AI Operations Platform
Routine triage and data entry run on their own, giving the ops team hours back every week.
- Sector
- AI & Automation · Operations · AI agents
- Built with
- LLMs
- RAG
- Python
- APIs
01 · Discover
What the client needed
An operations team spent much of each day on the same steps: reading inbound emails and documents, typing data into systems, and chasing follow-ups.
Requirements
- Triage inbound email by intent and urgency
- Extract data from PDFs, invoices, and forms into existing systems
- Agents that act through the tools the team already uses
- A person approves anything risky or unclear
- A full log of what the AI did and why
- Customer data kept private and within agreed regions
Constraints
- No replacing the existing CRM and back-office tools
- Accuracy had to be measured before anything ran on its own
- Costs per task kept predictable
02 · Pitch
What we proposed
We picked the highest-volume, lowest-risk tasks to automate first, and agreed how accuracy would be measured before building.
Options we weighed
What we agreed
- Start with email triage and document intake
- An evaluation set from real, anonymised examples
- Confidence thresholds that route uncertain cases to a person
03 · Build
How we built it
Each agent has a narrow job, a set of tools it may use, and tests it must pass before release.
Every requirement, and what we built for it
| Requirement | What we built |
|---|---|
| Email triage | Classification by intent and urgency, routed to the right queue with a suggested reply |
| Document data entry | Extraction from PDFs and scans, validated against business rules before it is saved |
| Work in existing tools | Agents that call the team’s CRM and back-office APIs through scoped permissions |
| People stay in control | Approval steps for higher-risk actions and anything below a confidence threshold |
| Full audit trail | Every input, tool call, and decision logged and searchable |
| Privacy | PII redaction before model calls and data kept in agreed regions |
Platform and architecture
- Agents
- Python services using LLM tool calling
- Knowledge
- Retrieval (RAG) over policies and past cases for grounded answers
- Quality
- Evaluation suite run on every change, plus guardrails on outputs
- Integrations
- Email, document storage, CRM, and back-office APIs
04 · Handover
Launch and handover
Agents went live in suggestion mode first, then took over tasks one by one as their accuracy held.
- Shadow mode: agents suggest, people decide
- Gradual move to automatic for proven tasks
- Review screen and training for the ops team
- Monitoring of accuracy, cost, and response time
05 · Outcome
The outcome
Routine triage and data entry run on their own, giving the ops team hours back every week.
Routine triage and data entry run on their own, and the team handles the cases that need judgement.
- Hours back every week for the operations team
- Faster first responses to inbound requests
- Every automated action is logged and reviewable
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