{"id":50454102,"url":"https://github.com/mizcausevic-dev/briefing-intelligence-engine","last_synced_at":"2026-06-01T01:05:38.886Z","repository":{"id":356617798,"uuid":"1233330881","full_name":"mizcausevic-dev/briefing-intelligence-engine","owner":"mizcausevic-dev","description":"Python + FastAPI intelligence engine for executive briefing scoring, narrative generation, risk ranking, and action sequencing.","archived":false,"fork":false,"pushed_at":"2026-05-17T05:12:13.000Z","size":47,"stargazers_count":0,"open_issues_count":8,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-05-17T07:26:08.595Z","etag":null,"topics":["analytics","decision-engine","fastapi","openapi","pandas","pydantic","python"],"latest_commit_sha":null,"homepage":"https://kineticgain.com/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mizcausevic-dev.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2026-05-08T21:00:20.000Z","updated_at":"2026-05-17T05:12:15.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/mizcausevic-dev/briefing-intelligence-engine","commit_stats":null,"previous_names":["mizcausevic-dev/briefing-intelligence-engine"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mizcausevic-dev/briefing-intelligence-engine","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mizcausevic-dev%2Fbriefing-intelligence-engine","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mizcausevic-dev%2Fbriefing-intelligence-engine/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mizcausevic-dev%2Fbriefing-intelligence-engine/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mizcausevic-dev%2Fbriefing-intelligence-engine/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mizcausevic-dev","download_url":"https://codeload.github.com/mizcausevic-dev/briefing-intelligence-engine/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mizcausevic-dev%2Fbriefing-intelligence-engine/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33755379,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-05-31T02:00:06.040Z","response_time":95,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analytics","decision-engine","fastapi","openapi","pandas","pydantic","python"],"created_at":"2026-06-01T01:05:38.809Z","updated_at":"2026-06-01T01:05:38.867Z","avatar_url":"https://github.com/mizcausevic-dev.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Briefing Intelligence Engine\r\n\r\n![Hero](screenshots/01-hero.svg)\r\n\r\n## Executive Summary\r\n\r\nBriefing Intelligence Engine is a recruiter-ready Python + FastAPI backend that\r\nturns fragmented operating signals into executive briefing outputs. It ingests\r\nrevenue, growth, security, AI, customer, and operations inputs, scores them with\r\nPandas-backed logic, then emits narrative summaries and prioritized next actions.\r\n\r\n## Recruiter Takeaway\r\n\r\nThis project is designed to show:\r\n\r\n- Python backend depth beyond TypeScript-heavy portfolio work\r\n- FastAPI and Pydantic modeling for production-style API design\r\n- Pandas-backed scoring and prioritization logic\r\n- how executive reporting systems can be engineered, not just mocked up\r\n\r\n## Tech Stack\r\n\r\n[![Python](https://img.shields.io/badge/Python-3.14-111827?style=for-the-badge\u0026logo=python\u0026logoColor=FFD54F\u0026labelColor=111827\u0026color=1F6FEB)](https://www.python.org/)\r\n[![FastAPI](https://img.shields.io/badge/FastAPI-API-111827?style=for-the-badge\u0026logo=fastapi\u0026logoColor=44E1B6\u0026labelColor=111827\u0026color=0F766E)](https://fastapi.tiangolo.com/)\r\n[![Pydantic](https://img.shields.io/badge/Pydantic-Validation-111827?style=for-the-badge\u0026logo=pydantic\u0026logoColor=F9A8D4\u0026labelColor=111827\u0026color=7C3AED)](https://docs.pydantic.dev/)\r\n[![Pandas](https://img.shields.io/badge/Pandas-Scoring-111827?style=for-the-badge\u0026logo=pandas\u0026logoColor=F4E7B3\u0026labelColor=111827\u0026color=4338CA)](https://pandas.pydata.org/)\r\n[![Pytest](https://img.shields.io/badge/Pytest-Tested-111827?style=for-the-badge\u0026logo=pytest\u0026logoColor=FBBF24\u0026labelColor=111827\u0026color=7C2D12)](https://docs.pytest.org/)\r\n[![License](https://img.shields.io/badge/License-MIT-111827?style=for-the-badge\u0026logo=opensourceinitiative\u0026logoColor=E5E7EB\u0026labelColor=111827\u0026color=84CC16)](https://opensource.org/license/mit)\r\n\r\n## Overview\r\n\r\n| Area | What it shows |\r\n| --- | --- |\r\n| Signal modeling | Executive briefing inputs across revenue, growth, AI, security, ops, and customer domains |\r\n| Scoring engine | Pandas-backed pressure, urgency, and gap-to-target calculation |\r\n| Narrative outputs | Executive summary, shifts to watch, opportunity framing, risk framing |\r\n| Action sequencing | Priority-ranked operational next steps with ownership and due dates |\r\n| API surface | FastAPI routes with automatic OpenAPI docs at `/docs` |\r\n\r\n## Architecture\r\n\r\n```mermaid\r\nflowchart LR\r\n  P[\"Signal payloads\"] --\u003e V[\"Pydantic validation\"]\r\n  V --\u003e F[\"Pandas scoring frame\"]\r\n  F --\u003e N[\"Narrative generation\"]\r\n  F --\u003e A[\"Action prioritization\"]\r\n  N --\u003e R[\"Briefing-ready API response\"]\r\n  A --\u003e R\r\n```\r\n\r\n## Executive Briefing Workflow\r\n\r\n1. A client sends a structured briefing payload with multi-domain signals.\r\n2. FastAPI validates the request through Pydantic.\r\n3. The engine normalizes signals into a Pandas DataFrame.\r\n4. Weighted pressure, target gaps, and confidence produce a composite score.\r\n5. The service returns a status, headline, why-it-matters points, and next actions.\r\n\r\n## Sample Request\r\n\r\n```json\r\n{\r\n  \"briefing_id\": \"briefing-demo-q2\",\r\n  \"account_name\": \"Northstar Holdings\",\r\n  \"audience\": \"executive\",\r\n  \"time_horizon\": \"monthly\",\r\n  \"signals\": [\r\n    {\r\n      \"signal_id\": \"rev-pipeline-01\",\r\n      \"domain\": \"revenue\",\r\n      \"title\": \"Pipeline coverage compression\",\r\n      \"metric\": \"coverage\",\r\n      \"current_value\": 2.1,\r\n      \"previous_value\": 2.8,\r\n      \"target_value\": 3.0,\r\n      \"impact_weight\": 0.92,\r\n      \"confidence\": 0.86,\r\n      \"owner\": \"Revenue Operations\",\r\n      \"due_in_days\": 9,\r\n      \"note\": \"Enterprise segment slipped below planning guardrail.\"\r\n    }\r\n  ]\r\n}\r\n```\r\n\r\n## Sample Response\r\n\r\n```json\r\n{\r\n  \"status\": \"needs-attention\",\r\n  \"score\": 62,\r\n  \"headline\": \"Northstar Holdings is still recoverable, but pipeline coverage compression should lead the next operating review.\",\r\n  \"why_it_matters\": [\r\n    \"Pipeline coverage compression is off target by 0.9 on coverage.\"\r\n  ],\r\n  \"recommended_next_actions\": [\r\n    {\r\n      \"title\": \"Stabilize pipeline coverage compression\",\r\n      \"owner\": \"Revenue Operations\",\r\n      \"priority\": \"high\",\r\n      \"due_in_days\": 9,\r\n      \"rationale\": \"High weighted pressure driven by coverage, confidence 86%, and deadline pressure.\"\r\n    }\r\n  ]\r\n}\r\n```\r\n\r\n## Screenshots\r\n\r\n### Hero\r\n\r\n![Hero](screenshots/01-hero.svg)\r\n\r\n### Scoring Workflow\r\n\r\n![Workflow](screenshots/02-workflow.svg)\r\n\r\n### Priority Surface\r\n\r\n![Priorities](screenshots/03-priorities.svg)\r\n\r\n### Validation Proof\r\n\r\n![Proof](screenshots/04-proof.svg)\r\n\r\n## Setup\r\n\r\n```powershell\r\ncd briefing-intelligence-engine\r\npy -3.11 -m venv .venv\r\n.venv\\Scripts\\Activate.ps1\r\npython -m pip install -e .[dev]\r\nuvicorn app.main:app --reload\r\n```\r\n\r\nOpen:\r\n\r\n- API docs: [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)\r\n\r\n## Validation\r\n\r\n```powershell\r\ncd briefing-intelligence-engine\r\npython -m pytest\r\npython -m compileall app tests\r\n```\r\n\r\n## Portfolio Links\r\n\r\n- [Kinetic Gain](https://kineticgain.com/)\r\n- [LinkedIn](https://www.linkedin.com/in/mirzacausevic)\r\n- [Skills / Portfolio](https://mizcausevic.com/skills/)\r\n- [Medium](https://medium.com/@mizcausevic)\r\n- [GitHub](https://github.com/mizcausevic-dev)\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmizcausevic-dev%2Fbriefing-intelligence-engine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmizcausevic-dev%2Fbriefing-intelligence-engine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmizcausevic-dev%2Fbriefing-intelligence-engine/lists"}