AI systems for complex operations
We design, build, and run AI systems inside real operational workflows. From diagnosis to production and beyond.
Workflows we typically work on
- Order, quote, and request processing
- Document-intensive workflows
- Service and support operations
- Approval and exception management
- Cross-system operational coordination
- Reporting and reconciliation
- Customer onboarding and case handling
We usually start where execution friction is high, the business case is clear, and there's a realistic path to production.
Trusted LLM ecosystem
The real problem details
Pilots stay pilots.
Teams test use cases. Almost none make it into daily operations.
Data and workflows are fragmented.
Knowledge sits in silos. Process logic lives in people's heads, not in systems you can audit.
Nobody owns what happens next.
Without metrics and operating routines, AI adoption stalls right after the first wins.
From diagnosis to production and operations.
Four stages, each with a concrete deliverable. Diagnosis, prioritization, implementation, then governance.
Diagnose
We map the operation end to end -- workflows, handoffs, bottlenecks, data dependencies, decision latency. We establish a baseline before suggesting anything.
Deliverable: operational diagnostic with bottleneck map and opportunity backlog.
Prioritize
We score each opportunity on business value, implementation effort, data readiness, and change complexity. Ideas become a ranked roadmap with clear ROI expectations.
Deliverable: prioritized roadmap with MVP sequence and expected impact per initiative.
Implement
We ship production MVPs tied to actual workflows, not isolated demos. Every system is built to run inside your operation from day one.
Deliverable: production MVPs integrated with real workflows and connected to operational data.
Govern and Scale
We set up governance cadence, KPI tracking, and optimization routines. What works scales across teams and business units.
Deliverable: governance model with KPI ownership and a plan to scale what's working.
Strategy and execution stay together.
The team that defines the roadmap also builds and runs the systems. No advisory decks without execution behind them.
Decisions are ROI-first.
Every initiative gets scored against measurable business outcomes before we commit resources to it.
Governance is built in, not added later.
We track adoption, quality, and impact from day one. That's how results outlast the initial launch.
Arc inside
IATM
Arc is our AI orchestration product. Inside IATM, it's the execution layer -- the thing that turns strategy into running systems.
Agents, data flows, workflows, integrations, controls -- all configured for your operation. Not forced into rigid templates.
Opportunity Scoring
Rank initiatives by ROI, effort, and feasibility. Before building anything.
Operational Baseline
Map processes, data, and bottlenecks before touching anything.
MVP Delivery
Ship production MVPs connected to real workflows.
Architecture Layer
Integrations, channels, and orchestration -- one model for all of it.
Governance and KPI
Track adoption, quality, and business impact continuously.
Need a practical path to AI operations?
The 22-page AI Operations Playbook covers use case prioritization, governance setup, and the path from pilot to production.
Download the playbookSoftware and product engineers. 20+ years running complex technology initiatives across the Americas. We work with leadership teams at mid-sized companies and large enterprises that need AI execution -- not experimentation theater.
One focus: measurable change in operations, with clear accountability.
Have an operational workflow worth transforming?
If the use case is viable, we'll build a working prototype in 72 hours. No cost.