# Venture Autonomy Platform — Product Model

## Executive Summary

**Venture** is an AI-native operating system that transforms autonomous agent infrastructure into self-financing economic entities. Unlike traditional automation tools or agentic frameworks, Venture treats agent work capacity as a primary commodity—quantifying labor, monetizing output, reinvesting revenue into expanded capacity, and optimizing a portfolio of revenue-generating "ventures" with mathematical precision.

The system operates as a living economic entity: it has a body (infrastructure, agent capacity, tools), a metabolism (treasury: income, burn rate, reserves), a constitution (policy: what it may and may not do), capabilities (what kinds of work it can sell), and founder-defined objectives with time horizons. This creates a self-sustaining loop where successful ventures fund growth, policy constraints ensure safety, and audit trails provide full transparency for compliance.

**Core thesis:** Autonomous agents can generate real economic value when constrained by policy and operated like self-funding startups with explicit budget limits, revenue targets, and governance structures.

---

## Vision Statement

To enable founders to deploy AI labor as autonomous, self-optimizing economic entities that operate safely within strict policy guardrails, generate measurable revenue, and scale their own infrastructure through profitable execution—without human intervention in the critical loop.

---

## The Core Metaphor: Venture as a Living Economic Entity

### The Body (Infrastructure)

- **Agent capacity**: pool of available compute, deployed as containerized agents (copilot processes, orchestrators, researchers, solvers)
- **Tool stack**: permitted integrations (API calls, file operations, web scraping, artifact compilation, payment processing)
- **Data infrastructure**: event bus, ledger, vault, policy registry, audit log
- **Orchestration layer**: process-compose manages services, FSMs coordinate task execution

### The Metabolism (Treasury)

- **Income**: revenue generated by completing billable work (writing, code, research, data services)
- **Burn rate**: operational cost of running agents, storing data, and calling external APIs
- **Reserves**: working capital allocated by the founder, released gradually as ventures prove profitable
- **Reinvestment loop**: profitable ventures unlock capital for capacity expansion (more agents, longer execution windows, premium API tiers)

### The Will (Policy Constitution)

- **Hard constraints**: legal boundaries (jurisdictions, data handling, payment restrictions), regulatory compliance (DSAR, retention, consent)
- **Soft policies**: founder preferences (risk tolerance, venture mix, approval gates, escalation thresholds)
- **Default-deny principle**: every action requires explicit authorization before execution
- **Immutable versioning**: all policy decisions are timestamped and reproducible

### The Skills (Agent Capabilities)

- **Research & analysis**: data aggregation, competitive intelligence, market analysis
- **Content production**: written articles, documentation, technical guides, marketing copy
- **Code generation**: building libraries, scaffolding, bug fixes, refactoring
- **Artifact compilation**: slides, videos, timelines, dashboards, reports
- **GTM execution**: outreach campaigns, lead qualification, demo scheduling, contract negotiation

### The Goals (Founder Objectives)

- **Near-term**: immediate revenue to cover operational costs and demonstrate viability
- **Medium-term**: scaled venture portfolio generating consistent margin above burn
- **Long-term**: expanded infrastructure capacity funded entirely by autonomous revenue
- **Compliance**: zero policy violations, full audit coverage, continuous attestation

---

## Market Position & Competitive Landscape

### Competitive Alternatives

| Player | Mechanism | Limitations |
|--------|-----------|------------|
| **AutoGPT / AgentGPT** | Chain-of-thought agent loops | No treasury layer, no revenue model, no governance |
| **Devin / SWE-agent** | Task-focused agent chains | Single-task, not portfolio-based, no self-funding |
| **Traditional RPA tools** | Workflow orchestration | Require human definition, not generative |
| **Consulting / Staff augmentation** | Human labor markets | Requires scaling headcount, no exponential returns |
| **Crypto trading bots** | Automated trading on blockchain | Pure speculation, no real value creation |

### What Venture Does Differently

1. **Self-financing model**: Revenue from real work funds growth, not investor capital or speculation
2. **Portfolio optimization**: Treats ventures like a VC fund—allocating capital to maximize risk-adjusted returns
3. **Full governance**: Policy constitution, audit trails, kill switches—built in, not bolted on
4. **Headless execution**: No human-in-the-loop for routine decisions; founders set policy once, then observe
5. **Event sourcing**: Complete replayability and forensics for every decision and action
6. **Labor commodification**: Agent work hours are the unit of value, transparently tracked and monetized

