Risk Intelligence Copilot

CoCo CLI Hackathon · GCC Edition · Team Behelit · Arun Kumar (solo)

✨ What it does

Evidence, not guesses	Fixed, allow-listed SQL pulls the transaction rows. User text is sent as a bound value (separate from the SQL), never pasted into the query.
Law by meaning	AI_EMBED turns each rule clause into 768 numbers; cosine similarity picks the TOP-3 clauses for the question. Cortex Search (hybrid search) is optional.
A draft with receipts	AI_COMPLETE (claude-sonnet-5, fallback llama3.1-8b) writes the SAR with the Cortex Guard harm filter always on. Every draft is fingerprinted with SHA-256.
A human signs	Only users in APP_OFFICERS can approve. The check runs on the server at click time. Denied tries are logged under the real viewer's name.

🚀 Try it

1 · In your browser (no install, no keys). Open the live demo.

• Pick analyst or officer and press a quick query.
• Demo mode uses fake sample data and a template draft (no AI call), and sends nothing to Snowflake.
• The first load takes 20–60 seconds while Python starts in the browser.

2 · On your machine.

git clone https://github.com/Arunkumar06-cmd/risk-intelligence-copilot.git && cd risk-intelligence-copilot
pip install -r requirements.txt -r requirements-dev.txt

DEMO_MODE=TRUE streamlit run streamlit_app.py      # demo mode, no keys needed
# or, for the real Snowflake path:
cp .streamlit/secrets.toml.example .streamlit/secrets.toml   # add your Snowflake keys + APP_PASSWORD
streamlit run streamlit_app.py

3 · Inside Snowflake.

• In Snowsight (the Snowflake web UI), run sql/01_setup.sql, then sql/03_rate_limit.sql, then sql/02_sis_deploy.sql.
• Full steps and grants are in docs/ARCHITECTURE.md.

🛠️ Ecosystem & tooling

🧭 How it works

flowchart LR
    Q([English question]) --> G[Input guard<br/>+ rate limit]
    G --> R[Intent router<br/>whole-word]
    R --> E[(Evidence SQL<br/>bound values)]
    R --> L[(Law search<br/>AI_EMBED TOP-3)]
    E --> P[Prompt builder<br/>≤350 words]
    L --> P
    P --> C[AI_COMPLETE<br/>guardrails ON]
    C --> D[Draft SAR<br/>+ SHA-256]
    D --> O{{Officer gate<br/>APP_OFFICERS}}
    O -->|approve| A[(AUDIT_LOGS<br/>APPROVED)]
    O -->|blocked| X[(AUDIT_LOGS<br/>APPROVE_DENIED)]

The plain-words version, with the real SQL and numbers behind each box, is in the Feynman explainer.

📊 Engineered metrics

Metric	Value	Where it is enforced
Law clauses per question	3	config.TOP_K
Evidence rows per draft	5	config.EVIDENCE_TX_LIMIT
AI calls without the harm filter	0	AI_COMPLETE_SQL sets {'guardrails': TRUE}; no unguarded retry
Max question length · prompt size	2000 chars · 350 words	MAX_INPUT_LEN, MAX_PROMPT_WORDS
Rate limits	10 / min / session · 50 / hour / user	check_rate, RATE_LIMIT table (fails closed)
Automated tests	102 (no Snowflake needed)	tests/

📁 Project structure

.
├── streamlit_app.py            # UI: login, KPI strip, chat, evidence inspector, officer sign-off
├── backend/
│   ├── hybrid_agent.py         # guard → router → evidence SQL → law search → AI draft → officer gate → audit
│   ├── runtime.py              # where am I running? local · Snowflake (SiS) · viewer identity
│   ├── demo.py                 # demo mode: sample rows, sample clauses, template draft
│   └── config.py               # settings, model allow-lists, limits (single source of truth)
├── sql/                        # run in order: 01 setup · 02 SiS deploy · 03 rate limit · 04 optional Cortex Search
├── tests/                      # 102 pytest checks, fake database only
├── site/index.html             # GitHub Pages shell that runs the app in the browser (stlite)
├── docs/
│   ├── ARCHITECTURE.md         # deep reference: pipeline, models, SiS deploy, security model, cost
│   ├── context.html            # Feynman explainer (also live on Pages)
│   ├── deck/                   # hackathon slides (.pptx)
│   └── assets/                 # screenshots
├── .github/                    # CI, security, release, Pages workflows · issue/PR templates · Dependabot
├── context.md                  # handoff notes for the next engineer or agent
├── CHANGELOG.md · CONTRIBUTING.md · SECURITY.md · LICENSE
└── pyproject.toml · requirements.txt · requirements-dev.txt · .pre-commit-config.yaml · .editorconfig

📚 Documentation

Read this	When you want
Feynman explainer	The idea in plain words, then the real machinery
docs/ARCHITECTURE.md	Models, Cortex Search, the Snowflake deploy, security model, cost
context.md	Handoff notes and decisions that must not regress
CONTRIBUTING.md	Setup, checks to run, commit format
SECURITY.md	How to report a hole, the scans that run, the access model
CHANGELOG.md · Releases	What changed in each version

🔐 Security at a glance

• Inputs: a character allow-list, blocked SQL words, 2000-character cap, bound values only, and integer-only LIMIT/OFFSET.
• AI: Cortex Guard is always on, temperature 0, and only allow-listed models can be called.
• People:
  • Inside Snowflake, approval is checked per viewer against APP_OFFICERS.
  • RESTRICTED accounts are hidden from analysts, and customer names are masked.
• Output: tags are stripped before display, and the export never contains SQL.
• Honest limit: not yet run on a live Snowflake account. Every path is covered by fake-database tests and checked against the Snowflake docs. See ARCHITECTURE.md.

👤 Team

Behelit: Arun Kumar (@Arunkumar06-cmd), solo build for the CoCo CLI Hackathon, GCC Edition.

License

MIT