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Embedded AI Search for Oil & Gas Engineering Reviews

How Azati embedded AI-powered knowledge retrieval into an existing capital project platform, helping engineering experts find relevant historical observations faster without disrupting established review workflows.

Oil & gas capital project teams can retrieve similar engineering observations in seconds with Azati's embedded AI search. The result: faster reviews, more consistent production decisions, and no new tool for reviewers to learn.

Discuss enterprise AI strategy
~92%

Faster search for similar engineering observations

85–90%

Similarity-matching quality in feature testing

~500

Historical observations accessible across 6–8 active projects

Technologies used

Java
Java
JavaScript
JavaScript
Python
Python
Spring Boot
Spring Boot
React
React
GraphQL
GraphQL
PostgreSQL
PostgreSQL
Hasura
Hasura

The project's specifics

Engineering teams accumulate valuable knowledge across capital projects, but finding a comparable past observation still often requires manually searching project records, reviews, and documentation.

The client’s enterprise platform supported engineering reviews, requirements, PDRI assessments, assurance processes, documentation, and project observations across large oil & gas projects. As the portfolio grew, experts needed a faster way to reuse relevant engineering knowledge without leaving their existing workflow.

Azati embedded AI-powered semantic search into the platform, allowing experts to find and rank similar observations within the relevant engineering discipline. The solution combined enterprise AI orchestration, domain-restricted retrieval, and contextual recommendations while preserving the existing UX, governance, and review process.

What business challenges do engineering organizations face while handling decades of project knowledge?

Challenge 01

Slow access to past engineering decisions

Experts manually searched observations, project phases, and technical reviews to find comparable cases, adding significant time to engineering analysis.

#1
Challenge 02

Knowledge trapped in project history

Valuable engineering experience existed across completed projects but was difficult to reuse consistently across new work.

#2
Challenge 03

AI had to fit existing workflows

The client needed AI inside the established capital project platform, not another application for experts to learn and maintain.

#3
Challenge 04

Enterprise controls could not be compromised

AI-assisted recommendations needed to stay within the relevant engineering discipline and operate within existing infrastructure, governance, and security requirements.

#4

Why do industrial organizations choose Azati for enterprise AI implementation?

AI where engineering work already happens

Azati embedded AI into the existing engineering registry instead of introducing another application, reducing context switching and adoption friction.

Enterprise AI, not just an LLM integration

The enterprise platform integrates semantic retrieval, domain restrictions, LLM orchestration, request lifecycle management, and controlled response handling.

Fast adaptation to complex engineering workflows

Azati quickly learned a specialized oil & gas engineering environment and translated evolving requirements into a working AI capability in just four weeks.

Knowledge reuse without platform replacement

The approach made historical engineering observations searchable and reusable while keeping the client's existing workflow, governance, and infrastructure untouched.

Bring AI into enterprise engineering workflows

Azati helps industrial organizations implement enterprise AI, semantic search, and knowledge management solutions that improve engineering productivity while preserving governance, security, and existing business processes.

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How Azati embedded enterprise AI into engineering review workflows

Azati added AI-powered knowledge retrieval directly to the client's existing engineering platform rather than introducing a standalone assistant. Experts can search for similar observations from within the engineering registry and review relevant project history without leaving their normal workflow.

01

AI-assisted engineering search

The solution analyzes engineering recommendations, retrieves similar historical observations within the same engineering discipline, and ranks them by semantic relevance. Results link back to the source projects, phases, activities, and remediation history.

Key capabilities:
  • Semantic search
  • LLM integration
  • Engineering knowledge retrieval
  • AI recommendations
  • Domain-restricted search
02

Embedded AI workflow integration

Experts launch the search directly from an engineering observation and receive ranked recommendations inside the existing interface. This removes unnecessary context switching while preserving established review and approval workflows.

Key capabilities:
03

Enterprise AI operations and governance

Azati implemented an orchestration layer that manages AI requests, LLM processing, response persistence, lifecycle handling, timeouts, and controlled execution. Domain restrictions and workload controls help keep AI operations predictable and governable within the enterprise environment.

