Smart Opportunity Finder

A self-hosted, multi-agent system that scans public MedTech signals (patents, clinical trials, regulatory filings, funding rounds, academic literature, grants) and surfaces a short, ranked list of growth opportunities matched to a company's manufacturing competencies.

The output answers two questions per opportunity:

• Why us? — deterministic match against the company's competency model
• Why now? — derived from timing proxies in the public record (trial phase transitions, patent grant windows, regulatory clearance events, funding rounds)

The hard invariant: no claim without traceable evidence. A trust gate in Python forces any claim with no retrieved evidence to unsupported, regardless of what the LLM said.

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Quick start

cp .env.example .env          # fill in at least one provider key (or set DRY_RUN=true)
docker compose up -d
# UI:    http://localhost:3000
# API:   http://localhost:3000/api/v1
# Docs:  http://localhost:3000/api/docs   (nginx-proxied OpenAPI)

DRY_RUN=true runs the full pipeline without any LLM call (deterministic placeholder JSON). Useful for CI and offline demos.

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Architecture

flowchart TD
    User([Browser SPA]) -->|HTTPS| API[FastAPI]
    API --> Pipe{{OpportunityOrchestrator}}

    Pipe --> S1[1. PLAN<br/>LLM]
    S1 --> S2[2. RESEARCH<br/>deterministic fan-out]
    S2 -->|parallel| A1[ClinicalTrials]
    S2 -->|parallel| A2[Patents — Google & EPO]
    S2 -->|parallel| A3[Regulatory — openFDA]
    S2 -->|parallel| A4[Funding — SEC EDGAR]
    S2 -->|parallel| A5[Academic — OpenAlex & PubMed]
    S2 -->|parallel| A6[Grants — CORDIS & NIH]
    A1 & A2 & A3 & A4 & A5 & A6 --> S3[3. CHECK<br/>LLM + Trust Gate]
    S3 --> S4[4. MATCH<br/>Competency Matcher]
    S4 --> S5[5. TIME<br/>rules + LLM rationale]
    S5 --> S6[6. RANK<br/>weighted blend]
    S6 --> S7[7. BRIEF<br/>LLM]
    S7 --> S8[8. CRITIQUE<br/>LLM quality tier]
    S8 --> Out[Ranked Briefing]

    API -.->|persistence| DB[(SQLite / Postgres)]
    API -.->|embeddings| QD[(Qdrant)]
    API -.-> LLM[UnifiedLLM<br/>Anthropic / OpenAI / DeepSeek / Ollama]

Loading

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The 8-step pipeline

Entry point: core/orchestrator/opportunity_flow.py::OpportunityOrchestrator.run().

Step	Function	Notes
1. PLAN	step_plan	LLM decomposes the focus area into atomic claims with competency_hint and search_terms.
2. RESEARCH	_research	Parallel fan-out across all registered SourceAdapter instances (20 s per-adapter timeout).
3. CHECK	step_check	LLM verifies each claim against retrieved evidence. The Python trust gate (enforce_trust_gate) overwrites any verified verdict that has no evidence. Coherence pass halves confidence when invented identifiers (NCT IDs, DOIs, dollar amounts) are detected.
4. MATCH	step_match	Deterministic competency match using competencies/schott.yaml. Below MATCH_THRESHOLD=0.20 is dropped.
5. TIME	step_time	Rules + LLM rationale. Maps timing signals to {NOW, 2_YEARS, 5_YEARS, EMERGING} with a [0,1] urgency score. Rules in scoring/timing_rules.yaml.
6. RANK	core/scoring/ranking.rank	Weighted blend (see below). Top 5 returned.
7. BRIEF	step_brief	LLM writes the final card; coherence pass downgrades confidence if invented refs are spotted.
8. CRITIQUE	step_critique	LLM quality grade. Critique score < 0.4 or blocking issues → card dropped.

Trust gate (the invariant)

def enforce_trust_gate(verdict: str, evidence: list[EvidenceItem]) -> str:
    if not evidence and verdict == "verified":
        return "unsupported"
    return verdict

Source: core/orchestrator/opportunity_flow.py.

Scoring formula

$$\text{score} = \frac{\sum_i w_i \cdot s_i}{\sum_i w_i}$$

Axis	Default weight	Source
competency_fit	0.40	matcher score
timing	0.30	TIME step urgency
signal_strength	0.15	min(len(evidence)/10, 1.0) × coherence
market_potential	0.10	CHECK confidence × coherence
competitive_window	0.05	default 0.6, tunable per opportunity

Weights renormalise if they don't sum to 1.0. All five RANK_W_* overrides come from environment variables — no code change needed.

Competency matching

core/scoring/matcher.py. Fully deterministic — no embeddings, no LLM. Four signals blended into one [0,1] score against each competency in competencies/schott.yaml:

Signal	Weight	How
Keyword overlap	50%	Fraction present; substring match for multi-word terms
Application overlap	30%	Phrase hit = 1.0; partial token overlap ≤ 0.5
Material match	20%	Exact / prefix / order-agnostic token-set; generic tokens excluded
Negative-keyword penalty	subtractive	Caps at 30%

Results memoised with a 1 024-entry LRU keyed on (text_hash, competencies_id).

