# Kytchen ICP + Market Wedge

**Linear Issue**: SHA-105 / SHA-300
**Status**: Draft v1.1

---

## The Pitch (30 Seconds)

> **"We're the Heroku for AI Agents."**
>
> Before Heroku, developers wasted 50% of their time managing servers.
> Before Kytchen, developers waste 50% of their time managing context.
>
> Heroku said: "Don't think about servers. Just write code."
> Kytchen says: **"Don't think about context. Just write prompts."**
>
> GitHub is where code sleeps. **Kytchen is where agents live.**

### The Magic Command
```bash
kytchen run recipe.json --ingredients ./docs/
```
You send us your recipe and ingredients. Magic happens. We provision the sandbox, parse the documents, manage the memory, and return the dish with sauce (evidence).

---

## Executive Summary

Kytchen targets **compliance-conscious mid-market companies** (50-500 employees) who need to analyze large document sets with AI but cannot afford the risk of unverified outputs. Our wedge is **auditable context management** - we're the only solution that provides cryptographic evidence trails for every AI analysis step.

**Infrastructure Play**: Like Heroku became the default for deploying web apps, Kytchen aims to become the default for deploying AI agents that need context management.

---

## Ideal Customer Profiles (Multi-Segment)

### Segment 1: **Developer Building AI-Native Apps** (Primary Market)

| Attribute | Detail |
|-----------|--------|
| **Title** | Software Engineer, AI Engineer, Full-Stack Developer |
| **Building** | AI-native apps, coding assistants, automated workflows |
| **Pain** | "My agent can only see what fits in the prompt. I need it to understand my whole codebase." |
| **Use Cases** | Code review agents, doc generation, test generation, refactoring, migration |
| **Decision** | Individual or team lead, $0-500/mo self-serve |
| **Why Kytchen** | Context management as infrastructure - don't reinvent the wheel |

### Segment 2: **Platform/Infra Team at AI Company**

| Attribute | Detail |
|-----------|--------|
| **Title** | Platform Engineer, Infra Lead, AI/ML Engineer |
| **Building** | Internal agent infrastructure, multi-agent systems |
| **Pain** | "We keep rebuilding context management for every agent. We need a standard." |
| **Use Cases** | Shared context layer, agent memory, multi-agent orchestration |
| **Decision** | Team lead, $1K-10K/mo |
| **Why Kytchen** | Become the standard context layer, not a custom solution |

### Segment 3: **Compliance/GRC Manager** (Wedge Market)

| Attribute | Detail |
|-----------|--------|
| **Title** | Compliance Manager, GRC Analyst, Security Lead, Internal Audit |
| **Company Size** | 50-500 employees (mid-market) |
| **Industry** | SaaS, FinTech, HealthTech, LegalTech, Enterprise B2B |
| **Pain** | "I can't use AI for compliance work because I can't prove what it read." |
| **Use Cases** | SOC2 gap analysis, policy review, audit prep |
| **Decision** | $5K-50K annual |
| **Why Kytchen** | Audit-ready evidence trails (the "sauce") |

### Segment 4: **Engineering Lead / Architect**

| Attribute | Detail |
|-----------|--------|
| **Title** | Staff Engineer, Security Architect, Platform Lead |
| **Motivation** | Wants AI tooling that's observable, not magic black boxes |
| **Pain** | "I can't deploy AI that I can't explain. I need to see what it's doing." |
| **Decision Role** | Technical evaluator, influences buy decision |
| **Why Kytchen** | Glass kitchen - full observability into agent behavior |

---

## The CI/CD Evolution (Bigger Vision)

### Old World: Push → Pray
```
Developer writes code
    → Push to GitHub
    → CI runs tests
    → CD deploys if green
    → Hope nothing breaks
```

### New World: Intent → Evidence → Ship
```
Developer describes intent
    → Agent analyzes codebase (via Kytchen)
    → Agent writes code with full context
    → Agent verifies against requirements
    → Agent produces evidence trail
    → Human reviews evidence, approves
    → Deploy with confidence
```

**Kytchen's role**: We're the context layer that makes agent-assisted development possible at scale. Without us, agents can only see what fits in a prompt. With us, agents can surgically explore any codebase, produce verifiable evidence, and ship with confidence.

**The shift**: From "CI/CD gates that catch failures" to "AI agents that prevent failures and prove correctness."

---

## Pain Points (Ranked by Severity)

### 1. **Context Limits** (Critical - All Developers)
> "My agent can only see 128K tokens. My codebase is 2 million tokens. It's useless for real work."

- Agents can't understand full codebases
- Prompt stuffing leads to hallucinations
- RAG chunking loses structure and misses connections

### 2. **Reproducibility** (High - All Developers)
> "I ran the same prompt twice and got completely different refactoring suggestions. How do I trust this?"

- Non-deterministic outputs block adoption
- No way to replay or debug agent behavior
- Can't share working configurations with team

### 3. **Black Box Distrust** (High - Enterprise)
> "I can't deploy AI that I can't explain. My architect/auditor/manager won't sign off."