Key capabilities:
  • LLM orchestration
  • Request lifecycle management
  • AI governance
  • Enterprise AI operations
  • Platform reliability
04

Contextual engineering decision support

Instead of returning generic search results, the platform presents ranked observations with the context experts need to assess their relevance, including similarity score, project stage, engineering phase, event and priority, remediation status, and responsible engineer.

Key capabilities:
  • Engineering decision support
  • Contextual recommendations
  • Organizational knowledge reuse
  • Project knowledge management

What Azati delivered

AreaContribution
Enterprise AIEmbedded AI directly into an existing capital project management platform
AI-assisted engineering reviewImplemented AI-powered retrieval of similar engineering observations
Knowledge managementEnabled organizational reuse of engineering expertise across projects
Workflow automationIntegrated AI recommendations into existing engineering review workflows
Enterprise architectureConnected LLM services with enterprise applications through secure backend orchestration
Decision supportImproved access to historical engineering knowledge during technical review
Engineering productivityReduced manual searching across historical project records
Platform engineeringDelivered enterprise AI capabilities without disrupting existing business processes

Operational challenges

Reusing engineering knowledge at enterprise scale

Engineering teams accumulated valuable expertise across multiple capital projects, but locating relevant historical observations required manual searches through project records, engineering reviews, and technical documentation. As the knowledge base expanded, this became increasingly time-consuming and inconsistent.

Introducing AI without disrupting established workflows

The client wanted AI to strengthen existing engineering processes rather than replace them. New capabilities needed to integrate seamlessly into the enterprise platform without changing how experts reviewed observations, managed documentation, or governed project decisions.

Balancing AI performance with enterprise governance

AI-assisted engineering review needed to deliver relevant recommendations while operating reliably inside the client's enterprise environment. The solution also had to control LLM workload, restrict searches to the appropriate engineering domain, and fit within existing infrastructure and security requirements.

Modernize engineering knowledge management with enterprise AI

Whether you're implementing AI-assisted engineering review, introducing enterprise AI, or building intelligent engineering knowledge management systems, Azati helps organizations embed AI into existing business applications while preserving governance, security, and operational continuity.

Discuss enterprise AI implementation

Key delivery outcomes

AreaOutcome
Enterprise AI implementationProduction-ready within 4 weeks
AI-assisted engineering reviewAvailable from every engineering observation
Organizational engineering intelligence50,000+ historical observations searchable
Workflow integrationNo reviewer retraining required
Engineering productivityFaster lookup vs. manual project navigation
Organizational knowledge accessibilityImproved across engineering disciplines
AI governanceDomain-restricted retrieval with request audit
Enterprise AI foundationEstablished for future AI-assisted workflows

Results & business impact

~1 hour → up to 5 minutes

Faster engineering review
~500 observations in 6–8 active projects
AI-assisted search and copy reduced the time experts spent finding comparable observations 85–90%
Similarity-matching quality during testing. Ranked results appear inside the existing review workflow, reducing repetitive research and accelerating engineering review.

Organizational engineering intelligence

Historical engineering knowledge was distributed across projects and difficult to reuse. The solution made 50,000+ historical observations searchable through one AI-assisted experience, turning previously siloed project knowledge into reusable organizational engineering intelligence.

Higher engineering productivity

Experts spent valuable review time locating comparable observations instead of evaluating engineering decisions. Faster access to relevant historical expertise reduced repetitive research and freed reviewers to focus on technical assessment. 85% less manual search effort per reviewer per week.

Seamless AI adoption inside existing enterprise software

Introducing AI as a separate tool would have added training, workflow changes, and adoption friction. Azati embedded AI into the existing engineering interface and delivered the capability in four weeks, with no end-user retraining required.

Enterprise AI governance

AI requests are centrally managed through a dedicated orchestration layer with request tracking, response persistence, timeout handling, and configurable retention policies, providing controlled enterprise AI operations instead of direct, unmanaged LLM calls.

Strategic impact

Lower adoption risk than a standalone AI rollout

By embedding AI into the existing project management platform, the team got a new capability without changing how they worked. This reduced workflow disruption and simplified adoption.

