Build Production-Grade AI Agents for Enterprise Workflows
We deeply understand the specifics of the oil and gas business and its key problems, which allows us to create highly effective IT solutions.
Two paths from AI potential to results
Whether you're just starting with AI tools or ready to build production agents - we meet you where you are.
Track A
Outcome
Business users become AI-capable - without loading IT
Build Internal Capability
Hands-on coaching for business users on Copilot, Cognite etc
Prompt design, workflow architecture & agent evaluation
Safe AI practices and governance patterns
Reduces shadow AI risk through governed platforms
Track B
Outcome
Hours → minutes.
Measurable cycle-time reduction.
Measurable cycle-time reduction.
Accelerate High-Impact Solutions
For workflows drowning in manual data gathering, document handling & repetitive analysis
2–3 production-grade AI agents, co-developed with your team
Connected to your data platforms (DB, SharePoint, Cognite)
For teams who hit the ceiling of basic AI tools and want to make a step change in productivity and complexity of applied solution
Pre-built templates: RAG, Data Quality, Text-to-SQL
Industry and Asset aware agents
Together, these tracks create both breadth (empowerment) and depth (impact) - a sustainable AI capability, not isolated pilots.
breadth
depth
From data to decisions - in minutes, not days
Lessons learned that don’t stay buried in PDFs.
Engineering knowledge made accessible, when it matters most
Errors hiding in 200-page reports? Found. Fixed. Fast.
Find out how smarter review workflows made it possible.
From ‘Where’s that policy?’ to ‘Here’s your answer’ - instantly.
No need to explore HR portal to find the guidance
Don’t Just Question Costs - Know Them.
Reduce overpayments and pricing uncertainty through AI-driven contract insights
- Engage 10-12 employees who like technologies and willing to experiment.
- Train pilot team on effective prompting and basics of applied AI solutions development
- Deploy relevant LLM on corporate servers.
- Connect deployed model to key corporate data sources.
- Coach your IT team to maintain the system.
- Define long list of use cases relevant to your company.
- Select 3-5 most valuable in short term.
- Co-create AI solutions for selected cases.
- Evaluate pilot results and main learnings
- Discuss scale up options and best use cases to be taken further
Small pilots, big clarity. Discover what AI can do for you.
Low risk, high reward: Experience AI in action with a pilot project.
Test drive AI: a small pilot today, a great impact tomorrow.
Discover AI in a low risk sandbox - pilot projects that turn curiosity into clarity.
Industry focused use cases
- Say goodbye to data digging!
Assistant understands your questions and delivers production insights with visual reports in minutes, not hours. - Transparent Pricing at Your Fingertips: Eliminate guesswork from quotations review. Quickly verify contractor submissions against reliable data and get advice for negotiations.
- Learn, Improve, Excel: Capture project lessons systematically, converting past experiences into structured knowledge that prevents future overruns and enhances performance.
Technical Assistant
Problem
Managers spend a lot of time searching for specific production data that isn’t covered by standard reports or dashboards. For example, queries like “Why did well #X experience a drop in production in June?” or “Compare the production trends at Field XYZ over the last quarter” require manual data analysis. This leads to delays:
- To answer non-standard questions takes hours or even days.
- Employees are overloaded with routine analytical tasks (data filtering, chart creation).
- Decisions are made slowly due to a lack of timely information.
AI assistant can:
• Accept questions in natural language, clarify details, and quickly find the required production data in the database.
• Build graphs and visual reports in real time based on the retrieved data.
• Perform rapid screening for anomalies over selected time periods, flagging unexpected changes in key indicators.
Expected result
• Faster access to information: responses to ad-hoc inquiries including visualizations take only a few minutes not hours.
• Reduced routine analytics by about 70%, as many standard queries are processed automatically.
• Quicker and more confident decision-making thanks to fast analytics delivered directly through the dialogue with the AI assistant.
