RAISEDECK

Build and maintain an investor-update + data-room manifest from a metrics YAML, rendering monthly MRR/burn/runway updates with consistent KPIs.

Part of the Cognis Neural Suite.

pip install cognis-raisedeck
raisedeck scan .            # → prioritized findings in seconds

Usage — step by step

1. Install (Python 3.9+):

   pip install raisedeck            # or: pipx install raisedeck

2. Render an investor update from a metrics YAML file:

   raisedeck render metrics.yaml

3. Report a specific month. By default the latest month is used; pin it with --period:

   raisedeck render metrics.yaml --period 2026-02

4. Get machine-readable output for further processing or templating:

   raisedeck render metrics.yaml --format json > update.json

5. Read the result. The default output is a formatted investor update (growth, runway, and KPI deltas vs. the prior period); JSON mode emits the same figures as structured data.

6. Wire it into a workflow. Regenerate the monthly update from the canonical metrics file:

   raisedeck render metrics.yaml --period 2026-02 > updates/2026-02.txt

Contents

• Why raisedeck? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why raisedeck?

Investor relations as a reproducible monthly artifact — KPIs are computed from source data, so every update is internally consistent and version-controlled.

raisedeck is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

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Features

• ✅ Parse Yaml
• ✅ Load Metrics
• ✅ Compute Update
• ✅ Render Table
• ✅ Render Json
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

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Quick start

pip install cognis-raisedeck
raisedeck --version
raisedeck scan .                       # scan current project
raisedeck scan . --format json         # machine-readable
raisedeck scan . --fail-on high        # CI gate (non-zero exit)

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Example

$ raisedeck scan .
  [HIGH    ] RAI-001  example finding             (./src/app.py)
  [MEDIUM  ] RAI-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

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Architecture

flowchart LR
  IN[capture / scan] --> P[raisedeck<br/>parse + map]
  P --> OUT[report]

Loading

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Use it from any AI stack

raisedeck is interoperable with every popular way of using AI:

• MCP server — raisedeck mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe raisedeck scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

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How it compares

	Cognis raisedeck	Visible.vc + Carta updates, rendered via Quarto
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Built in the spirit of Visible.vc + Carta updates, rendered via Quarto, re-framed the Cognis way. Missing a credit? Open a PR.

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Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (raisedeck mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

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Install — every way, every platform

pip install "git+https://github.com/cognis-digital/raisedeck.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/raisedeck.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/raisedeck.git" # uv
pip install cognis-raisedeck                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/raisedeck:latest --help        # Docker
brew install cognis-digital/tap/raisedeck                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/raisedeck/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/raisedeck	DEPLOY.md (AWS/Azure/GCP/k8s)

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Related Cognis tools

• warmline — Score and rank inbound/outbound leads from a YAML rulebook, emitting a ranked queue as JSON/CSV for your SDRs and CI gates.
• coldforge — Render personalized cold-outreach sequences from Markdown templates + a contacts CSV, with spam-score linting and per-send dry-run preview.
• pactgen — Generate branded sales proposals and SOWs from a YAML scope file + pricing table into PDF/HTML, with a deterministic line-item math check.
• crmsync — Bidirectional, idempotent sync of contacts/deals between a local SQLite source-of-truth and CRM APIs (HubSpot/Pipedrive/Salesforce) via one config.
• dripcheck — Lint email sequences and drip campaigns for deliverability: SPF/DKIM/DMARC, link health, unsubscribe presence, and CAN-SPAM/GDPR compliance.
• dealflow — Model your sales pipeline as a YAML state machine and compute conversion rates, stage velocity, and weighted forecast straight from CRM exports.

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

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Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If raisedeck saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license (licensing@cognis.digital). See LICENSE.

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_{Cognis Digital · one of 170+ tools in the Cognis Neural Suite · Making Tomorrow Better Today}