Preserved engineering workflows

Experts continued working within familiar review processes while gaining AI-assisted recommendations at hand. Existing governance, documentation, and project approval procedures remained unchanged.

Enterprise knowledge as a strategic asset

The solution transformed years of engineering observations into searchable organizational intelligence. This will bring value more easily across future capital projects, engineering reviews, and technical assessments.

Foundation for governed enterprise AI adoption

The project established a practical pattern for introducing AI into business-critical applications through controlled orchestration, domain-restricted retrieval, and existing enterprise governance. In a nutshell, the same approach can support future AI-assisted engineering initiatives and facilitate decision-making.

Long-term platform evolution

The project demonstrated how enterprise AI can be introduced incrementally into business-critical applications, enabling organizations to modernize engineering processes without replacing existing systems or breaking what already functions.

Embedded AI vs. Standalone Copilot: which fits capital project engineering?

For high-frequency, in-flow engineering work, embedding AI into the existing enterprise platform can reduce adoption friction and workflow disruption. A standalone copilot is better suited to exploratory, cross-system questions.


The right choice depends on where AI needs to operate: inside an established workflow or across multiple information sources.

DimensionEmbedded AI in existing platformStandalone AI copilot
Reviewer workflowAI surfaces directly inside the engineering registry, eliminating context switchingReviewers leave the registry, open the copilot, enter a query, and return to the workflow
Adoption speedCan go live in weeks with no retraining when the existing UI remains unchangedRequires user onboarding, training, and change management
IT and securityWorks within the existing platform's SSO, RBAC, audit, and approval workflowsIntroduces a separate security and governance perimeter
Best fitRepeated, in-flow lookups such as similar observations, prior decisions, and remediation historyExploratory, open-ended, or cross-system questions
Risk profileLower delivery and adoption risk because AI is added to a trusted applicationHigher risk from new procurement, data-handling reviews, governance, and failure surfaces
Time to valueValue can be demonstrated with the first production release and compounds as the knowledge base growsUsage patterns may take longer to stabilize after rollout

How Azati recommends deciding

Choose embedded AI when the workflow is high-frequency, in-flow, and governed, such as engineering review, document review, claims triage, or capital project assurance.

Choose a standalone copilot when the workflow is exploratory, cross-system, or unbounded, such as natural-language research across multiple enterprise repositories.

Many enterprise AI programs can use both. Azati typically starts with embedded AI when it can deliver value faster inside an established workflow.

Frequently asked questions

Embedded AI means AI capabilities are delivered inside the existing enterprise application reviewers already use. For example, surface similar historical engineering observations directly within the engineering registry, rather than as a separate AI tool or chatbot.

It depends on the workflow. Embedded AI is often a better fit for frequent, in-flow tasks because users can access AI without switching tools or learning a separate interface. Standalone copilots are better suited to exploratory or cross-system research. Both have their place. What's usually wrong is forcing one pattern to do both jobs.

For this implementation, Azati used a Java/Spring Boot backend, GraphQL APIs through Hasura, PostgreSQL, React, and a domain-restricted LLM orchestration layer that targets semantic search within a specific engineering discipline.

LLM behavior is controlled through backend orchestration: requests are routed through governed services, searches are restricted to the relevant engineering domain, timeouts and retries are managed centrally, and responses are persisted for audit. Users never interact with the model directly.

Yes. The same workflow-orchestration pattern used for a single discipline can be extended to additional disciplines and historical project portfolios, because domain restriction, request lifecycle management, and enterprise integration are handled by the platform layer rather than the AI layer.

Engagement & delivery

Azati worked as an embedded engineering partner using a Kanban delivery model, delivering the AI-assisted engineering search capability within one month.

The engagement required rapid immersion into a specialized oil & gas engineering domain, adaptation to evolving requirements, and integration with the client's enterprise infrastructure. The solution was subsequently showcased in the client's internal AI innovation demonstration program, and the collaboration continued after delivery.

