https://github.com/mizcausevic-dev/briefing-intelligence-engine
Python + FastAPI intelligence engine for executive briefing scoring, narrative generation, risk ranking, and action sequencing.
https://github.com/mizcausevic-dev/briefing-intelligence-engine
analytics decision-engine fastapi openapi pandas pydantic python
Last synced: 26 days ago
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Python + FastAPI intelligence engine for executive briefing scoring, narrative generation, risk ranking, and action sequencing.
- Host: GitHub
- URL: https://github.com/mizcausevic-dev/briefing-intelligence-engine
- Owner: mizcausevic-dev
- License: mit
- Created: 2026-05-08T21:00:20.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2026-05-17T05:12:13.000Z (about 1 month ago)
- Last Synced: 2026-05-17T07:26:08.595Z (about 1 month ago)
- Topics: analytics, decision-engine, fastapi, openapi, pandas, pydantic, python
- Language: Python
- Homepage: https://kineticgain.com/
- Size: 45.9 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
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README
# Briefing Intelligence Engine
## Executive Summary
Briefing Intelligence Engine is a recruiter-ready Python + FastAPI backend that
turns fragmented operating signals into executive briefing outputs. It ingests
revenue, growth, security, AI, customer, and operations inputs, scores them with
Pandas-backed logic, then emits narrative summaries and prioritized next actions.
## Recruiter Takeaway
This project is designed to show:
- Python backend depth beyond TypeScript-heavy portfolio work
- FastAPI and Pydantic modeling for production-style API design
- Pandas-backed scoring and prioritization logic
- how executive reporting systems can be engineered, not just mocked up
## Tech Stack
[](https://www.python.org/)
[](https://fastapi.tiangolo.com/)
[](https://docs.pydantic.dev/)
[](https://pandas.pydata.org/)
[](https://docs.pytest.org/)
[](https://opensource.org/license/mit)
## Overview
| Area | What it shows |
| --- | --- |
| Signal modeling | Executive briefing inputs across revenue, growth, AI, security, ops, and customer domains |
| Scoring engine | Pandas-backed pressure, urgency, and gap-to-target calculation |
| Narrative outputs | Executive summary, shifts to watch, opportunity framing, risk framing |
| Action sequencing | Priority-ranked operational next steps with ownership and due dates |
| API surface | FastAPI routes with automatic OpenAPI docs at `/docs` |
## Architecture
```mermaid
flowchart LR
P["Signal payloads"] --> V["Pydantic validation"]
V --> F["Pandas scoring frame"]
F --> N["Narrative generation"]
F --> A["Action prioritization"]
N --> R["Briefing-ready API response"]
A --> R
```
## Executive Briefing Workflow
1. A client sends a structured briefing payload with multi-domain signals.
2. FastAPI validates the request through Pydantic.
3. The engine normalizes signals into a Pandas DataFrame.
4. Weighted pressure, target gaps, and confidence produce a composite score.
5. The service returns a status, headline, why-it-matters points, and next actions.
## Sample Request
```json
{
"briefing_id": "briefing-demo-q2",
"account_name": "Northstar Holdings",
"audience": "executive",
"time_horizon": "monthly",
"signals": [
{
"signal_id": "rev-pipeline-01",
"domain": "revenue",
"title": "Pipeline coverage compression",
"metric": "coverage",
"current_value": 2.1,
"previous_value": 2.8,
"target_value": 3.0,
"impact_weight": 0.92,
"confidence": 0.86,
"owner": "Revenue Operations",
"due_in_days": 9,
"note": "Enterprise segment slipped below planning guardrail."
}
]
}
```
## Sample Response
```json
{
"status": "needs-attention",
"score": 62,
"headline": "Northstar Holdings is still recoverable, but pipeline coverage compression should lead the next operating review.",
"why_it_matters": [
"Pipeline coverage compression is off target by 0.9 on coverage."
],
"recommended_next_actions": [
{
"title": "Stabilize pipeline coverage compression",
"owner": "Revenue Operations",
"priority": "high",
"due_in_days": 9,
"rationale": "High weighted pressure driven by coverage, confidence 86%, and deadline pressure."
}
]
}
```
## Screenshots
### Hero
### Scoring Workflow
### Priority Surface
### Validation Proof
## Setup
```powershell
cd briefing-intelligence-engine
py -3.11 -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install -e .[dev]
uvicorn app.main:app --reload
```
Open:
- API docs: [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
## Validation
```powershell
cd briefing-intelligence-engine
python -m pytest
python -m compileall app tests
```
## Portfolio Links
- [Kinetic Gain](https://kineticgain.com/)
- [LinkedIn](https://www.linkedin.com/in/mirzacausevic)
- [Skills / Portfolio](https://mizcausevic.com/skills/)
- [Medium](https://medium.com/@mizcausevic)
- [GitHub](https://github.com/mizcausevic-dev)