Managers spend a lot of time searching for specific production data that isn’t covered by standard reports or dashboards. For example, queries like “Why did well #X experience a drop in production in June?” or “Compare the production trends at Field XYZ over the last quarter” require manual data analysis. This leads to delays:
- To answer non-standard questions takes hours or even days.
- Employees are overloaded with routine analytical tasks (data filtering, chart creation).
- Decisions are made slowly due to a lack of timely information.
AI assistant can:
• Accept questions in natural language, clarify details, and quickly find the required production data in the database.
• Build graphs and visual reports in real time based on the retrieved data.
• Perform rapid screening for anomalies over selected time periods, flagging unexpected changes in key indicators.
Expected result
• Faster access to information: responses to ad-hoc inquiries including visualizations take only a few minutes not hours.
• Reduced routine analytics by about 70%, as many standard queries are processed automatically.
• Quicker and more confident decision-making thanks to fast analytics delivered directly through the dialogue with the AI assistant.
Other cases
Cost advisor
Problem
The company often overpays when signing service contracts. This happens due to:
Employees who initiate purchases and procurement specialists don’t always know what constitutes a fair price for services.
There’s no efficient system for quickly comparing current contractor proposals with historical data or market benchmarks.
A tendency to avoid conflict: sometimes employees don't challenge inflated prices because they’re unsure of their calculations or hesitant to engage in negotiations.
AI assistant can:
• Analyze contract histories to automatically identify average prices for similar services from past projects.
• Build a “service-price” library with standardized metrics, allowing price estimates for new, similar services, even when descriptions slightly differ.
• Compare prices in real time by checking contractor proposals against internal company data and market indicators (e.g., regional pricing trends, seasonal fluctuations).
• Provide negotiation support by highlighting key cost items and suggesting arguments for price reductions.
• Recommend ways to reduce uncertainties in scope and specifications, hence reduce the quotation prices.
Expected result
• Reduced overpayments: contracts will be based on objective data rather than subjective judgments.
• Greater confidence for procurement teams: they’ll be able to verify prices quickly and negotiate with solid facts.
• Improved transparency: decisions will be backed by historical data and market analysis, making contract approvals smoother.
• Risk mitigation: the system will help spot subtle pricing discrepancies and reduce reliance on individual opinions.
• Optimized service-related expenses for the company.
The company often overpays when signing service contracts. This happens due to:
Employees who initiate purchases and procurement specialists don’t always know what constitutes a fair price for services.
There’s no efficient system for quickly comparing current contractor proposals with historical data or market benchmarks.
A tendency to avoid conflict: sometimes employees don't challenge inflated prices because they’re unsure of their calculations or hesitant to engage in negotiations.
AI assistant can:
• Analyze contract histories to automatically identify average prices for similar services from past projects.
• Build a “service-price” library with standardized metrics, allowing price estimates for new, similar services, even when descriptions slightly differ.
• Compare prices in real time by checking contractor proposals against internal company data and market indicators (e.g., regional pricing trends, seasonal fluctuations).
• Provide negotiation support by highlighting key cost items and suggesting arguments for price reductions.
• Recommend ways to reduce uncertainties in scope and specifications, hence reduce the quotation prices.
Expected result
• Reduced overpayments: contracts will be based on objective data rather than subjective judgments.
• Greater confidence for procurement teams: they’ll be able to verify prices quickly and negotiate with solid facts.
• Improved transparency: decisions will be backed by historical data and market analysis, making contract approvals smoother.
• Risk mitigation: the system will help spot subtle pricing discrepancies and reduce reliance on individual opinions.
• Optimized service-related expenses for the company.
Other cases
Projects After-Action-Reviewer
Problem
Capital projects often repeat cost overruns due to insufficient learning from past experiences.
- Valuable lessons and best practices are dispersed across various reports and not systematically analyzed.
- Project teams lack a centralized view of historical insights related to cost drivers.
