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Offshore production platform at dusk with its process modules lit.

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.

Start the conversation See our solutions

Select a highlighted asset to see the AI at work

Concept artwork · fictional facility · illustrative AI overlay

The problem

Wells, terminals or refineries: AI value stalls in the same four places.

Offshore production platform at dusk
Upstream
Compressor station with an LNG terminal behind
Midstream & LNG
Refinery process unit at dusk
Downstream
  • Disconnected systemsHistorians, maintenance, GIS and documents each hold part of the answer.
  • Knowledge locked in documentsDecades of drawings, datasheets and reports nobody can search.
  • Legacy tools AI can’t reachPilots stall outside the applications people use every day.
  • Data that can’t leaveOperating and subsurface data must stay inside your boundary.

Solutions

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
Alert level
Work history
WO-4410Thrust bearing replaced
WO-5531Route reading · due
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 approve Exception → ownerApproved
Operations supervisorApproves the plan
Field engineerChecks on site
NeIO 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 place Historian Drawings and manuals CMMS
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

Built on NeIO and configured to your systems, data and boundary.

Product screens show demonstration data; names and drawing identifiers are removed.

Proof

Applied AI and data engineering delivered for energy and industrial operators. Client identities are anonymized; results are as reported for each engagement.

  • LNG storage tanks and process plant at dusk
    LNG & refining · SparesSouth Asia

    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.

    Read more
    ~65%less engineer time per part check
    Weeks → daysto a sourcing decision
  • Offshore production platform on the horizon at dusk
    Upstream · Land & GISNorth America

    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.

    Read more
    Minutesfor routine map and lease requests
    ~70%routine analyst time freed
  • Compressor skid with piping at night
    National oil company · ReliabilitySoutheast Asia

    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.

    Read more
    280+models in production
    3,000sensors monitored
  • LNG terminal and pipeline corridor at dusk
    Midstream & LNG · Historian dataMiddle East

    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.

    Read more
    50K+tags synchronised
    ~1 minhistorian to governed table
  • Refinery columns lit at dusk
    Upstream · ProductionSouth America

    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.

    Read more
    20spatial cluster models
    Monthlywell and field forecasts

Why Codvo

Operations depth, the NeIO platform and engineers who stay with you.

01Sovereign by designYour data stays inside your boundary: cloud tenancy, on-premises or air-gapped.
02Operations depthHistorians, CMMS, P&IDs and GIS, connected around real assets.
03Production, not pilotsRunning with energy operators today, measured against an agreed baseline.
04Full-stack deliveryOne team from data foundation to AI agents, then runs it or hands it to yours.

Explore your AI vision

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.