Estimation platform for an enterprise bank
A five-agent supervisor turns unstructured requirements and intake tickets into effort-scored plans, with SME review and an immutable audit trail.
- Result
- 80%
less manual estimation effort - Role
- Led design and development
- Domain
- Enterprise bank
- Period
- 2025
Problem
Project estimates started from long, unstructured business requirements documents and intake tickets. Estimating was slow, inconsistent between estimators, and hard to audit.
What I built
- A LangGraph supervisor coordinating research, estimation and two wizard agents, with typed state, rollback and an SME-in-the-loop review step.
- A production regression model (Azure ML, MLflow) as the baseline, with LLM adjustment, confidence intervals and citations.
- A bronze/silver/gold lakehouse, an immutable Postgres audit log, enterprise SSO and two-way Jira sync.
Outcome
- Manual estimation effort reduced by about 80%.