your documents and records, and prepares the work, inside your operation.
Select a highlighted asset to see the AI at work
Concept artwork · fictional facility · illustrative AI overlay
The problem
Different assets. The same barriers.
Wells, terminals or refineries: AI value stalls in the same four places.
Solutions
How we unlock value with AI.
Two ways in. Add AI to the applications you already run, or rebuild a process around AI agents. Both stand on one foundation, inside your boundary.
Maintenance application · K-201AI added
Vibration · VT-201B · 30 days
Work history
| WO-4410 | Thrust bearing replaced |
| WO-5531 | Route reading · due |
| WR-DRAFT | Inspect NDE bearing |
AI assistantAnswers cite the record
Vibration has risen for six days and is about 70% of the way to the alert level. Check the overdue route reading, lube-oil temperature and pressure, and the thrust bearing replaced in March 2025.
Summarises the records. The cause stays with the reliability engineer.
Natural-language answers, cited to the record, inside the tools people already use.Illustrative
Peopledecide and approveException routed to ownerApproved
Operations supervisorApproves the plan
Field engineerChecks on site
Operations supervisorApproves the planField engineerChecks on siteNeIO OSPlans the run and routes every exception to the person who owns it.
Your environment
AI agentsdo the work
Asset health agentSpots the drift
Document agentReads drawings and manuals
Work-pack agentDrafts the work request
Systemsstay in placeHistorianDrawings and manualsCMMS
Team channelK-201 work request ready for approval, every source cited.Encrypted · approved answers only
A fusion team: agents work on your systems, people own every decision.
One foundation under bothEach piece answers a barrier above.
- Connected context
Every record about an asset, linked across systems.
Disconnected systems - Engineering knowledge
Drawings, datasheets and manuals read and cited to the page.
Locked documents - AI in everyday tools
Results land in the apps and channels teams already use.
Legacy tools - Your boundary
No external network path. Updates arrive as signed offline packages.
Data that can’t leave
Ready-made agents
Start from an agent already running in production.
Built on NeIO and configured to your systems, data and boundary.
AI agent
AI agentMidstream & LNG · Downstream
Obsolescence Management Agent
From plant tag to cited alternates and certificate checks, before the reorder fails.
AI agentAcross the value chain
Engineering Document Agent
Reads P&IDs, datasheets and manuals, links every tag and cites the page.
Product screens show demonstration data; names and drawing identifiers are removed.
Proof
Explore our deployments
Applied AI and data engineering delivered for energy and industrial operators. Client identities are anonymized; results are as reported for each engagement.
Obsolete spares found before the reorder fails
Challenge: Manufacturers had merged or exited, and the specifications needed to qualify alternates sat in scanned vendor files.
What we did: Read part identities from scans and P&IDs, linked every tag to its documents, confirmed obsolescence and compared alternates with certificate checks.
~65%less engineer time per part check
Weeks reduced to daysto a sourcing decision
Map and lease answers without the queue
Challenge: Routine spatial questions queued 2–3 days behind a small GIS team as the portfolio passed 15,000 wells.
What we did: One client-hosted land layer across GIS, lease, permit and well data, with plain-language questions answered as cited maps and tables.
Minutesfor routine map and lease requests
~70%routine analyst time freed
Asset health monitoring at estate scale
Challenge: Failures were found only after threshold alarms, and maintenance ran on calendar intervals.
What we did: Anomaly models per asset class trained on historian data, deployed through a model factory and served inside the customer’s boundary.
280+models in production
3,000sensors monitored
A governed route from the plant historian to analytics
Challenge: Direct historian queries risked loading live plant systems, with no audit trail to reproduce results.
What we did: Time-windowed historian reads, replayable staging and governed lakehouse tables for reporting, models and agents.
50K+tags synchronised
~1 minhistorian to governed table
Production outlooks that account for geology
Challenge: Generic type curves missed how production varies across a field.
What we did: Wells grouped by geology with spatial indexing and clustering, producing governed monthly forecasts at well and field level.
20spatial cluster models
Monthlywell and field forecasts
Why Codvo
Why Codvo for industrial AI.
Operations depth, the NeIO platform and engineers who stay with you.
Explore your AI vision
Turn energy operations intelligence into real-world impact.
Bring a business process, the systems it touches and the result you want to improve. We’ll agree the KPIs and a baseline, build with your team, and measure the difference before you scale.