AI That Works Inside Your Operations
We build AI agents, document intelligence and predictive models that connect to your ERP, maintenance and field systems, with approvals, audit trails and monitoring built in.
How AI Fits into Your Operations
Your documents, data and systems go in. Models and agents work over them. Answers and actions land in the tools your teams already use, with a person approving what matters.
01Data in
- Documents and procedures
- ERP and work orders
- Sensors and SCADA
- Photos and field reports
02AI capabilities built for your use case
AI Agents
Agents that take actions in your systems, such as raising work orders or updating records, with approvals and audit trails.
Grounded Assistants
Answers from your manuals, procedures, contracts and work history, with a link to the source.
Document and Image Intelligence
Data pulled from invoices, permits, inspection photos and field reports into structured records.
Copilots in Your Tools
Drafting, search and summaries inside the apps your teams already use.
Predictive Maintenance and Forecasting
Proven machine learning for failure risk, demand and production planning.
Anomaly Detection
Alerts when equipment, processes or transactions drift from normal.
03Results out
- ERP and maintenance
- MES and SCADA
- Approval queues
- Dashboards and apps
From Use Case to Production
Three phases. Each one ends with something your team can evaluate before the next begins.
Phase 1
Assess
We pick use cases with a clear business case, check data quality and access, and agree on how success is measured.
- Use cases ranked by value and risk
- Data and access review
- Success criteria and evaluation set
Phase 2
Build and Evaluate
We choose a model for each task, connect it to your data and systems, and test it against real cases before go-live.
- Model choice by task, cost and data residency
- Retrieval and system integrations
- Evaluations and guardrails
Phase 3
Deploy and Monitor
We release with human approval where it matters, monitor quality and cost in production, and extend to more sites and workflows.
- Approvals and audit trails
- Quality, cost and drift monitoring
- Rollout across sites
Where We Apply AI
Use cases we build for field, plant and supply chain teams.
Oil and Gas
Field work, equipment health and reporting.
- Agents that turn field reports and alarms into draft work orders
- Assistants grounded in procedures and well files
- Predictive maintenance for artificial lift and compression
Manufacturing
Quality, maintenance and planning on the plant floor.
- Visual quality inspection on the line
- Assistants over SOPs, work instructions and maintenance history
- Condition monitoring and production planning
Logistics
Documents, exceptions, routing and forecasting.
- Extraction from bills of lading, invoices and customs forms
- Agents that handle shipment exceptions
- Volume forecasting for staffing and inventory
Models and Tools We Build With
We pick models per task and run them where your data is allowed to go.
Capabilities
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Agents and MCP
Agents connected to your systems through APIs and MCP, acting within the permissions you set.
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Retrieval and Knowledge
Search and answers over your documents and data, with sources cited.
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Document and Image Intelligence
Extraction from forms, invoices, permits, photos and video.
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Predictive Models
Forecasting, failure risk and anomaly detection on operational data.
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Evaluation and Guardrails
Test sets, output checks and monitoring for quality, cost and drift.
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Governance and Security
Access control, PII handling, audit logs and data residency options.
Stack We Build On
AI Models
Frontier APIs or open-weight models run privately, chosen per task by quality, cost and data residency.
ML and Data
Training, evaluation and data pipelines.
Deployment
Your cloud tenant, on-premise or at the edge.
Systems We Connect To
Results go where your teams already work.
AI and Automation Questions
What operations and IT teams ask before starting an AI project.
Contact our teamAI agents that act in business systems, assistants grounded in your documents, document and image extraction, copilots inside existing tools, and proven machine learning such as predictive maintenance and forecasting. We start from an operational problem, not a model.
Agents work through defined tools and permissions. Higher-risk steps need a person to approve, and every action is logged.
It depends on the task. We use frontier APIs from providers such as OpenAI, Anthropic and Google, or open-weight models such as Llama and Mistral run privately, balancing quality, cost and data residency.
Yes. Models can run in your cloud tenant, on-premise or at the edge, and sensitive data can stay out of third-party APIs.
Through APIs, industrial protocols and the Model Context Protocol (MCP). Results land in your ERP, maintenance system, MES or mobile apps.
We build an evaluation set from real cases before go-live, then monitor quality, cost and drift in production.
We agree on a baseline and a target measure in the assessment phase and test the pilot against it before scaling.
Discuss Your AI Use Case with Our Team
Tell us which operational problem you want to solve. We will review your data and propose a first phase.
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01Discovery
We review your processes, systems and objectives.
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02Proposal
Defined scope, timeline and investment for phase one.
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03Delivery
A production release, with a roadmap to scale.