---

## Product Surfaces (Detailed)

### 1. Policy Constitution Editor

**Purpose:** Founders define the rules the system will follow autonomously.

**Key interfaces:**
- **Policy YAML editor** with autocomplete, syntax validation, and versioning
- **Policy preview** showing rules in natural language ("This venture can spend up to $500/day on API calls for X service")
- **Conflict detection**: warns when policies contradict or create logical loops
- **Approval workflow**: policies are reviewed, signed, and locked to a git commit hash before activation

**Editable surfaces:**
- **Budget policy**: daily/weekly/monthly spend caps per venture type, currency, merchant category
- **Authorization gates**: which user roles can approve which spend levels, escalation rules
- **Tool allowlists**: which agents can use which tools (e.g., orchestrator can call API, but researcher cannot send payments)
- **Jurisdiction rules**: which countries/regions can be targeted for outreach, payment processing, data export
- **Data handling**: retention periods, DSAR workflows, do-not-sell classifications
- **Venture portfolio**: which venture types are allowed, success criteria, target revenue per venture
- **Escalation thresholds**: what events trigger alerts, freeze conditions, manual review requirements

### 2. Venture Portfolio Dashboard

**Purpose:** Founders and operations teams see the health of active ventures and treasury performance.

**Key displays:**
- **Portfolio grid**: each active venture shows: name, type, current revenue (YTD), burn rate, margin %, policy compliance status, days to fund-out
- **Treasury at a glance**: total revenue, total burn, current reserves, runway (days), next rebalancing trigger
- **Venture ranking**: ventures sorted by ROI, margin%, or risk score; highlight over-performers and at-risk ventures
- **Venture detail pane**: drill down to any venture to see: task history, client list, success metrics, policy violations (if any), recommendations to scale or pause

**Real-time metrics:**
- Revenue generation by venture type and geography
- Cost per unit output (article, code artifact, analysis report)
- Policy compliance: % of decisions approved on first pass vs escalated
- Uptime and latency of agent work
- Team utilization: how many agents are active, idle, or paused

**Actions available:**
- Launch new venture (define type, initial budget, success criteria)
- Pause/resume venture (suspends agent work without losing state)
- Reallocate budget between ventures (founder moves capital, system recalculates runway)
- Request rebalancing recommendation (system analyzes performance and suggests portfolio changes)

### 3. Workstream Orchestration Console

**Purpose:** Visibility into active agent work, DAG execution, and decision traces.

**Key displays:**
- **DAG visualization**: each workflow shows tasks as nodes, dependencies as edges; color coding for status (pending, running, completed, failed)
- **Task timeline**: Gantt-style view of execution history, showing which tasks ran in parallel, where bottlenecks occur
- **Agent assignments**: which agents are assigned to which tasks, compute time used, cost of that task
- **Decision breadcrumbs**: every high-impact decision (spend approval, outreach authorization) shows the policy rule that permitted it

**Drill-down capabilities:**
- Click any task to see: full input, output artifacts, any API calls made, costs incurred, time elapsed
- Click any decision to see: the policy rule triggered, the confidence score, any manual reviews
- Search by: workflow ID, task type, agent role, cost range, date range

**Actions available:**
- Pause workflow (all active tasks pause, can resume later)
- Kill workflow (all active tasks stop immediately, generate incident report)
- Manual override (founder can approve a pending decision, with full audit trail)
- Retry failed task (re-run with same inputs, useful for transient failures)

### 4. Treasury & Spend Firewall

**Purpose:** Enforce budget constraints in real-time and provide forensic spend visibility.

**Key displays:**
- **Real-time spend ledger**: each transaction shows: timestamp, agent/venture, spend amount, merchant/category, authorization rule, approval status
- **Budget headroom**: for each policy budget cap, show: allocated, used, available, days until reset
- **Velocity controls**: track spend rate per hour/day; alert if spending accelerates above historical baseline
- **Merchant analysis**: which vendors is the system using, how much per vendor, contract terms if known

**Authorization mechanics:**
- Default-deny: every spend attempt triggers a policy check before execution
- If approved: transaction is logged, funds are escrowed, external payment is authorized
- If denied: transaction is blocked, reason code is logged, escalation rule fires (e.g., notify finance, pause venture)
- If manual gate: transaction waits for founder approval before proceeding