Security & AI governance

Enterprise AI was introduced without creating a separate unmanaged AI layer around the engineering platform. Azati routed AI requests through controlled backend orchestration, with domain-restricted retrieval, request tracking, response lifecycle management, timeout handling, and governed integration with the existing enterprise environment.

This gave the client a controlled way to introduce AI into engineering workflows while preserving existing governance and operational requirements.

Security & governance:

  • Enterprise AI governance
  • Controlled LLM orchestration
  • Domain-restricted semantic retrieval
  • Request lifecycle management
  • Response persistence
  • Secure enterprise integration

If any of these match your roadmap, this case study is for you:

  • Enterprise AI implementation
  • Embedded AI integration
  • AI-assisted engineering review
  • Engineering knowledge management
  • Semantic search & knowledge retrieval
  • LLM integration & orchestration
  • Enterprise AI architecture
  • AI-powered decision support
  • Capital project software
  • Oil & gas software development
  • Digital engineering platforms
  • Enterprise software modernization

Azati's related expertise in enterprise AI implementation and engineering review

Get a snapshot of our recent projects and see how Azati delivers measurable results for our clients.

Enterprise Engineering Project Governance Platform Modernization
Engineering & Manufacturing Energy, Oil & Gas

Enterprise Engineering Project Governance Platform Modernization

6 core engineering governance capabilities modernized
3 new enterprise reporting and document export formats introduced
Enterprise secure engineering environment with role-based information governance
  • Spring
  • Java
  • Angular
  • GraphQL
  • Keycloak
  • Engineering project governance

Business challenge

A large industrial enterprise needed to modernize its established engineering project management platform to improve project visibility, document governance, and workflow efficiency as project complexity grew. Rather than replacing the system, the client required incremental enhancements to strengthen information management and collaboration while maintaining operational continuity.

Solution at a glance

Azati modernized the enterprise project governance platform by improving information management, workflows, and access control while integrating AI-assisted engineering knowledge discovery. This created a more transparent, collaborative, and scalable foundation for future digital transformation.

How Azati solved the challenge

  • Centralized engineering project governance: Enhanced project portfolio management by improving visibility into engineering requirements, project readiness, ownership, and maturity assessments, enabling stakeholders to monitor investment projects from a single platform.
  • Engineering document management: Modernized document workflows by improving document generation, exports, information sharing, and structured management of engineering documentation throughout the project lifecycle.
  • Collaborative review and approval workflows: Expanded expert review processes, engineering remark management, comment workflows, and project approvals, helping engineering teams coordinate reviews and resolve issues more efficiently.
  • Enterprise information governance: Strengthened role-based access management and secure information governance to ensure controlled access to engineering documentation, project information, and approval processes within a secure enterprise environment.
  • Continuous platform modernization: Stabilized inherited functionality, resolved legacy document generation limitations, introduced AI-assisted remark similarity analysis, and continued platform evolution without disrupting active engineering projects.

Business outcome

  • Better engineering project visibility: Provided engineering teams and management with centralized access to project information, documentation, readiness indicators, and approval status, improving transparency across investment initiatives.
  • More efficient engineering collaboration: Enhanced review workflows, comment management, and approval processes, reducing coordination effort between engineering experts and accelerating project preparation activities.
  • Stronger document governance: Improved document generation, reporting, and information management, enabling more consistent engineering documentation throughout complex project lifecycles.
  • Secure and scalable enterprise platform: Strengthened information governance through role-based access control while modernizing the platform architecture to support future functional expansion and enterprise growth.
  • Sustainable long-term modernization: Demonstrated how enterprise engineering platforms can evolve incrementally without costly replacement projects, allowing the organization to continuously improve project governance while maintaining operational continuity.
Full case study
AI Process Orchestration for Claims Workflow
Banking & Finance

AI Process Orchestration for a Legacy Claims Workflow

3X better case throughput
61% faster case resolution
100% of AI decisions with a full audit trail
  • Python
  • Elasticsearch
  • OpenAI
  • Oracle DB
  • REST APIs

Business challenge

The loan amendment process at this regional bank was a mess. Case officers had too much on their plates: juggling three legacy systems, with tons of manual effort every time the work changed hands. Most of their routine consisted of pulling data and double-checking details between systems, while they could actually review cases and make decisions. Add to this the restriction to automate anything without messing with the main platforms.

