6 months in production

Build an AI CEO
for Your SaaS

Never sleeps. Always shipping. Bug fixes before customers can refresh their inbox.

€10k to build vs €200k/year to hire. No equity demands. No ego. Just execution—on-prem AI that fixes bugs, ships features, and deploys from Jira or WhatsApp.

See the ROI→
$ ai-ceo --run
$# Sentry alert detected
$→ Analyzing error: TypeError in NotionPageRende▋
€5k
Hardware Investment
53%
Cost Savings
2 min
Bug → Deploy
143 hrs
Saved Monthly
Works with Hetzner, OVH, or your dedicated servers
No vendor lock-in—you own the infrastructure
Open-source models, full data privacy

> The Token Economics Problem

API costs are rising ~15% annually. Your "affordable" AI spend today becomes a budget crisis tomorrow.

cost_trajectory.json
YearMonthly API CostCumulative
2024€4,700€56,400
2025€5,400€121,200
2026€6,200€195,600
2027€7,100€280,800
4-Year Total€280,800

The Smart Alternative

🐴
Workhorse (On-Prem)
95% of tasks: bug fixes, tests, docs, refactoring
Cost: €0 per request
⚡
Strike Force (Claude/GPT)
5% of tasks: architecture, security, creative
Cost: ~€0.05-0.08 per request
Result:
€280k → €34k (88% savings)

> How It Works

From bug detection to production deployment in minutes, not hours.

Automation workflow
01

Detect

Sentry alert or Jira ticket triggers the system

02

Analyze

AI reads codebase, understands context

03

Fix

Generates code + tests, creates PR

04

Deploy

Auto-deploy to staging, CEO approves prod

> What Works Exceptionally Well

90%+ success rate on these task types with on-prem models (€0 per request)

92%

Bug Fixes

Known code paths, missing null checks, type errors

88%

Test Generation

Comprehensive tests from existing code patterns

95%

Refactoring

Extract functions, rename variables, clean up

98%

Documentation

JSDoc, README sections, API docs generation

78%

Architecture Changes

Cross-file refactors with Claude 3.5 Sonnet

82%

Security Reviews

Audit middleware, find vulnerabilities

> Guardrails Matter

Models lose context. Costs spiral. Output gets inconsistent. Without guardrails, AI becomes a liability. With thoughtful orchestration, worktrees, and your SOPs baked in—AI becomes reliable.

❌ Problem 1

Models Lose Context

Without worktrees and context isolation, models mix up unrelated issues. Fix one bug, break another. Your 200k codebase becomes a tangled mess in the model's "memory."

Result: 40% failure rate, hours of debugging
❌ Problem 2

Costs Spiral Out of Control

One infinite loop of API calls. One bug in auto-retry logic. Wake up to a €10k bill because there's no hard budget limit or request throttling.

Result: Bill shock, CFO furious, project canceled
❌ Problem 3

Output Ignores Your SOPs

Your team has architecture guidelines, coding standards, naming conventions—your secret sauce. Generic AI doesn't know them. Output requires heavy editing to match your style.

Result: "AI-generated" code looks foreign in your codebase
guardrails_architecture.yml
1. Worktrees → Context Isolation
✓ Each issue gets its own isolated worktree (branch + context)
✓ Models can't pollute each other's context
✓ Parallel fixes without interference
Example: Bug #123 (auth) and Bug #124 (payments) run in separate worktrees, never cross-contaminate
2. Own-Agents → Optimize Your Workflows
✓ Specialized agents for your specific tasks (not generic)
✓ Learn from past fixes, improve over time
✓ Route to right model based on task type
Example: "Bug-fix agent" uses workhorse (€0), "architecture agent" uses strike force (€0.40)
3. Your SOPs → AI's Guardrails
✓ Architecture guidelines injected into every context
✓ Naming conventions enforced automatically
✓ Code style matches your secret sauce
Example: "All API routes must use /api/v2/ prefix" → model always follows, no exceptions
4. Hard Budget Limits → No Surprises
✓ €20/hour, €100/day, €500/week hard limits
✓ Auto-pause at 80% threshold
✓ Alert CFO if approaching limit
Result: Predictable costs, no bill shock

Why Guardrails Transform AI from Liability to Asset

Raw AI (No Guardrails)
Context Chaos
Mixes up unrelated issues, 40% failure rate
Cost Explosion
No budget limits, €10k surprise bills
Generic Output
Ignores your SOPs, needs heavy editing
Manual Babysitting
Developer reviews every output
Trust Level: 30%
Basic AI (Simple Limits)
Some Context Control
Per-request limits, still mixes issues
Basic Budget Caps
Daily limits, but no intelligent routing
No SOP Integration
Can't learn your architecture patterns
Reactive Only
Catches errors after they happen
Trust Level: 60%
AI CEO (Full Orchestration)
Worktree Isolation
Each issue in own context, zero pollution
Smart Cost Routing
Workhorse (€0) for 95%, strike force for 5%
Your SOPs Baked In
Architecture guidelines in every context
Own-Agents Learn
Improve over time, optimize workflows
Trust Level: 92%

Watch Guardrails Prevent a €5k Mistake

guardrail_prevented_disaster.log
$# Developer triggers: "Fix all auth bugs"
$→ Orchestrator analyzing request...
$⚠ GUARDRAIL TRIGGERED: Task too broad
├─ Found 27 auth-related issues
├─ Estimated cost: €21.60 (all Claude @ €0.80/issue)
├─ Context per issue: ~50k tokens
└─ Would send 27 separate requests without batching
$→ Breaking into 27 isolated worktrees...
├─ 22 simple bugs → Workhorse (€0 each)
├─ 3 medium bugs → Llama 70B (€0 each)
├─ 2 complex bugs → Claude strike force (€0.80 each)
└─ Total estimated cost: €1.60 (vs €21.60)
$✓ Saved €20 + parallelized execution
$✓ Processing 22 bugs in parallel (worktrees)
$⏱ Estimated time: 8 minutes (vs 4 hours manual)

> Smart Model Selection

Be smart about your AI spend. Use on-prem models as your workhorse—they handle 95% of tasks at €0 cost. Deploy Claude/GPT as your strike force for the complex 5%.