**Compliance outputs:**
- Daily reconciliation: compare internal ledger with bank/processor statements; flag drift
- Monthly P&L: revenue vs burn, venture margin analysis, recommend next month's budget
- Audit export: generate compliance report with all transactions, policy versions, and approvals for regulatory review

### 5. Compliance & Audit Cockpit

**Purpose:** Operations auditors and external reviewers verify policy compliance and safety.

**Key displays:**
- **Compliance dashboard**: policy violations (if any), DSAR requests pending, data retention overages, escalation events
- **Audit trail**: searchable log of all events (policy decisions, spend approvals, artifact generation, data access) with timestamp, agent, policy version, outcome
- **Evidence chain**: for any high-impact action, generate a reproducible evidence bundle (policy version, input, output, approvals, costs)
- **Incident log**: historical incidents, classification (budget overrun, policy violation, tool misuse), remediation, postmortem status

**Query capabilities:**
- "Show all decisions made by Venture X on 2025-03-15"
- "Show all spend approvals where policy rule Y was triggered"
- "Export evidence chain for venture Z's revenue between 2025-01-01 and 2025-02-28"
- "List all DSAR requests created by this workspace in the past 90 days"

**External auditor support:**
- Read-only audit API: external auditors can access audit log and policy versions without modifying data
- Evidence export: generate zipfile with policy bundles, event logs, ledger snapshots for archival
- Attestation workflow: auditor reviews evidence and signs attestation certificate (verified timestamp, policy version, no violations found or violations documented)

---

## User Personas (Expanded)

### Persona A: Founder Governor

**Role:** Defines the policy constitution and owns the venture's strategic direction.

**Goals:**
1. Deploy autonomous agents that generate revenue without daily oversight
2. Set safe bounds (budgets, tool access, jurisdictions, escalation rules) and trust the system to operate within them
3. Monitor venture performance and reallocate capital to high-performers
4. Ensure the venture scales profitably without human intervention

**Pain points:**
- Fear of runaway spend: traditional tools offer no treasury layer, so founder must constantly monitor budgets
- Policy drift: hard to know if the system is still following the rules as intent evolves
- Black-box decisions: agents take actions, but the founder can't easily see why

**Success criteria:**
- Ventures run autonomously under policy without human approval for routine decisions
- Revenue exceeds burn rate within 30 days of launch
- Zero policy violations (no unauthorized spend, no out-of-jurisdiction outreach)
- Founder checks dashboard once daily and can make portfolio decisions in <5 min

**Tools they use:**
- Policy editor (weekly)
- Portfolio dashboard (daily)
- Compliance cockpit (weekly or on incident)

---

### Persona B: Operations Auditor

**Role:** Reviews run traces, detects anomalies, and ensures policy compliance.

**Goals:**
1. Verify that agents only took actions permitted by policy
2. Detect anomalies early (unusually high spend, unexpected agents involved, new tool usage)
3. Generate compliance evidence for internal audits and external reviews
4. Support incident investigation and postmortem

**Pain points:**
- Manual audit trails: traditional tools produce logs, but correlating decisions is manual and error-prone
- Silent failures: policy violations might go undetected if not explicitly logged
- Incomplete provenance: hard to answer "why did the system do X?"

**Success criteria:**
- Can reproduce any agent decision in <2 min (find policy rule, inputs, outputs, approvals)
- Monthly compliance report generated in <1 hour with full evidence chain
- Anomalies detected and escalated within same day as occurrence
- External auditors can review evidence without access to internal systems

**Tools they use:**
- Compliance cockpit (daily)
- Audit trail search (as-needed)
- Incident log (weekly or on incident)

---

### Persona C: Finance Controller

**Role:** Approves treasury doctrine and monitors spend integrity.

**Goals:**
1. Ensure budgets are realistic and enforced in real-time
2. Reconcile autonomous spend against bank statements daily
3. Optimize venture portfolio for margin and risk
4. Prepare financial statements and audit-ready spend ledgers

**Pain points:**
- Budget overruns: if there's no real-time enforcement, runaway ventures consume reserves
- Reconciliation delays: manual matching of internal ledger to bank statements is slow and error-prone
- Poor visibility: hard to trace spend to specific ventures or decisions

**Success criteria:**
- Budget caps enforced before transactions execute (no overruns)
- Daily reconciliation completes in <10 min and shows <$0.01 drift
- Can drill into any transaction and see: venture, decision rule, approval chain, business justification
- Monthly P&L and venture margin analysis auto-generated and ready for executive review

