Automation
Compressing weeks of solution estimation into a day
A Python and Streamlit workflow made detailed effort, cost and resource estimates faster to produce and easier to review.
- Industry
- Enterprise Data Services
- Focus area
- Automation
- Headline result
- ~1 day
- Headline result
- ~1 day
- Reduced turnaround from 1.5–2 weeks to around 1 day
Context
Complex data engagements required granular effort, resource and cost estimates before proposals could move forward.
The challenge
The manual workflow took approximately 1.5–2 weeks and created avoidable back-and-forth between customer-facing and delivery teams.
My role
Designed the estimation logic, translated it into a guided application and aligned the output to proposal and review workflows.
Approach
Decomposed estimation into reusable rules
Built guided inputs and validations
Automated resource loading and cost calculations
Produced review-ready outputs
Process
Model the existing workflow
Define inputs and guardrails
Build and validate the tool
Embed it into solutioning practice
Outcome
- Reduced turnaround from 1.5–2 weeks to around 1 day
- Made assumptions visible and reviewable
- Improved reuse across solutioning work