Industries / Oil and gas
AI for oil and gas, with engineers in control.
AI in oil and gas works best as agents that watch, forecast and recommend across production, maintenance and planning. People keep every safety-critical decision.
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What we can build for oil and gas. Safety-critical and asset-heavy work needs AI that supports people, not one that replaces their judgement.
Production forecasting
Forecasts from well, plant and market data that refresh as conditions change, reviewed by your engineers.
Maintenance and anomaly alerts
Unusual readings flagged early, with the evidence behind each alert, so teams respond faster.
Procedures and inspection assistant
Answers from procedures, inspection reports and HSE documents, each with a link to its source.
Planning without spreadsheets
Scenario models for planning and capital decisions, replacing slow manual spreadsheet cycles.
Document intelligence
Data extracted from permits, contracts, invoices and inspection reports, with exceptions routed to people.
Logistics coordination
Agents that track supply and logistics issues and propose options for the team to approve.
Built on your systems
Designed to read from the systems you already run, such as ERP, maintenance and historian data. Control systems stay untouched.
Audit trail
Every recommendation and approval recorded, so decisions can be traced.
Human approval
Nothing operational changes without a person signing it off.
Permit-to-work support
Permit packs checked for missing fields before a supervisor reviews them.
Regulatory reporting
Production and emissions reports drafted from operational data, for review.
Shift handover notes
Handover notes summarised from logs, with open issues highlighted.
Match the autonomy to the task. Agents watch and recommend. Engineers keep control.
- L1WorkflowA fixed, auditable path.
- L2AssistantAI drafts. A person reviews.
- L3Supervised agentAI acts and flags exceptions.
- L4AutonomousRoutine, low-risk, monitored.
- Human-ledPeople decideAI supports the analysis.
Production forecasting
Agents refresh forecasts and flag changes. Engineers review and decide.
L3 Supervised agentMaintenance anomaly alerts
Unusual readings flagged with the evidence, for the maintenance team to act on.
L3 Supervised agentProcedures and inspection reports
An assistant finds and summarises the right document. A person checks it.
L2 AssistantDocument and invoice processing
A fixed, auditable workflow, with AI extracting the data.
L1 WorkflowSafety-critical operating decisions
Operating decisions stay with qualified people. AI supports the analysis.
Human-led
Related work from other industries. We have not yet delivered in oil and gas. These engagements built the same kinds of systems.
AI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effort AI · Document intelligenceAI document intelligence
Extraction and classification that sends only the exceptions to people.
>80% less manual review AI · Workflow automationWorkflow automation platform
AI extraction feeding a deterministic rules engine, with consistency checks built in.
~70% faster document prepHave an oil and gas process that runs on spreadsheets and manual checks?
The technology we would use for oil and gas. Chosen per task: forecasting, anomaly alerts and assistants over your procedures and reports.
01 / 07
Machine learning
Custom models for prediction, vision and classification.
- PyTorch
02 / 07
Data pipelines
Moving and preparing data so models see the right, fresh inputs.
- Apache Spark
- Apache Kafka
- Apache Airflow
- Databricks
03 / 07
Databases
Where product data lives.
- PostgreSQL
- Snowflake
- Elasticsearch
04 / 07
Models
The language models behind each feature, picked per task on cost, speed and quality.
- Anthropic Claude
- OpenAI
- Google Gemini
- LlamaIndex
05 / 07
Vector search
How answers stay grounded in your content.
- Qdrant
- Weaviate
06 / 07
Cloud and infrastructure
Where it runs, in your cloud or ours.
- AWS
- Microsoft Azure
- Google Cloud
07 / 07
Evals and operations
Tracing, testing and monitoring once it is live.
- MLflow
From one process to supervised agents. Four stages, with your engineers reviewing a working version at each one.
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Discovery
Map the process, the systems and data involved, and what good looks like.
Discovery notes
- The decisions and data involved
- Where manual work sits today
- Safety and approval rules
-
Scope & architecture
Agree the data sources, the approval checkpoints and which decisions stay with engineers.
Solution design
- Architecture and data access
- Approval checkpoints
- Success measures
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Build
Build against your real data, with engineers reviewing forecasts and alerts at each milestone.
Working build
- Working version on real data
- Engineer review
- Accuracy tests
-
Ship & support
Roll out with approval checkpoints, monitoring and a defined support window.
Production rollout
- Production rollout
- Monitoring
- Support window
Why Avinya for oil and gas. Agent systems with human control, already in production elsewhere.
Human approval by design
Our agent platform puts a human checkpoint before any high-stakes action.
Evidence with every answer
Our assistants link each answer to its source document.
Built for exceptions
Our document systems route only the exceptions to people, with over 80% less manual review in one engagement.
Honest about fit
We tell you where AI will pay off and where a simple workflow is enough.
Engagement models. Pick the shape that fits where you are.
Dedicated team
A team that extends yours as sites, assets and data sources are added.
Project-based
A defined scope for one process, such as forecasting or inspection reports, with milestones and a delivery date.
Consulting & advisory
Advice on where agents fit and where a simpler workflow is enough, before you commit to a build.
What our clients say.
AI platform for an insurance broker
“Avinya Labs built our full-stack AI solution … Their expertise accelerated our launch and significantly reduced execution risk.”
Guides on this topic. Plain answers to the questions buyers ask first.
Operational AI Systems: A 2026 Guide for Enterprises
How AI moves from pilots into the daily operations of a business.
Read the guide → GuideEnterprise AI Deployment: Why Projects Stall in Pilot
The eight causes we see most, and a roadmap that gets AI live.
Read the guide → GuideAI Agent Development Cost: What Drives the Price (2026)
What drives the price of an AI agent: scope, complexity, iterations and retainer.
Read the guide →AI for oil and gas FAQs.
Have you worked in oil and gas before?
Not yet. We have built the same kinds of systems in other industries: agent platforms with human approval, document intelligence and workflow automation. Those engagements are linked on this page.
Will AI make operating decisions?
No. Agents forecast, watch and recommend. Safety-critical operating decisions stay with qualified people.
Does it connect to control systems?
We design for read access to the data you choose, such as ERP, maintenance and historian data. Control systems are not changed.
Where should an operator start?
With one process where the data already exists and a wrong output is low risk, such as search over inspection reports. We map it, agree what good looks like, and build a working version engineers can review.
How is operational data handled?
Access is scoped to what the system needs, and safety-critical decisions stay with an engineer. Where data lives and which models can see it are agreed during discovery.
Which AI models do you use?
We evaluate models against your data and choose per task. Whichever model is used, anything that touches a safety-critical decision keeps an engineer in the loop.
How long does a project take?
A prototype on one process and its data takes about 6 to 9 weeks, and a first version engineers can review about 8 to 12 weeks. Production, with approval checkpoints and monitoring, typically takes 3 to 5 months.
Can we sign an NDA first?
Operational data is often sensitive. Yes. We can sign an NDA before you share anything, and we agree ownership of the work in the contract before the work starts.
What should you check before adopting AI in oil and gas?
Start with the data you already collect, the decisions engineers still own and how errors would be caught. We have not delivered an oil and gas project, so we scope AI in oil and gas work as a discovery step and review risks with the NIST AI Risk Management Framework.
Free tool
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Twenty questions, about four minutes. You get a score across five areas and what to fix first.