Industry Practices
AI agent programs for operations- and engineering-heavy work
Five practices carry our deepest reference architectures — healthcare, semiconductor, predictive maintenance, process automation and document operations. They are where we start, not where we stop: the same delivery model applies to materials simulation, architecture and AEC, digital building twins, and any other domain where the work is technical, exception-heavy or spread across systems that do not talk to each other.
▸ WHAT A NAMED AGENT IS
Each vertical has a named agent system — a reusable architecture, evaluation harness, and integration pattern we have built for that domain. It is not a product you license. Every engagement starts from one of these and is rebuilt around your data, your systems, and your compliance constraints. You own the deployment and the source.
Not one of the five? The delivery model is the same — tell us the workflow.
Pick the operation that looks like yours
Each practice page carries the agent architecture, the systems it connects to, how the work is phased, and where the published baselines sit.
Healthcare
MedAgent
Give clinicians their documentation time back
Clinical support and documentation acceleration for high-throughput care teams.
Connects to
Semiconductor
ChipSense
Find the defect and its root cause before the lot ships
Defect detection and yield optimization for advanced manufacturing lines.
Connects to
Predictive Maintenance
MaintainAI
Catch equipment failure before it stops the line
Failure prediction and maintenance scheduling for asset-heavy operations.
Connects to
Process Automation
FlowAgent
Handle the exceptions your RPA bots hand back
Cross-system workflow orchestration with exception handling and approvals.
Connects to
Document Intelligence
DocuMind
Turn unstructured documents into checked, structured data
Extraction, classification, and decision support across unstructured content.
Connects to
Beyond the five
The practices above are where our reference architectures are deepest — they are not the limit of what we build. Multi-agent systems are a method, not an industry, and the same delivery model applies wherever the work is technical, exception-heavy, or spread across systems that do not talk to each other.
- Materials simulation
- Architecture & AEC
- Digital building twins
Every practice is delivered the same way
The domain knowledge changes. The method, the artifacts, and the handover do not.
Diagnose
Map workflows, constraints, and baseline KPIs to identify highest-value opportunities.
Output — Bottleneck map, KPI baseline, and ranked opportunity list
Design
Define agent roles, escalation logic, data pathways, and governance controls.
Output — Agent architecture, rollout blueprint, and pilot scope
Deploy
Launch into production with integration, evaluation, and enablement for frontline and leadership teams.
Output — Go-live plan with measurable milestones and named owners
Optimize
Track performance, reduce costs, and improve quality through ongoing tuning cycles.
Output — KPI dashboard and tracked gains in speed, quality, and cost
Engagement models, what each phase includes, and the published price ranges are set out on pricing, and the governance and control model on governance.
Bring one workflow you want fixed
Send us the workflow, its monthly volume, and the systems it touches. We will tell you whether an agent system is the right answer for it, the agent shape we would propose, and roughly what it would take.
No commitment · We reply within one business day
▸ WHAT THE FIRST CALL COVERS
- The workflow as it runs today, and where it breaks
- Whether an agent system is the right tool — including when it is not
- The agent shape we would propose, and the systems it would touch
- An engagement range and what we would need from your team