boardroom-map/README.md

Boardroom Map — private board-deck grading on your DGX Sparks

Boardroom Map is a StartOS service (.s9pk) that grades portfolio-company board decks with a panel of local LLMs on your NVIDIA DGX Sparks. Drop each company's deck into inbox/<company-slug>/; the panel grades it against the BDEF v1.1 framework (Girdley + Munger/Buffett), an optional local adjudicator reconciles the panel, and a deterministic Python scorer computes a 0–100 composite that lands on the company's running scorecard ledger. A web dashboard shows per-company trends. There is no frontier model and no cloud API key — in the default air-gapped mode the decks and their grades never leave your hardware.

It is a sibling of Chambers and reuses the same control-plane pattern (a GPU-free orchestrator on StartOS driving the Sparks over SSH), but swaps the free-form document-review panel for a deterministic deck-grading pipeline with pinned KPI targets and per-company ledgers.

The scoring model

composite (0–100) = quant 60 + qual 40 − red flags (capped at 15)

• Quantitative 60: profitability KPI attainment 30 (heaviest slice), other measurable KPIs 20, forecast integrity 10 — deck N's actuals are chained against deck N−1's stated targets, so moved goalposts cost points. KPI credit is linear above a floor ratio (default 0.5 → zero credit below).
• Qualitative 40: eight BDEF categories (A–H) × 5 points, scored by the panel with evidence quotes; thin evidence scales down.
• Red flags: up to −15; silently dropped KPIs are auto-flagged (capped), and flags raised by a single grader are damped by 0.5.

Every knob lives in config (weights, per-company pinnedTargets and kpiAliases) so the model can be retuned without a rebuild.

Architecture

StartOS box (control plane, no GPU)            DGX Spark(s)
┌────────────────────────────────────┐         ┌───────────────────────────────┐
│ FastAPI dashboard + job runner      │  SSH    │ per-job Docker network        │
│  • inbox/<company-slug>/ (decks)    │ ───────▶│   (──internal in airgapped)   │
│  • extract text (PDF/PPTX/DOCX/…)   │  rsync  │  ┌─────────┐  ┌────────────┐  │
│  • plan model "waves"               │ ───────▶│  │ vLLM(s) │◀─│ LiteLLM     │  │
│  • extractor → graders → adjudicator│         │  └─────────┘  │ router      │  │
│  • deterministic composite scorer   │◀─────── │  ┌──────────────┐  ▲         │  │
│  • per-company ledgers + scorecards │  rsync  │  │ grader ×N    │──┘         │  │
└────────────────────────────────────┘         │  │ (read-only,  │            │  │
                                                │  │  sandboxed)  │            │  │
                                                │  └──────────────┘            │  │
                                                └───────────────────────────────┘

• Graders 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.
• Air-gap modes: airgapped (default — graders reach only the on-Spark model proxy, zero egress, models pre-pulled) or local_services (graders may reach LAN services like SearXNG and the second Spark — has egress unless firewalled).
• Confidentiality: decks are extracted to text on the StartOS box; only text crosses to the Sparks, and it is wiped from the Sparks after the job. Scorecards and ledgers live only on the StartOS box.

Setup order

Configure Sparks → Test Spark Connection → Configure Models → Configure Graders → Configure Grading (rubric, air-gap, weights) → Configure Companies (slugs, KPI aliases, pinned targets — especially profitability thresholds) → drop decks into inbox/<company-slug>/ → Grade Decks → watch the dashboard.

Two operational notes:

• Deck filenames must contain the period with the year — e.g. Board Deck - Q4 2025.pdf parses; a bare Q4 does not. Periods are canonicalized (2025-Q4, 2026-H1, 2026-05, FY2026) and drive the forecast-target chaining between consecutive decks.
• If a Spark already runs resident vLLM containers, list their names in Configure Grading → preJobStopContainers — they are docker-stopped on the head Spark at job start to free GPU memory (and deliberately not restarted afterward; re-warm them from whatever job owns them).

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 dashboard + JSON API
  jobs.py           the job runner (extract → serve waves → grade → adjudicate → score)
  serving.py        vLLM + LiteLLM router on the Sparks, in waves
  graders.py        launch the grading panel
  adjudicator.py    the local lead grader
  extraction.py     PDF/PPTX/DOCX/TXT/MD → text (on the StartOS box)
  preflight.py      probe models before launching graders
  spark_client.py   SSH/rsync helpers
  bdef.md           the baked-in BDEF v1.1 rubric
  bm_config.py      config defaults (mirrors startos/file-models/config.ts)
sandbox/            grader image (built ON the Spark, not packed in the s9pk)
  grader_agent.py grader.Dockerfile build.sh

Build

Local build with start-cli (on a Mac: colima + start-cli in a VM — see the s9pk-build-on-mac recipe). The vLLM and grader images are built on the Sparks, not packed into the .s9pk:

• boardroom-vllm:latest — the vLLM serving image with ENTRYPOINT [] (serving.py passes the full vllm serve … command as the container CMD).
• boardroom-grader:latest — from sandbox/build.sh.

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

Canonical repo: https://gitea.ten31.ai/Ten31AI/boardroom-map.

Status

v0.1.8 — live in production. Deployed on a StartOS box driving a DGX Spark in single-spark air-gapped mode (gemma-4-31B panel: munger-lens / girdley-operator / buffett-owner). First full grading run completed 2026-07-29: a three-deck company history graded end-to-end into a running ledger with quarter-over-quarter forecast chaining.

Version history: v0.1.1 fixed config persistence (absolute /media/startos/… paths — relative paths resolved into an ephemeral cwd); v0.1.2 added preJobStopContainers; v0.1.3 hardened preflight (authed model probes, poll-until-loaded, crash fast-fail); v0.1.4 fixed air-gapped serving (HF_HUB_OFFLINE — the --internal network has no DNS) and raised grader timeouts for ~3.6 tok/s local generation; v0.1.5 fixed the dashboard viewer (stays open across background refresh) and added report/JSON/scorecard downloads; v0.1.6 made reports human-friendly — rendered markdown in the dashboard, an at-a-glance strip per deck report, and a concise "At a glance" summary table atop every generated DECK_REPORT.md; v0.1.7 added company deletion from the dashboard (wipes the graded history, optionally restores the graded deck files to the inbox) so an evaluation can be re-run from scratch with a different model panel; v0.1.8 added the two-Spark pipeline — secondary-Spark models work in air-gapped mode (dual-homed proxy; graders keep zero egress), extraction and grading run in parallel when their models sit on different Sparks, and single-wave jobs keep the vLLMs warm across decks instead of reloading the 31B (~6 min) per deck.

Known optimization not yet done: the wave is torn down per deck, so the 31B reloads from disk (~6 min) between decks even when the model set is unchanged.

HF model pre-pull on the head Spark is required for air-gapped runs.