**Tools they use:**
- Treasury firewall (daily)
- Portfolio dashboard (2-3x weekly)
- Reconciliation report (daily)

---

### Persona D: Venture Manager

**Role:** Oversees a specific revenue-generating venture and optimizes its execution.

**Goals:**
1. Maximize revenue for assigned venture(s) within policy constraints
2. Identify and eliminate bottlenecks in the venture's task workflow
3. Propose new ventures or venture types based on market demand
4. Mentor junior team members on venture best practices

**Pain points:**
- Limited visibility into agent behavior: hard to see why a venture is underperforming
- Manual intervention: some decisions require founder approval, slowing execution
- Knowledge gaps: best practices for running ventures aren't documented

**Success criteria:**
- Assigned venture(s) achieve target revenue and margin within 60 days
- Can drill into venture dashboard and identify top 3 optimization opportunities in <30 min
- Proposes at least one new venture per quarter based on data
- Mentors new ventures from launch to profitability (<90 days)

**Tools they use:**
- Portfolio dashboard (daily)
- Workstream orchestration (daily)
- Venture detail pane (3x weekly)

---

### Persona E: External Compliance Reviewer

**Role:** Audits the venture on behalf of regulatory bodies or customers (SOC 2, GDPR, etc.).

**Goals:**
1. Verify the venture is following declared policies and legal obligations
2. Collect evidence for regulatory submissions
3. Assess risk and identify gaps
4. Build confidence that the venture is trustworthy

**Pain points:**
- Data silos: auditors have to request evidence from multiple teams
- Incomplete records: older evidence is hard to find or has been deleted
- Time-intensive: collecting and validating evidence is slow

**Success criteria:**
- Can download complete evidence chain for any time period in <5 min
- Policy versions and event logs are tamper-evident (digitally signed, checksummed)
- No missing data: audit trail is continuous with no gaps
- Can validate evidence offline (cryptographically verify timestamps and policy versions)

**Tools they use:**
- Audit cockpit (read-only access)
- Evidence export API
- Attestation verification tool

---

## Revenue Mechanics (Detailed)

### Labor Commodification Model

The core insight: **Agent work is a scarce, quantifiable resource that can be monetized.**

**Step 1: Define venture types**

A venture is a repeatable business process that generates revenue. Examples:
- **Research ventures**: aggregating market data, competitive intelligence, trend analysis → sold as reports
- **Content ventures**: writing technical guides, marketing copy, documentation → sold to customers
- **Code ventures**: generating libraries, scaffolding, bug fixes → sold as services or open-source sponsorship
- **Data ventures**: cleaning/labeling datasets, building knowledge graphs → sold to ML teams
- **GTM ventures**: running outreach campaigns, lead qualification → sold to B2B sales teams

**Step 2: Price the work**

For each venture type, define:
- **Unit of work**: hours of agent computation, outputs per hour, cost per output
- **Market rate**: what customers will pay for that work (research report: $500–2000, code artifact: $50–500, outreach campaign: $1000–5000)
- **Target margin**: revenue minus cost of infrastructure and APIs; aim for 60–80% gross margin (typical SaaS)

Example venture: "Research Reports"
- Unit: one 5000-word market analysis report
- Cost per report: $25 (compute + API costs)
- Market rate: $500 per report
- Margin: 95%
- Founder's goal: 10 reports/month = $5000 revenue, $250 cost = $4750 margin

**Step 3: Allocate capacity**

The founder allocates a portion of total agent capacity to each venture:
- Total available agents: 5 orchestrators + 10 researchers + 20 solvers (example)
- Allocation to "Research Reports": 1 orchestrator, 4 researchers, 8 solvers
- Capacity commitment: team can produce up to 10 reports/month (if fully utilized)

**Step 4: Execute and measure**

As agents complete work, the system tracks:
- **Inputs**: task submitted, deadline, customer requirements
- **Execution**: which agents worked, for how long, what APIs called
- **Outputs**: artifacts delivered, customer acceptance (if available)
- **Costs**: compute time, API calls, storage, payment processing
- **Revenue**: customer payment, recorded in ledger

**Step 5: Optimize the loop**

At regular intervals (weekly/monthly), analyze metrics:
- **Revenue per venture**: which ventures are generating the most margin?
- **Cost per unit output**: can we make reports faster/cheaper by tuning prompts or parallelizing tasks?
- **Utilization**: which agents are idle vs overloaded?
- **Customer satisfaction**: are outputs meeting quality thresholds?

