Dynamic prompt engineering
User-defined thresholds get injected into AI prompts at runtime via Python template strings. Teams customize escalation rules without touching code.
Case study
CRA Enterprise AI
A full-stack AI automation platform that turns a four hour manual case review into a twenty five minute intelligent workflow. Analyzes the portfolio, recommends escalations with reasoning, tracks sentiment, and delivers customer-ready Excel reports automatically.
- 93.75% Time reduction
- 51 Features shipped
- 1 week Build time
- $648K Annual value at 50 users
Why now
The problem
Enterprise support teams managing 80 to 120 active cases were spending 16 plus hours a month on the same repetitive review work. Reading case notes. Making escalation decisions one by one. Formatting spreadsheets. Writing the same email to twelve different customers. The pattern was obvious. The automation was not there.
Manual
- 4 hours per 100-case review
- 25 cases reviewed per hour
- 15 to 20% false positive rate
- 0% sentiment tracking
- Documentation rarely complete
Automated
- 25 minutes per 100-case review
- 240 cases reviewed per hour
- 8% false positive rate
- 100% sentiment tracking
- Always documented, full audit trail
Development ROI of 7,200%. Breaks even on the first run, which is twenty five minutes.
How it works
Four stages, one workflow.
- 01
Portfolio analysis
Accept case portfolio exports from enterprise case management dashboards. Pick from eight review templates. Configure custom escalation thresholds per team. Output is a structured AI input ready for bulk processing.
- 02
AI-powered review
Score every case against the configured criteria. Produce escalation recommendations with reasoning, classify customer sentiment, detect communication gaps based on last customer update, and assign next-step ownership across Support, Product Group, Customer, or Account Team.
- 03
Report generation
Parse AI responses with regex pattern matching, apply filters across search, escalation status, age, and customer, then generate customer-separated Excel reports with conditional formatting. Sentiment drives emoji sizing, escalation drives color, age drives a traffic light.
- 04
Distribution and analytics
Pre-filled Outlook drafts via COM automation, one per customer. Editable review grid for human-in-the-loop edits before final output. Metrics dashboard with time saved, value delivered, and trend charts. Structured CSV export for Power BI or Tableau.
Innovations
Key technical innovations
The parts that made this small enough to ship in a week and durable enough to run in production.
Intelligent response parsing
Regex-based extraction handles varied AI response formats and missing fields gracefully. 97% parsing success across 847 production cases.
Conditional Excel formatting
Context-aware styling reads data values and applies formatting automatically. The report itself communicates priority before anyone reads a word.
Session state management
Streamlit session state preserves multi-step workflow across UI interactions so users can edit, filter, and email without regenerating reports.
Desktop integration via COM
Web app connects to native Outlook through pywin32 to create pre-filled drafts. Privacy-first by construction. Nothing leaves the machine until the user clicks Send.
Tech stack
Built on tools that already live in the enterprise
- Streamlit Browser UI
- Pandas Portfolio manipulation and merging
- OpenPyXL Formatted Excel output
- Plotly Interactive metrics dashboard
- Microsoft Copilot DfM Bulk case analysis
- pywin32 Outlook draft generation through COM
- Python 3.12 Application runtime
Screens
Production screens
Sanitized production screenshots and a short demo video are next on the list. The executive summary PDF and full feature set are available in the repository today.
Streamlit portfolio analysis view with template selection.
Editable review grid with sentiment, escalation, and ownership columns.
Customer-separated Excel report with conditional formatting.
Domain expertise paired with agentic methods.
Built in one week using agentic AI development. I directed AI tools to implement features while keeping architectural ownership, product decisions, and quality validation in human hands. Four versions, fifty one features, roughly 950 lines of production code.
The point of the project is not the code. The point is that domain expertise paired with agentic methods can produce production-ready enterprise software faster than traditional approaches, without giving up the parts that matter, like privacy, human oversight, and graceful degradation.
Closer look
Want a closer look?
Happy to walk through the architecture, run a live demo, or talk about what a similar build would look like for your team.