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Source adapters

All registered in core/orchestrator/adapter_factory.py::build_default_adapters. Adapters that need a key skip themselves if their env var is empty.

Adapter	Source	Notes
ClinicalTrialsAdapter	clinicaltrials.gov v2	Graceful token-limit degradation
GooglePatentsAdapter	Google Patents (scraper)	Requires pip install -e .[patents]
EpoOpsAdapter	EPO Open Patent Services	Skipped without EPO_OPS_API_KEY
OpenFDADevicesAdapter	openFDA devices	FDA 510(k) / PMA clearances
OpenFDADrugAdapter	openFDA drug	NDA / ANDA approvals
SecEdgarAdapter	SEC EDGAR full-text	Funding / M&A signals
CordisAdapter	EU CORDIS	Horizon grants
NihReporterAdapter	NIH Reporter	US NIH grants
OpenAlexAdapter	OpenAlex	250M+ academic works
PubmedAdapter	PubMed / MEDLINE	Biomedical literature
EuClinicalTrialsAdapter	EudraCT	EU trial registrations

A new source = subclass core.sources.base.SourceAdapter, return EvidenceItems, register in build_default_adapters.

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LLM routing

providers/unified.py::UnifiedLLM. Three cost tiers configured independently:

Tier	Default model	Used by
quality	claude-sonnet-4-20250514	PLAN, BRIEF, CRITIQUE
balanced	gpt-4o-mini	CHECK
cheap	deepseek-chat	TIME rationale, fallback

Runtime guarantees:

• Circuit breakers per provider (pybreaker) — a failing provider trips for the rest of the request.
• Health cache with 60 s TTL — unhealthy providers are skipped without a probe call.
• Budget guard (providers/budget.py) — pre-call cost estimate, skipped if it would exceed MAX_BUDGET_USD.
• Fallback chain — UnifiedLLM tries every configured provider in priority order before raising.
• Ollama passthrough — QUALITY_PROVIDER=ollama + OLLAMA_HOST point at any on-prem instance.
• DRY_RUN=true — returns deterministic placeholder JSON, no API call.

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API reference

apps/api/main.py. Base path served by nginx at /api/v1/.

Method	Route	Description
GET	/health	Liveness check
POST	/api/v1/runs	Start a new opportunity scan (returns immediately, async)
GET	/api/v1/runs/{run_id}	Fetch run status + result
GET	/api/v1/runs/	List recent runs
GET	/api/v1/events/{run_id}	SSE stream of pipeline stage transitions
POST	/api/v1/schedules	Create a cron schedule
GET	/api/v1/schedules	List schedules
DELETE	/api/v1/schedules/{id}	Delete a schedule
POST	/api/v1/schedules/{id}/trigger	Fire a schedule once, outside cron cadence

The React SPA renders the SSE events as live scan progress. Pages that only read from /api/v1/runs (Dashboard, OpportunityDeepDive, StrategicBets, RadarFeed) show an empty state until a scan completes.

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Database schema

core/persistence.py. SQLAlchemy 2.x. SQLite by default; Postgres via DATABASE_URL.

runs — one row per pipeline execution:

Column	Type	Notes
run_id	UUID PK
status	enum	pending / running / done / error
focus_area	text	User-supplied query
created_at / completed_at	timestamp
current_stage	text	Last pipeline stage name
result	JSON	Full RankedBrief payload
error	text	Traceback on failure
triggered_by	text	manual or schedule
schedule_id	FK → schedules	Nullable

schedules — cron-triggered scans:

Column	Type
schedule_id	UUID PK
name, focus_area, cron, enabled	text / text / text / bool
created_at, last_run_at, last_run_id	timestamps + FK

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Frontend

frontend/ — React 18 + Vite + TypeScript + Tailwind, served as a static SPA by the nginx layer in the API container.

Path	Page	Backed by
/	Dashboard	GET /runs
/scan/:runId	ScanMonitor	GET /runs/:id + SSE /events/:id
/opportunity/:id	OpportunityDeepDive	GET /runs (client-side lookup)
/bets	StrategicBets	GET /runs
/radar	RadarFeed	GET /runs
/schedules	Schedules	GET/POST/DELETE /schedules
/research	ResearchAgent	Ad-hoc manual query (client-side)
/competencies	Competencies	YAML editor (client-side)
/admin	Admin	Settings UI (client-side)
/login	Login	sessionStorage gate using VITE_AUTH_USERS

The auth gate is a static credential check (set VITE_AUTH_USERS=user:pass,... in frontend/.env) — there is no backend auth endpoint yet.

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Install (without Docker)

git clone https://github.com/W00DSRULES/launch.git
cd launch
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env

# backend
uvicorn apps.api.main:app --reload
# frontend (separate terminal)
cd frontend && npm install && npm run dev

Python 3.11+ required. SQLite + an embedded Qdrant are used by default — zero external services needed for a first run.

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Develop

make test         # pytest
make lint         # ruff + black
make typecheck    # mypy on core/ + providers/
make format       # ruff --fix + black

Pre-commit hooks (.pre-commit-config.yaml) run ruff + black on every commit.

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License

MIT — see LICENSE.