- Engineers want observability
- Compliance needs audit trails
- Leadership needs accountability

### 4. **Token Cost Unpredictability** (Medium)
> "We blew through $800 in API costs analyzing our codebase. And I'm not even sure it read the right files."

- No budget controls
- No visibility into what's actually being processed
- Can't estimate costs before running

### 5. **Reinventing the Wheel** (Medium - Platform Teams)
> "Every agent we build needs context management. We keep rebuilding the same infrastructure."

- Context parsing (PDF, DOCX, code)
- Memory management
- Evidence collection
- Budget enforcement
- Teams waste months on infrastructure instead of features

---

## Why Kytchen Wins (The Wedge)

### Positioning Statement

> **Kytchen is the auditable AI context layer for compliance-conscious teams.**
>
> Unlike ChatGPT or direct API usage, Kytchen provides cryptographic evidence trails, deterministic context management, and hard budget controls - so you can use AI for compliance work and prove exactly what it analyzed.

### Competitive Differentiation

| Capability | ChatGPT/Claude | RAG Platforms | **Kytchen** |
|------------|----------------|---------------|-------------|
| Long document handling | Stuff + pray | Chunk + hope | **Surgical exploration** |
| Evidence trail | None | Metadata only | **Cryptographic receipts** |
| Budget control | None | Token limits | **Hard cost/time/iteration caps** |
| Audit-ready output | No | Partial | **Yes - tamper-evident logs** |
| Reproducibility | No | Maybe | **Yes - deterministic replay** |

### The "Glass Kitchen" Metaphor

Traditional AI: Black box restaurant. Food appears. You eat it. Hope you don't get sick.

**Kytchen**: Open kitchen. You see the prep. You see what ingredients were used. You get a receipt. If something goes wrong, you can trace it.

---

## Market Wedge: SOC2 Gap Analysis

### Why This Wedge?

1. **Urgent pain**: Every SaaS company selling to enterprise needs SOC2
2. **High-value**: Companies pay $50K-200K for manual SOC2 prep
3. **Document-heavy**: Policies, procedures, evidence - perfect for Kytchen
4. **Audit-centric**: The customer already thinks in terms of evidence trails
5. **Land-and-expand**: Once in for compliance, expand to other document analysis

### Initial Use Case

**"SOC2 Gap Analysis in 30 Minutes, Not 30 Hours"**

1. Upload your policy documents (infosec, HR, incident response, etc.)
2. Kytchen analyzes against SOC2 trust criteria
3. Get a gap report with **cited evidence** for each finding
4. Export audit-ready documentation

### Success Metrics

| Metric | Target |
|--------|--------|
| Time to first insight | < 5 minutes |
| Token efficiency ratio | > 10x vs context stuffing |
| Evidence coverage | 100% (every claim has a citation) |
| Customer NPS | > 50 |

---

## Go-to-Market Motion

### Phase 1: Founder-Led Sales (Now - Q1 2025)

- Target: 5-10 design partners from network
- Offer: Free usage in exchange for feedback + case study rights
- Goal: Validate wedge, refine product, collect testimonials

### Phase 2: PLG + Content (Q2 2025)

- Self-serve signup with free tier
- Content: "How to Prepare for SOC2 with AI (Without the Risk)"
- SEO: Target "SOC2 automation", "AI compliance tools", "auditable AI"

### Phase 3: Sales-Assisted (Q3 2025)

- Hire first AE after $50K ARR
- Focus on mid-market expansion deals
- Partner channel: MSPs, compliance consultants

---

## Pricing Hypothesis

| Tier | Price | Pantry | Rate Limit | Target |
|------|-------|--------|------------|--------|
| **Free** | $0 | 50MB | 5 req/min | Individual/trial |
| **Pro** | $39/mo | 10GB | 100 req/min | Small teams |
| **Team** | $199/mo | 50GB | 200 req/min | Growing companies |
| **Enterprise** | Custom | Unlimited | Custom | Compliance-heavy orgs |

**Key insight**: We charge for **storage + compute**, not LLM tokens. BYOLLM model means customers use their own API keys. We capture value on the orchestration layer.

---

## Objection Handling

### "We already use ChatGPT/Claude"

> "Great - you can keep using it. Kytchen sits in front of your LLM and adds the audit trail your compliance team needs. Think of it as the glass kitchen for your AI."

### "We have a RAG system"

> "RAG gives you chunk retrieval. Kytchen gives you surgical context exploration with cryptographic evidence. When your auditor asks 'how do you know the AI read page 47?', we have the receipt."

### "This seems expensive"

> "Compare to the cost of a failed audit, or the $100K you'd pay a consultant to do this manually. Kytchen pays for itself on the first gap analysis."

### "We need on-prem / self-host"

> "We offer Docker self-hosting for enterprise. Same audit trail, your infrastructure."

---

## Next Steps

1. [ ] Validate ICP with 5 customer interviews
2. [ ] Build SOC2 recipe template
3. [ ] Create demo video showing gap analysis workflow
4. [ ] Draft landing page copy using hooks from brand bible
5. [ ] Set up pilot pipeline tracking in Linear

---

*Draft created 2025-12-16 by Claude Opus 4.5*
*Awaiting review by GTM lead*