- The absence of structured knowledge sharing leads to repeated mistakes and missed opportunities for improvement.
AI assistant can:
• "Digest" existing post project reports or After Action Reviews and create structured knowledge base.
• Create chatbot assisting project teams to quickly reveal any relevant learnings or cost benchmarks
• Create voice controlled or free text input assistant which may collect any unstructured and dispersed inputs from project team members to form consistent effective post project analysis report and to update the Knowledge base.
Expected result
• Structured and easy to maintain projects knowledge base with easy (context aware) navigation
• Easy voice or text input of new learnings to keep knowledge base evergreen
• Quick retrieval of the relevant learnings for new project directly helping to avoid mistakes and cost overruns.
Capital projects often repeat cost overruns due to insufficient learning from past experiences.
- Valuable lessons and best practices are dispersed across various reports and not systematically analyzed.
- Project teams lack a centralized view of historical insights related to cost drivers.
- The absence of structured knowledge sharing leads to repeated mistakes and missed opportunities for improvement.
AI assistant can:
• "Digest" existing post project reports or After Action Reviews and create structured knowledge base.
• Create chatbot assisting project teams to quickly reveal any relevant learnings or cost benchmarks
• Create voice controlled or free text input assistant which may collect any unstructured and dispersed inputs from project team members to form consistent effective post project analysis report and to update the Knowledge base.
Expected result
• Structured and easy to maintain projects knowledge base with easy (context aware) navigation
• Easy voice or text input of new learnings to keep knowledge base evergreen
• Quick retrieval of the relevant learnings for new project directly helping to avoid mistakes and cost overruns.
Other cases
Why Choosing us
Our team of experts combines deep industry knowledge with cutting-edge AI expertise, ensuring you receive the best possible solutions tailored to your unique needs.
Subject matter experts
Our extensive background covers the entire upstream value chain, from well and reservoir management to surface facility optimization, strengthened by years of practical experience with Shell and other major operators.
We’ve successfully built and led cross-functional teams, giving us a clear understanding of what it takes to implement impactful IT and digital solutions in complex industrial environments.
We know what works - and we also know what doesn’t. Having navigated project challenges, we understand the pitfalls to avoid, helping ensure more successful outcomes.
With over 25 years of total hands-on experience in global operations - from offshore platforms to LNG facilities - we bring deep technical expertise in facilities engineering and production optimization.
Technical experts
They drive business growth through innovative digital solutions and best-in-class technology, aligning every project with your business goals to strengthen your competitive edge.
ENBISYS, a technology leader with 20 years of experience across diverse sectors like Oil and Gas, Healthcare, and Edtech. Their strength lies in AI for Enterprise, particularly optimizing neural networks for resource-constrained hardware and navigating the complexities of integrating new technologies within large corporate IT landscapes.
Frequently Asked Questions
As businesses explore AI adoption, common questions arise about practicality, costs, and implementation approaches.
ChatGPT-like tools are universally relevant for many industries and domains:
- Process and analyze vast amounts of business documents in minutes instead of hours, extracting key insights and answering specific questions
- Automate routine tasks like report generation, email drafting, and document summarization, freeing up employees for strategic work
- Enhance knowledge sharing by making corporate expertise easily accessible through intelligent search and Q&A across internal documentation
- Begin with well-defined, low-risk use cases that can demonstrate quick wins and ROI
- Implement on-premise LLM solutions to maintain data security while leveraging AI capabilities
- Develop internal AI expertise through pilot projects to make informed decisions about larger implementations
- Start with basic automation tasks, then gradually expand to more complex applications as your team gains experience
- Create a feedback loop with users to identify pain points and opportunities for AI implementation
- On-premise LLM offers cost-effective implementation with minimal hardware requirements (single GPU server) and no expensive model training needed (use pre-trained models)
- Avoid recurring subscription costs per user
- Scale gradually based on actual usage and benefits
- ...and save on potential future issues with data security.
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