boardroom-map/README.md

Boardroom Map — a private document-review panel for your DGX Sparks

Boardroom Map is a StartOS service (.s9pk) that lets you drop confidential documents in and have a panel of local LLMs review them on your NVIDIA DGX Sparks. You pick the models and the personas (lenses), and how many reviews to run; each reviewer writes a report, and an optional local lead reviewer synthesizes them into one consolidated report. There is no frontier model and no cloud API key — in the default air-gapped mode the documents and reviews never leave your hardware.

It is a sibling of Nightshift and reuses the same control-plane pattern (a GPU-free orchestrator on StartOS driving the Sparks over SSH), but with the swarm, the git blackboard, and the Claude overseer removed and replaced by an on-demand document-review pipeline.

Architecture

StartOS box (control plane, no GPU)            DGX Spark(s)
┌────────────────────────────────────┐         ┌───────────────────────────────┐
│ FastAPI web UI + job runner         │  SSH    │ per-job Docker network        │
│  • inbox (drop documents)           │ ───────▶│   (──internal in airgapped)   │
│  • extract text (PDF/DOCX/TXT/MD)   │  rsync  │  ┌─────────┐  ┌────────────┐  │
│  • plan model "waves"               │ ───────▶│  │ vLLM(s) │◀─│ LiteLLM     │  │
│  • launch reviewer containers       │         │  └─────────┘  │ router      │  │
│  • pull reports, synthesize, wipe   │◀─────── │  ┌──────────────┐  ▲         │  │
│  • reports saved here only          │  rsync  │  │ reviewer ×N  │──┘         │  │
└────────────────────────────────────┘         │  │ (read-only,  │            │  │
                                                │  │  sandboxed)  │            │  │
                                                │  └──────────────┘            │  │
                                                └───────────────────────────────┘

• Reviewers are one-shot, read-only, hardened containers (non-root, --cap-drop ALL, read-only rootfs, no docker socket). In air-gapped mode they sit on an --internal network and can reach only the model proxy.
• Waves: the job runner serves models in waves bounded by maxConcurrentModels, so a panel can span more models than fit in GPU memory at once.
• Confidentiality: documents are extracted to text on the StartOS box; only text crosses to the Sparks, and it is wiped from the Sparks after the job.

Repo layout

startos/            StartOS package definition (TypeScript / start-sdk)
  manifest/ main.ts interfaces.ts versions/ file-models/ actions/
orchestrator/       The control-plane app (Python)
  app.py            FastAPI UI + JSON API
  jobs.py           the job runner (extract → serve waves → review → synthesize)
  serving.py        vLLM + LiteLLM router on the Sparks, in waves
  reviewers.py      launch the reviewer panel
  synthesis.py      the local lead reviewer
  extraction.py     PDF/DOCX/TXT/MD → text (on the StartOS box)
  preflight.py      probe models before launching reviewers
  spark_client.py   SSH/rsync helpers
  bm_config.py      config defaults (mirrors startos/file-models/config.ts)
sandbox/            reviewer image (built ON the Spark, not packed in the s9pk)
  grader_agent.py reviewer.Dockerfile build.sh
openclaw/           what each Spark needs provisioned (OpenClaw's job)

Build

Same path as Nightshift — GitHub CI (.github/workflows/build.yml) or a local build with start-cli (see the s9pk-build-on-mac recipe). The vLLM and reviewer images are built on the Sparks, not packed into the .s9pk.

npm ci && npm run check && npm run build   # type-check + bundle
make                                        # pack the .s9pk (needs start-cli)

Status

v0.1 — source complete, tsc-clean and Python-syntax-clean. Not yet validated against live Sparks. See openclaw/OPENCLAW_SPEC.md for the Spark-side provisioning (HF model pre-pull is required for air-gapped runs).