Based on analysis, rebalance: move agents to high-margin ventures, pause low-margin ventures, propose new ventures.

### Venture Portfolio

The founder manages a portfolio of ventures, like a VC managing companies. The portfolio balances:

| Dimension | Strategy | Example |
|-----------|----------|---------|
| **Risk vs return** | Mix of proven (low-risk, steady revenue) with experimental (higher-risk, higher-return) | 70% proven ventures, 30% experimental |
| **Capital efficiency** | Ventures with different capital needs; some are capital-light (research) while others require upfront investment (building an IP library) | Allocate 20% of reserves to building assets, 80% to cash-generating work |
| **Timeline** | Mix of near-term revenue (weeks) with medium-term (months) and long-term (years) bets | 50% quick revenue, 40% 3–6 month runway, 10% moonshots |
| **Skill utilization** | Ventures that use different agent types, balancing compute load | Mix of research-heavy, code-heavy, and content-heavy |

**Portfolio metrics:**
- Total revenue (all ventures): $Y per month
- Total burn (all agents, infrastructure): $Z per month
- Margin: $(Y - Z) per month
- Runway (reserves / monthly burn): N months
- ROI per venture: (revenue - cost) / cost
- Risk score per venture: 0–100 based on volatility, customer concentration, market demand

**Portfolio rebalancing triggers:**
- Quarterly review: is the portfolio still aligned with founder goals?
- Performance trigger: if a venture underperforms for 2 consecutive months, pause and investigate
- Opportunity trigger: market analysis suggests a new venture could generate 3x revenue; founder evaluates fit
- Runway warning: if runway drops below 3 months, increase high-margin ventures or pause experimental ones

### Treasury Optimization Loop

The system continuously optimizes capital allocation:

**Day-to-day:**
- Every transaction: authorize spend, check against policy and budget caps, log in ledger
- Every completed task: record cost and revenue, update venture P&L
- End of day: reconcile ledger against bank statements, flag drift

**Weekly:**
- Venture performance: which ventures are trending up vs down?
- Cost optimization: identify cost overruns (e.g., API calls too expensive), negotiate alternatives
- Capacity planning: are we approaching limits on any resource (agent availability, API quotas)?

**Monthly:**
- Full P&L by venture
- Margin analysis: which ventures are most profitable?
- Rebalancing recommendation: AI suggests moving agents/budget to high-performers
- Runway forecast: if current spend/revenue continues, how many months until reserves depleted?
- Policy audit: any policy violations or close calls? Should we adjust caps or escalation rules?

**Quarterly:**
- Strategic review: has the market changed? Should we launch new ventures, exit old ones, or pivot?
- Capacity expansion: do profitable ventures justify expanding infrastructure (more agents, higher API quotas)?
- Risk assessment: how exposed are we to a single customer or market? Recommend diversification.

---

## Success Metrics (KPIs with Targets)

### Financial KPIs

| KPI | Definition | Target (MVP) | Target (v1) |
|-----|-----------|--------------|-------------|
| **Monthly Recurring Revenue (MRR)** | Total revenue from all ventures per month | $5,000 | $50,000 |
| **Gross Margin %** | (Revenue - direct costs) / Revenue | 70% | 75%+ |
| **Burn Rate** | Monthly operational cost (agents, infrastructure, APIs) | $2,000 | $15,000 |
| **Runway (months)** | Current reserves / monthly burn | 12+ | 12+ |
| **Venture ROI** | (Cumulative revenue - cumulative cost) / cumulative cost per venture | 2.0x (100%) | 5.0x (400%+) |
| **Cost per output unit** | Average cost to generate one artifact (report, code, article) | $30 | $15 |

### Operational KPIs

| KPI | Definition | Target (MVP) | Target (v1) |
|-----|-----------|--------------|-------------|
| **Uptime %** | % of scheduled agent work that completes without error | 98% | 99.5% |
| **Latency (p95)** | 95th percentile time from task submission to completion | 2 hours | 30 minutes |
| **Policy compliance rate** | % of decisions that align with policy on first pass | 95% | 99%+ |
| **Audit completeness** | % of actions with full provenance chain | 100% | 100% |
| **Manual override rate** | % of decisions requiring founder intervention | <5% | <1% |