Solution at a glance

Azati developed AI process automation middleware that connects all three existing platforms, using APIs and RPA bridges to manage the data flow. The AI handles and packages each case from start to finish, enabling human officers to still make the final call on any regulated decisions. The team kicked off automation with one standard case type and, as the compliance team got more comfortable, rolled out support for new case types every quarter.

How Azati solved the challenge

  • Connects multiple systems using APIs and RPA to keep everything in sync.
  • Screens eligibility with both rules and machine learning.",
  • Keeps humans involved wherever the rules say they’re needed—no skipping the important checkpoints.
  • Rolls out automation gradually, case by case, so changes aren’t overwhelming.

Business outcome

  • Pulls data from different sources, double-checks it, and puts everything together for each case.
  • Uses AI to help judge eligibility and risk, and flags anything fishy for review.
  • Routes approvals automatically and tracks progress to make sure nothing falls behind.
  • Keeps an unchangeable audit log for every case, ready to hand over if regulators ask.
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A Secure LLM for Enhanced Information Sharing
Professional Services

Secure Enterprise LLM Platform for Internal Knowledge Management

100% corporate data processed within private infrastructure
60% faster access to internal business information
75% reduction in employee search time
  • vLLM
  • Open WebUI
  • Enterprise LLMs
  • Retrieval-Augmented Generation (RAG)
  • Private AI infrastructure
  • Generative AI

Business challenge

Organizations increasingly want to use generative AI to improve employee productivity, but public LLM services create security, compliance, and confidentiality concerns. The client needed an enterprise AI assistant that could securely provide employees with fast access to internal knowledge while keeping sensitive business information entirely within the corporate environment.

Solution at a glance

Azati developed a secure, locally hosted enterprise LLM platform that combines private AI infrastructure, Retrieval-Augmented Generation (RAG), and enterprise knowledge integration. The solution delivers ChatGPT-like capabilities while maintaining full control over corporate data, supporting secure information retrieval and AI-assisted knowledge sharing across the organization.

How Azati solved the challenge

  • Private enterprise AI deployment: Deployed and optimized open-source large language models within the client's internal infrastructure, eliminating dependence on public AI services while ensuring complete control over sensitive corporate information.
  • Enterprise knowledge integration: Integrated the platform with internal knowledge sources, business data, and enterprise workflows, enabling employees to retrieve accurate, context-aware information from trusted corporate resources.
  • Advanced AI retrieval and reasoning: Enhanced Retrieval-Augmented Generation (RAG) using multi-query processing, contextual retrieval, keyword extraction, topic identification, and AI-assisted query expansion to improve response quality and search accuracy.
  • AI optimization for enterprise infrastructure: Applied model optimization and efficient deployment techniques to reduce computational requirements while maintaining high-quality AI responses suitable for enterprise environments.
  • Enterprise adoption and enablement: Delivered employee onboarding and best-practice guidance to support safe adoption of generative AI across the organization and maximize productivity improvements.

Business outcome

  • Secure enterprise AI adoption: Enabled employees to use generative AI capabilities without exposing confidential business information to external cloud services, supporting corporate security and compliance requirements.
  • Faster knowledge discovery: Reduced the time required to locate internal information by providing employees with conversational, context-aware access to enterprise knowledge.
  • Higher workforce productivity: Improved day-to-day efficiency by automating information retrieval and reducing time spent searching across multiple internal systems and documentation sources.
  • Flexible enterprise AI platform: Established a private AI foundation that can evolve alongside organizational knowledge, business processes, and future enterprise AI initiatives.
  • Sustainable AI transformation: Demonstrated how organizations can introduce generative AI into business operations while maintaining full ownership of their data, infrastructure, and governance.
Full case study
AI workflow automation for invoice and document processing
Insurance

AI workflow automation for invoice and document processing

85% docs processed autonomously
52% lower cost per processed doc
<90sec average end-to-end processing time
  • Python
  • Azure
  • PostgreSQL
  • Apache Kafka
  • Kubernetes

Business challenge

A shared mission-critical service center processed 40,000+ documents monthly, yet relied on manual review and error-prone legacy OCR. The workflows were drowned in manual effort and SLA delays. The challenge was to keep the existing SAP and document management infrastructure. No replacing, no rebuilds.

