Define the agent job
Beat, owner, entity scope, watch conditions, outputs, and escalation rules. A named role — not a chatbot.
← How we engage
Extends Managed OI
Custom Agents
Packaged analyst packs cover the common beats. When your operation needs a department- or process-specific agent — with custom actions and approvals — we design and run it as part of Managed Operational Intelligence.
The question
What would you assign to a perfect analyst if you could hire one for this exact department or process?
We translate the need into an agent role: what it watches, what it knows, what it may draft or write back, and where a human must approve — then we operate it inside your OI engagement.
What this adds to OI
A custom agent with a defined beat, data access, tool policy, approval model, and production outputs — extending who watches what in your operation.
In practice
What this looks like for a real client.
A mid-sized manufacturer runs a complex order book across multiple lines. Every morning the ops director needs at-risk orders, overrun work orders, material cover, and late supplier shipments — before the meeting. Staffing that analyst is impossible. We design an Operations Analyst agent on the typed workspace: it watches the beat, drafts the morning briefing, writes back updated promise dates where policy allows, and holds customer updates for one-tap approval. The same offering covers quality/CAPA, estimating & margin, or a department-specific agent your packaged pack does not include.
How it works
Designed as part of your Managed OI engagement.
Build knowledge and context
Domain rules, company language, thresholds, and examples the agent needs to reason usefully.
Design tools and approvals
What it can read, draft, publish, write back, or only recommend. External and financial actions require explicit human approval.
Pilot inside Managed OI
First briefing, alert, or action queue with client review. We tune and stay on as the operation changes.
Role of AI
Not a generic chatbot. Specific techniques, applied to specific data.
The agent owns a beat — not “answer any question.” It reasons over typed Entities against configured conditions. Tool grants are deny-by-default; consequential actions stay held for approval. Confirmed decisions sharpen the next cycle.
Selected engagements
Not slides. Shipped work, running in production.
Manufacturing
Manufacturing Operations Analyst Agent
Watches orders, work orders, inventory, and shipments continuously
Daily morning briefing auto-drafted; at-risk orders flagged with updated promise dates. Ops acted before the meeting — without hiring an analyst for the beat.
Pharma
Secondary Sales Enrichment Agent
4-stage matching: exact code → lookup cache → fuzzy search → LLM assist
Product match from ~70% to 94%+. Self-learning from human confirmations — downstream OI and finance finally trusted the same SKU truth.
Pharma / CPG
Scheme Claim Validation Agent
150,000–200,000 invoices/month with AI audit pipeline
Fraudulent and duplicate claims flagged before settlement. A 40-person manual queue became exception-only work — leakage caught while money could still be held.
What you get
Shipped into your OI engagement
- Agent role brief: beat, goals, data scope, and outputs
- Knowledge pack specific to the client operation
- Tool and approval policy for governed action
- Pilot runs with review notes and tuning backlog
What we need
What we need from your team to begin
- Named business owner for the agent beat
- Examples of good and bad decisions in the workflow
- Data access and records the agent should watch
- Approval rules for external, financial, or system-changing actions
Timeline
A practical delivery cadence, not an open-ended discovery.
Role and beat design
Agent workshop, sample cases, output definition, and approval model. Spec by Friday.
Agent build and context tuning
Knowledge, workflow, data access, tool policy, and test runs on representative scenarios.
Pilot cycle
First production briefing, alert, or action queue with review and behaviour tuning.
Industries
Where we have delivered this service.
Manufacturing
OTIF operations, quality & CAPA, supplier risk, reliability, estimating & margin
Financial Services
Reconciliation, compliance monitoring, portfolio risk agents
CPG & Pharma
Trade scheme compliance, distributor performance, stock-out prediction
Good fit
When this service is the right answer
- Beats that need continuous attention but cannot justify a dedicated hire
- Department- or process-specific agents beyond the packaged packs
- Teams that want AI capacity with explicit control over consequential actions
Honest call
When this is not the right choice
- You want a generic AI assistant to chat with — that is a Copilot, not an agent
- The beat has no repeatable data signals
- Your team is not ready to define what “good” looks like for decisions the agent will prepare
Start the engagement
Start with the beat that needs constant attention but cannot justify a hire.
We scope this as part of Managed Operational Intelligence — deliver a working result in weeks, and stay on so the picture keeps getting sharper after go-live.