Industries / Oil and gas
AI for oil and gas, with engineers in control.
Agents that watch, forecast and recommend across production, maintenance and planning. People keep every safety-critical decision.
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.
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 behind our AI systems. The right stack depends on the model, the data, the architecture and where it runs.
Generative AI and large language models
Models, chosen per task
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral AI
- Cohere
- Hugging Face
- OpenRouter
- LangChain
- LlamaIndex
AI agents and orchestration
Tools, steps and handover
- LangGraph
- AutoGen
- CrewAI
- Semantic Kernel
- Agentic workflows
- Tool calling
- Function calling
- Multi-agent systems
Vector databases and semantic search
Grounded answers from your content
- Pinecone
- Weaviate
- Milvus
- Qdrant
- Chroma
- pgvector
- Elasticsearch vector search
AI integration and APIs
Connecting to your systems
- REST APIs
- GraphQL
- Webhooks
- Microservices
- API gateways
- SDK integrations
- Third-party AI APIs
Cloud and AI infrastructure
Where it runs
- AWS
- Microsoft Azure
- Google Cloud
- Amazon SageMaker
- Azure Machine Learning
- Google Vertex AI
- Databricks
AI evaluation and observability
Quality and cost, measured
- Model evaluation
- Prompt testing
- Hallucination detection
- Latency monitoring
- Cost tracking
- Output quality assessment
- AI observability
- Langfuse
Show the full stack 12 more layersShow fewer layers
Programming languages
What the code is written in
- Python
- Java
- JavaScript
- TypeScript
- C++
- R
- Go
- SQL
- Swift
- Kotlin
AI and machine learning frameworks
Training and running models
- TensorFlow
- PyTorch
- Scikit-learn
- Keras
- XGBoost
- LightGBM
- JAX
- Hugging Face Transformers
Natural language processing
Understanding text
- Text classification
- Sentiment analysis
- Named entity recognition
- Text summarization
- Semantic search
- Question answering
- Embeddings
Computer vision
Understanding images and video
- OpenCV
- YOLO
- Detectron2
- Image classification
- Object detection
- OCR
- Image segmentation
- Facial recognition
- Video analytics
Speech and voice AI
Listening and speaking
- Speech-to-text
- Text-to-speech
- Voice recognition
- Voice assistants
- Conversational voice AI
- Audio processing
Data engineering and processing
Moving and preparing data
- Apache Spark
- Pandas
- NumPy
- Apache Kafka
- Apache Airflow
- Databricks
- dbt
Databases and data storage
Where the data lives
- PostgreSQL
- MySQL
- MongoDB
- Redis
- Microsoft SQL Server
- Amazon DynamoDB
- Elasticsearch
- Snowflake
Containers and orchestration
Deploying and scaling
- Docker
- Kubernetes
- Amazon EKS
- Azure Kubernetes Service
- Google Kubernetes Engine
- Terraform
MLOps and model operations
Keeping models healthy
- MLflow
- Kubeflow
- DVC
- Model versioning
- Model monitoring
- CI/CD pipelines
- Automated retraining
- Performance tracking
Backend development
Services and business logic
- Node.js
- Django
- FastAPI
- Flask
- Spring Boot
- .NET
- Express.js
Frontend and application development
What people use
- React
- Next.js
- Angular
- Vue.js
- React Native
- Flutter
- Android
- iOS
Development and collaboration tools
How we ship
- Git
- GitHub
- GitLab
- Bitbucket
- Jenkins
- Jira
- Postman
Used in our delivered platforms
From first use case to production. Four stages, with a working version reviewed at each one.
-
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 architecture, success measures and delivery milestones.
Solution design
- Architecture and data access
- Approval checkpoints
- Success measures
-
Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Working version on real data
- Engineer review
- Accuracy tests
-
Ship & support
Roll out to production with 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 dedicated team that works as an extension of yours, from first release to scale.
Project-based
A defined scope, milestones and delivery date for a specific product or workflow.
Consulting & advisory
Architecture and readiness advice before you commit to a build.
What our clients say.
“Avinya Labs built our full-stack AI solution, delivering scalable infrastructure, optimized AI workflows, and a seamless user experience. Their expertise accelerated our launch and significantly reduced execution risk.”
“We needed a platform that could handle complex construction workflows without breaking down every week. Avinya Labs delivered exactly that.”
“Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”
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 we start?
With one task where the data exists and the risk is contained. We map it, agree what good looks like, and build a working version you can review before anything scales.
How do you handle our data?
Access is scoped to what the system needs, with role-based permissions and audit trails where records change. We agree data handling during discovery.
Which AI models do you use?
We evaluate models against your real workflow and choose per task. The architecture is designed so models can change later without rebuilding the product.
Ready to turn your vision into reality?
Tell us the problem, the data, and where manual effort sits today. We'll map the path from MVP to scale.
Or email contact@avinyalabs.co