Solution at a glance

Azati crafted AI workflow automation middleware that integrates with the legacy SAP and DMS infrastructure through pre-built connectors. The project’s scope was to build and operate the solution from scratch, so Azati owns extraction accuracy, uptime SLA, and non-stop improvement as the core delivery model, not optional maintenance.

How Azati solved the challenge

  • Multi-format doc ingestion and classification
  • AI-assisted field extraction with confidence scoring
  • Human-in-the-loop workflow for uncertain decisions
  • AI process automation, including monthly costs and accuracy reports

Business outcome

  • Multi-channel doc ingestion capability (PDF, TIFF, DOCX, XML, EDI)
  • SAP REST API integration using master data matching
  • Document-level immutable audit trail with GDPR compliance
  • Operations dashboard with cost per document visibility
Explore the full case
AI Agents for Software Development Lifecycle Automation in Insurance
Insurance

Enterprise AI Agents for Software Development Lifecycle Automation

5 AI agents designed to automate engineering workflows across the SDLC
12,000+ employees supported by the enterprise engineering environment
12 months from solution design to production-ready AI agent specifications
  • Node.js
  • Azure DevOps
  • Kubernetes
  • OpenAI
  • PostgreSQL
  • Redis
  • Enterprise AI engineering

Business challenge

A large insurance enterprise wanted to reduce the engineering effort spent on repetitive software development tasks while maintaining the security, governance, and approval processes required in a regulated environment. The solution needed to integrate directly with existing Azure DevOps workflows, operate within the corporate infrastructure, and comply with strict information security policies.

Solution at a glance

Azati designed an enterprise AI agent ecosystem that automates routine software engineering activities across the development lifecycle. The solution combines secure LLM deployment, Azure DevOps integration, intelligent request routing, and AI-powered development assistants to improve engineering productivity without disrupting existing SDLC processes.

How Azati solved the challenge

  • Enterprise AI infrastructure: Designed a secure AI architecture that deploys open-source language models within the client's own infrastructure, allowing engineers to use generative AI while keeping proprietary code, documentation, and development data inside the corporate environment.
  • AI-powered code review: Developed an intelligent pull request review agent that analyzes code quality, security issues, and development standards directly within Azure DevOps, enabling engineers to identify potential problems before human review.
  • Automated software testing: Designed an AI agent that generates unit tests based on application code, improving test coverage consistency while reducing the manual effort required during feature development.
  • Requirements validation: Implemented an AI-driven specification verification process that compares software implementation with business requirements stored in internal documentation systems, helping detect inconsistencies earlier in the development lifecycle.
  • Intelligent deployment assistance: Created an AI deployment assistant that analyzes release context, references internal runbooks and deployment history, and provides contextual guidance to engineers during production deployments.

Business outcome

  • Faster software delivery: Reduced the manual effort associated with code reviews, test creation, requirements verification, and deployment preparation, allowing engineering teams to focus on higher-value development work.
  • AI integrated into existing workflows: Embedded AI capabilities directly into Azure DevOps, enabling developers to benefit from intelligent automation without changing established engineering processes.
  • Secure enterprise AI adoption: Established a production-ready AI architecture that satisfies enterprise security requirements by keeping sensitive source code, documentation, and development assets within the organization's infrastructure.
  • Foundation for AI-assisted engineering: Created a scalable architecture for expanding AI automation across additional software development activities while leveraging the client's existing DevOps ecosystem.
  • Enterprise-ready delivery model: Successfully navigated architecture governance, information security reviews, and stakeholder approvals to deliver AI capabilities suitable for a highly regulated insurance environment.
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