Document-driven workflows
Reading operational paperwork, extracting the fields that matter, and reconciling them against system records.
Generative AI
AI Agents & Process Automation
Automating multi-step operational work - reading documents, calling systems, validating results, and escalating what it should not decide alone.
Overview
Plenty of back-office work is a sequence of lookups, comparisons, and judgement calls against documents and internal systems. That shape suits an agent, provided every action is logged and anything uncertain stops for a human.
We have built this for port and logistics operations, covering disbursement accounts, port call handling, and figure validation against expected ranges. The design principle is the same each time: automate the traversal, keep the audit trail, escalate the exception.
What you receive
- Agent workflow with defined tool permissions
- Validation rule set agreed with your operations team
- Human review queue for escalated cases
- Audit log with replay of any past run
- Throughput and accuracy reporting
Typical stack
- Python
- LLM APIs
- LangGraph
- Celery
- PostgreSQL
- FastAPI
Capabilities
What this covers in practice
Tool and system integration
Agents that call your existing APIs and databases under explicit permissions, never free-form access.
Validation and escalation
Rule checks on every generated figure, with anything outside tolerance routed to a named reviewer.
Full audit trail
Every step, input, and decision recorded, so an outcome can be explained months later.
Related
Also under Generative AI
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