RevOps teams lose weeks to BI provisioning and thousands per seat to licenses they barely use. Sales Intel Terminal replaces PowerBI and Tableau for pipeline analytics — deployed in minutes, running on your infrastructure, querying millions of rows in sub-second time.
Watch the Walkthrough · Deploy in 5 Minutes · See the Architecture
Sales.Intel.Dashboard.mp4
Live filtering on a 500K-row pipeline, the Rep × Region heatmap, and stage-to-deal drill-down — in under 90 seconds.
Proof, not promises. Every screen below renders against a live in-memory query — no cached dashboards, no overnight refresh.
|
KPI Overview 
Bookings, Revenue Velocity, Win Rate, and MoM growth — rendered in under 200ms. |
Rep × Region Heatmap 
Coverage gaps and top performers, surfaced instantly across every territory. |
Pipeline Funnel 
Stage-by-stage conversion and drop-off, computed live on every filter change. |
The interface is built glass-morphism first — layered depth and translucency signal exactly what modern BI tooling should feel like. For the full interaction model, see the Product Walkthrough.
The math is simple: every week spent on Gateway configuration and workspace permissions is a week your reps close deals without visibility into what's working.
| Sales Intel Terminal | PowerBI / Tableau | |
|---|---|---|
| Infrastructure | Zero. One container, one process. | Gateway, workspace admin, dedicated BI engineer. |
| Licensing | $0 per seat. Self-hosted, open-core. | $10–$70+/user/month, compounding with headcount. |
| Query Latency | Sub-second on multi-million-row datasets. | Import/DirectQuery models buckle under pipeline-scale exports. |
| Time to First Insight | Minutes. Drop in a CSV, query immediately. | Weeks. Data modeling, gateway config, permissioning. |
| Deployment | One command — Docker, internal server, or Streamlit Cloud. | Enterprise IT provisioning and procurement approval required. |
| Extensibility | Full Python access — ship custom metrics in code. | Locked to DAX and vendor-defined visual limits. |
Bottom line: enterprise-grade analytical depth, zero procurement friction, a fraction of the compute cost.
📊 Pipeline Funnel Analytics Stage-by-stage conversion from lead to closed-won, with automatic drop-off detection that pinpoints exactly where deals stall.
⚡ Revenue Velocity Cycle time × win rate × ACV ÷ duration — one metric that quantifies pipeline health, not just pipeline size.
🎯 Win Rate Intelligence Segment by rep, region, industry, deal size, or source. Isolate the variables that actually correlate with closed-won.
🚀 DuckDB-Powered Speed In-memory SQL against Polars DataFrames. Interactive filtering and cross-tabulation on multi-million-row datasets with no perceptible lag and no external warehouse.
🔥 Rep × Region Heatmaps Performance density across the entire go-to-market org — coverage gaps and enablement priorities, visible at a glance.
🔐 Self-Hosted & Data-Sovereign Pipeline data never leaves your infrastructure. No third-party ingestion, no data-sharing agreements. Includes secure session gating and a 1-Click Demo Mode for frictionless stakeholder reviews.
Stateless, in-memory compute. No database to provision, back up, or patch.
┌─────────────────┐ ┌──────────────────┐ ┌───────────────────┐
│ Data Sources │ │ Ingestion & │ │ Compute Layer │
│ │─────▶│ Normalization │─────▶│ │
│ CSV / Excel / │ │ (Polars) │ │ DuckDB (in-memory │
│ CRM Export │ │ │ │ SQL execution) │
└─────────────────┘ └──────────────────┘ └─────────┬──────────┘
│
▼
┌───────────────────┐
│ Presentation │
│ Layer │
│ │
│ Streamlit + Plotly│
│ (Interactive UI) │
└───────────────────┘
- Polars ingests and normalizes source data via a Rust-backed columnar engine — 5–10x faster than pandas on typical CRM exports.
- DuckDB executes every analytical query directly against in-memory Polars frames through zero-copy Arrow interop. No ETL to a separate warehouse required.
- Streamlit + Plotly render the interaction layer. Every filter and drill-down triggers a live DuckDB query — never a pre-baked static report.
- No external database. State lives in memory for the session; source-of-truth data stays wherever your team already keeps it.
Python 3.10, 3.11, or 3.12 · pip · Docker (optional)
git clone https://github.com/Ali-datasmith/Sales-Intel-Dashboard.git
cd Sales-Intel-Dashboard
pip install -r requirements.txt
streamlit run app.pyAvailable at http://localhost:8501.
Already deployed and live. If you want to fork this repository and host your own private instance for your sales team:
- Fork this repository to your GitHub account.
- Go to share.streamlit.io and sign in with GitHub.
- Click New app, select your forked repo, and set the main file path to
app.py. - Streamlit will automatically read
requirements.txtand deploy your dashboard in under 5 minutes.
Sales Intel Terminal is designed for zero-friction ingestion. Simply export your pipeline data from Salesforce, HubSpot, or any CRM as a CSV or Excel file.
- Launch the application.
- Use the secure 1-Click Demo Mode or authenticate via the enterprise login gate.
- Drag and drop your CRM export into the sidebar ingestion panel.
- The Polars engine normalizes and caches the dataset in memory instantly.
Deploys behind your existing reverse proxy / SSO layer — no additional licensing:
streamlit run app.py \
--server.port 8501 \
--server.address 0.0.0.0 \
--server.enableCORS falseEvery commit runs through GitHub Actions: linting, type checks, and unit tests across Python 3.10, 3.11, and 3.12. See .github/workflows/ci.yml.
MIT License. See LICENSE.
Sales Intel Terminal — Pipeline visibility, without the platform tax.