🐴 Qwen 32B (Workhorse)
Bug fixes, tests, docs
€0
Fast
🐴 Llama 70B (Workhorse)
Feature planning, refactors
€0
Medium
⚡ Claude 3.5 Sonnet (Strike Force)
Architecture, security
~€0.05
Fast
⚡ GPT-4 Turbo (Strike Force)
Creative, customer copy
~€0.08
Fast
AI Brain visualization

> The ROI

398% return over 3 years. Pays for itself in 12 weeks.

financial_roi.json
3-Year API Costs (Status Quo)€169,200
3-Year Hardware + Ops€34,000
Total Savings€135,200
ROI398%
productivity_roi.json
Simple bug fix30 min → 5 min
Feature addition4 hrs → 1 hr
Test writing1 hr → 10 min
Monthly Hours Saved253 hrs
Server rack

> The Hardware Stack

€10k one-time investment for unlimited on-prem AI inference. Runs on Hetzner, OVH, or your own servers.

hardware_spec.yml
gpu: 2x RTX 4090 (48GB VRAM)
ram: 256GB DDR5
storage: 4TB NVMe
network: 10GbE internal
models:
- Qwen2.5-Coder-32B (4-bit, 20GB)
- DeepSeek-Coder-33B
- Llama 3.1 70B (2x GPU)
throughput: 35 tok/s, ~25s/request
monthly_cost: €150 (electricity)

> Why Blueprint?

The only platform that goes from Jira to production without touching your IDE.

comparison.json
FeatureBlueprintCopilotCursorDIY
Zero-touch deploy✓✗✗✗
On-prem option✓✗✗⚠
WhatsApp/Telegram control✓✗✗✗
Model choice per task✓✗✗⚠
Jira → PR → Deploy✓✗✗⚠
Auto-generated tests✓⚠⚠⚠

> CEO Dashboard

Deploy from anywhere. Approve from WhatsApp. Full visibility without touching code.

$ ai-ceo status
AI_SaaS_CEO v1.0.3 • uptime 14d 7h
# today
✓ 12 bugs fixed automatically
✓ 3 features deployed to prod
! 2 PRs awaiting approval
$ €3.40 spent (vs €120 typical)
# pending approval
JIRA-234 → staging
Add Notion database views
[a]pprove [r]eview [d]iff

> 6-Week Implementation

From hardware setup to full team rollout in 6 weeks.

Week 1

Hardware & Infrastructure

  • →Ubuntu 22.04 + NVIDIA drivers
  • →vLLM inference server
  • →PostgreSQL, Qdrant, Redis
Week 2

Control Plane

  • →Temporal orchestration
  • →Model gateway + router
  • →Policy engine + audit logs
Week 3

IDE Integration

  • →VS Code / Cursor extension
  • →Model picker UI
  • →Cost tracking dashboard
Week 4

Automation & Safety

  • →Code → tests → PR pipeline
  • →CI/CD integration
  • →Security scanning
Week 5

External Integrations

  • →Jira webhooks
  • →WhatsApp / Telegram bot
  • →Git hooks
Week 6

Rollout & Training

  • →Documentation
  • →Team training
  • →Gradual rollout

> FAQ

Common questions from dev teams evaluating AI CEO

Q1

What hardware do I actually need?

Two RTX 4090s (48GB total VRAM), 256GB RAM, 4TB NVMe. Total ~€10k. Runs Qwen-32B and Llama-70B locally. You can rent equivalent from Hetzner or OVH for ~€300/month.

Q2

How is this different from Cursor or Copilot?

Cursor and Copilot are IDE assistants—you still type, review, and deploy. AI CEO is zero-touch: Jira ticket → PR → tests → staging → your approval → prod. No IDE required.

Q3

What's the actual success rate?

92% for bug fixes, 88% for test generation, 95% for refactoring. Complex architecture changes drop to 78%—that's when we route to Claude strike force instead of workhorse models.

Q4

Can I use this with my existing CI/CD?

Yes. AI CEO creates standard PRs with conventional commits. Works with GitHub Actions, GitLab CI, CircleCI, Jenkins. Your existing pipelines run unchanged.

Q5

What about security and secrets?

Context filtering blocks .env files automatically. Semgrep scans all generated code. Your codebase never leaves your infrastructure—100% on-prem option available.

Q6

How long does implementation take?

6 weeks from hardware setup to full team rollout. Week 1-2: infrastructure. Week 3-4: integrations. Week 5-6: training and gradual rollout.

€10k investment
→ €135k saved over 3 years

The tools are ready. The models are good enough. The hardware is affordable.
What are you waiting for?

6 months in production
10 developers using daily
Open source blueprint