### Quality KPIs

| KPI | Definition | Target (MVP) | Target (v1) |
|-----|-----------|--------------|-------------|
| **Customer satisfaction** | % of delivered artifacts meeting customer spec on first submission | 85% | 95%+ |
| **Rework rate** | % of output requiring revision | <15% | <5% |
| **Security incidents** | Count of security/policy violations per quarter | 0 | 0 |
| **Compliance violations** | Count of policy breaches per quarter | 0 | 0 |

---

## Anti-Goals (What Venture Does NOT Do)

### 1. Venture is Not a Speculative Trading Bot

- **Not a crypto bot**: Venture does not engage in cryptocurrency trading, arbitrage, or speculation
- **Not a market timer**: Venture does not make bets on asset prices or market movements
- **Distinction**: Venture generates revenue from real work (creating valuable artifacts, executing services), not from price fluctuations

### 2. Venture is Not a Replicate of Human Team

- **Not a headcount replacement**: Venture is not designed to replace human employees; it augments human teams
- **Not a consulting firm**: Venture does not provide professional advice or strategy; it executes defined, repeatable work
- **Distinction**: Venture is a tool for amplifying human productivity and capital efficiency, not for eliminating jobs

### 3. Venture is Not Fully Autonomous in Perpetuity

- **Not a "set and forget" system**: Venture requires founder governance (policy review, rebalancing, incident response)
- **Not self-aware**: Venture does not learn to change its own policy or constitution without explicit founder authorization
- **Distinction**: Venture is autonomous within strict, founder-defined bounds; it executes without human approval for routine decisions, but strategic changes remain founder-driven

### 4. Venture is Not Unregulated

- **Not a loophole**: Venture operates within legal and regulatory frameworks; it complies with tax law, labor law (where applicable), GDPR, FTC, and other jurisdictional rules
- **Not a way to evade compliance**: Venture's policy layer enforces compliance, not circumvents it
- **Distinction**: Venture is a governance system that makes compliance easier and more transparent, not a tool for avoiding it

### 5. Venture is Not a Replacement for Human Judgment on Strategic Decisions

- **Not a CEO**: Venture does not make strategic decisions (market entry, product pivots, major spending) autonomously
- **Not a board**: Venture does not vote on governance changes or constitutional amendments
- **Distinction**: Venture executes on founder-defined objectives within strict policy guardrails, but the founder retains all strategic authority

---

## MVP vs v1 vs v2 Feature Matrix

### MVP (16 weeks)

**Objective:** Prove labor commodification and treasury enforcement work end-to-end.

| Component | Feature | Status |
|-----------|---------|--------|
| **Policy** | Basic policy YAML editor, policy versioning, syntax validation | Ready to implement |
| **Execution** | Workflow DAG orchestration, task routing to agents | Ready to implement |
| **Treasury** | Default-deny spend authorization, real-time ledger, daily reconciliation | Ready to implement |
| **Artifacts** | Headless artifact compilation (slides, docs, timelines) | Exists (build on) |
| **Observability** | Event sourcing, audit trail with policy version, provenance chains | Ready to implement |
| **UI** | Policy editor (command-line or basic web UI), portfolio dashboard (web), reconciliation view (CLI) | Ready to implement |

**Out of scope for MVP:**
- Multi-agent coordination (only single-agent workflows)
- Compliance automaton (DSAR, retention, consent workflows)
- Advanced portfolio analytics (will use basic spreadsheet-style dashboards)
- External auditor API (audit log is internal-only)
- Venture scaling recommendations (founder makes all scale decisions manually)

### v1 (32 weeks, builds on MVP)

**Objective:** Add multi-agent coordination, compliance automation, and portfolio optimization.

| Component | Feature | Addition |
|-----------|---------|----------|
| **Policy** | Advanced policy types (conditional rules, role-based gates, dynamic caps) | From MVP |
| **Execution** | Multi-agent DAG, agent capacity modeling, load balancing | New |
| **Treasury** | Venture-level budgeting, venture portfolio dashboard, margin analysis | New |
| **Artifacts** | Extended spec types (video, interactive dashboards), artifact versioning | From MVP |
| **Compliance** | DSAR workflows, data retention automation, consent management, policy attestations | New |
| **Observability** | Tamper-evident logs (checksums, digital signatures), external auditor API, evidence export | Enhanced |
| **Analytics** | Venture ROI tracking, cost-per-output analysis, rebalancing recommendations | New |
| **UI** | Portfolio dashboard (advanced), compliance cockpit, external auditor portal | New |

