Operations
PatternDocument-heavy intake and triage
Ingest, classify, extract and route inbound documents into the system of record, escalating what does not parse cleanly.
Production AI systems, designed as systems.
Most enterprise AI stalls because it was built as a capability rather than a system. We design the whole stack — data, models, agents, orchestration, oversight, measurement — as one thing, and stay accountable for how it performs once it is running.
The business problem
A capability is not an operating system.
Models are available and tools are accessible. What is missing is the ability to integrate AI into real workflows, systems, and decision paths without creating risk, fragility, or organizational friction. A component bought in isolation inherits none of the controls, handoffs or measurement the operation already depends on.
What that looks like
- Pilots that work on a sample and fail on the exception queue
- AI that sits beside the workflow, adding a step instead of removing one
- No audit trail, so the output cannot be relied on for a decision
- No measurement tied to throughput, cost or risk, so value cannot be proven
What Catalyze builds
What we build
One architecture, delivered through six stages, with a named owner for real-world performance.
- 01
System architecture
The full stack designed together — integration surface, data layer, model selection, agent scope, orchestration, oversight and measurement.
- 02
Exception-first design
We map failure modes before the happy path: malformed input, upstream outage, low-confidence output, disputed decision.
- 03
Integration into systems of record
The system reads and writes where the work already lives, rather than asking teams to move.
- 04
Controls and auditability
Permissions, approval gates, escalation paths and an audit trail for every automated action, designed in from day one.
- 05
Operational measurement
Instrumentation against throughput, cost, risk reduction and ROI — the terms the operation is already judged on.
Architecture
The stack we design
Every layer exists because removing it breaks something in production. This is the same architecture described on the homepage, at the level of detail an engineering team would need.
01Enterprise Systems
ERP, CRM, ITSM, core banking, clinical and supply chain systems of record.
02Data + Semantic Layer
Governed definitions, lineage and access control over the records those systems hold.
03AI Models
Selected per task, scoped, and swappable without redesigning the system around them.
04Agent Layer
Scoped actors with explicit permissions, tool boundaries and a recorded trail of actions.
05Workflow Orchestration
Routing across systems, teams and handoffs, with exception paths as first-class branches.
06Human Oversight
Approval gates, escalation and review points placed where the risk actually sits.
07Operational Outcomes
Throughput, cost, risk reduction and ROI, measured continuously rather than at go-live.
Example use cases
Where this applies
Patterns we design for. Items marked as published link to our own write-up.
Finance & Supply Chain
PatternReconciliation and exception handling
Match, flag and route breaks, so teams work the exceptions rather than the whole population.
Technology & integration
Integration surface
We integrate with the platforms you already rely on. Named systems below are ones Catalyze publicly works with; the rest are described at category level.
Named platforms
- Salesforce
- Workday
- ServiceNow
- AWS
Systems of record
- ERP
- CRM
- ITSM
- Core banking
- EHR
- WMS / TMS
Data
- Warehouse / lakehouse
- CDC + batch pipelines
- Document stores
- Vector indexes
Controls
- SSO / identity
- Role-based access
- Audit logging
- Data residency
Case studies
Proof in production.
Related insights
Our thinking on this.
Questions
What buyers ask us about AI Systems.
How is this different from an AI consulting engagement?
A consulting engagement typically ends at a recommendation or a prototype. We are accountable for a system running in production — integrated with your systems of record, governed, and measured against operational outcomes. Our last two delivery stages are Deploy and Optimize, not handover.
Do we have to replace our existing platforms?
No. The architecture is designed to embed into the platforms already running the work. Migration is occasionally the right answer, but it is a separate decision and we will say so rather than bundle it.
How do you handle model choice, given how fast the landscape moves?
Models are selected per task and kept swappable behind the system's own interfaces. Capability moves quickly; architecture is what stops you rebuilding every time it does.
What does accountability for performance actually mean?
We agree the operational measures before we build — throughput, cost per unit of work, time to detection, risk reduction — and instrument the system to report against them continuously.
Tell us about the workflow. We'll tell you whether AI Systems is the right place to start.