### v2 (TBD, builds on v1)

**Objective:** Advanced self-governance, multi-founder entities, and institutional integrations.

| Component | Feature | Addition |
|-----------|---------|----------|
| **Governance** | Multi-founder voting on policy, constitutional amendments, autonomous venture formation | New |
| **Treasury** | Multi-currency, international payment processing, advanced hedging strategies | New |
| **Compliance** | SOC 2 ready, automated regulatory filing, HIPAA/PCI support | New |
| **Interop** | Integrations with tax/accounting software, investor dashboards, DAO governance | New |
| **Scale** | Horizontal scaling across regions, agent federation, decentralized execution | New |

---

## Business Model & Go-to-Market

### Positioning

Venture targets **founders and technical teams** who want to:
1. **Automate a repeatable process** that currently requires hired labor or consulting
2. **Own the economic upside** of automation (not pay a SaaS fee)
3. **Operate within strict governance** (compliance, audit, policy enforcement)

### Revenue Streams (Venture-as-a-Product vs Venture-as-a-Service)

**Option A: Venture-as-a-Product** (licenses, open-source model)
- Founder deploys Venture in their own infrastructure (Docker, process-compose)
- Venture operates autonomously, generating revenue for the founder
- Venture platform operator (Ante, us) takes a small cut (5–10%) of gross revenue
- Example: founder launches a $10K/month research venture, we take $500–1000/month

**Option B: Venture-as-a-Service** (SaaS model)
- Founder uses our hosted Venture platform
- We charge per venture, per month, or per revenue dollar generated
- We manage infrastructure, handle all updates and compliance
- Example: founder pays $500/month for up to 3 ventures, or 10% of revenue (whichever is higher)

**Recommended approach for MVP:** Option A (product + small revenue share), then expand to Option B as demand grows.

### Customer Segments

1. **Technical founders** (solopreneurs, early-stage startups): want to bootstrap by automating consulting/services
2. **Professional services firms** (agencies): want to scale delivery without hiring more staff
3. **Enterprise operations teams**: want to automate internal work (documentation, data processing, compliance automation)
4. **Open-source projects**: want to fund development and community management without venture capital

### Marketing & Customer Acquisition

- **Community**: Build in public; share early results and lessons learned
- **Content**: Publish case studies (e.g., "How we built $100K/year in autonomous revenue using Venture")
- **Open-source**: Release core components (policy engine, artifact compiler) as open-source to build credibility
- **Partnerships**: Integrate with tools founders already use (GitHub, Stripe, Slack, API platforms)
- **Direct outreach**: Talk to early customers, gather feedback, iterate on product

---

## Glossary

| Term | Definition |
|------|-----------|
| **Venture** | A repeatable business process that generates revenue (e.g., "Research Reports venture") |
| **Treasury** | The system's collective working capital, reserves, and spending authority |
| **Policy Constitution** | Founder-defined rules governing what the system can and cannot do |
| **Policy bundle** | Versioned set of policies (budget caps, tool allowlists, escalation rules, compliance rules) |
| **Ledger** | Immutable record of all transactions and state transitions |
| **Event sourcing** | Recording every state change as an event, enabling full replay and audit |
| **DAG (Directed Acyclic Graph)** | Task workflow representation where nodes are tasks and edges are dependencies |
| **Agent** | A single-threaded executor (orchestrator, researcher, solver) that completes tasks |
| **Tool** | A permitted action (API call, file operation, web scrape, payment) that an agent can take |
| **Allowlist** | Policy-enforced set of tools each agent role may use |
| **Default-deny** | Authorization principle where all actions are blocked unless explicitly permitted |
| **Artifact** | Output of agent work (document, code, image, video, data) |
| **Margin** | Revenue minus cost for a venture; key metric for venture profitability |
| **Runway** | Months until reserves are depleted at current burn rate |
| **Rebalancing** | Moving agents or budget between ventures based on performance data |
| **Compliance cockpit** | UI for auditing policy adherence and viewing audit trails |
| **Evidence chain** | Complete record of inputs, decisions, and outputs for any high-impact action |
| **Tamper-evident** | Digitally signed and checksummed to prevent undetected modification |
| **DSAR (Data Subject Access Request)** | Regulatory request for all data collected about an individual |

