Automating the Chief Product Officer
for AI-built software.
11builders is the specification & memory layer that sits between product thinking and AI-generated code. It does the CPO job — requirements, architecture, NFRs, edge cases — as human-readable text and editable system graphs you can change directly. The bottleneck where vibe-coders hallucinated and crashed production backends is solved.
- 73%
- Reduced hallucinations
- 58%
- Reduced token usage
- 89%
- Codebase consistency
- 94%
- Memory retention
- 3.2×
- Large-repo completion
Measured vs. standard LLM coding agents
Why 11builders.
An elite product is shipped by a tight crew of about eleven builders: a CPO, a system architect, a few senior engineers, a designer, an SRE, a QA lead, a data person. They share one mental model of the system and edit it together — that shared memory is what keeps the codebase from collapsing under its own weight.
11builders is that crew, encoded. The platform plays every one of those roles as a persistent, structured layer above the code-generating LLM — so a single founder, or a small team, ships like eleven senior builders who never forget a requirement, an edge case, or a prior decision.
- Builder 01 — CPOTurns intent into requirements.
- Builder 02 — System architectOwns the component graph and NFRs.
- Builders 03–07 — EngineersGenerate, refactor, and localize edits.
- Builder 08 — DesignerKeeps flows and UX coherent.
- Builder 09 — SREWatches runtime, scale, and failure modes.
- Builder 10 — QAEnumerates edge cases before generation.
- Builder 11 — MemoryThe persistent spec & decision log.
It automates the Chief Product Officer.
The CPO role is the part of building software that vibe-coding skips: deciding what to build, how the system fits together, what the edge cases are, and which non-functional requirements matter. 11builders does that work explicitly, before a single line of code is generated.
- Turns a product idea, PRD, or transcript into a full requirements graph
- Decomposes into services, data models, APIs, flows, and NFRs
- Surfaces edge cases and dependencies a human PM would miss
- Validates the architecture before generation — not after a crash
Maintain real production software in plain English and visual diagrams.
Every spec, every architectural decision, every system relationship is stored as human-readable text and a navigable system graph. Open a node, edit the requirement, and only the affected modules regenerate — no full-context rewrite, no architectural drift.
- Edit
- a sentence
- Regenerate
- one module
- Inspect
- the graph
- Ship
- to production
Vibe-coding tools like Lovable hallucinate backends, drift architecturally, and crash in production. 11builders fixes the root cause: there was never a persistent spec or system memory to reason against.
Prompt-to-code, no memory, no spec — crashes on the third feature.
Spec-first, graph-aware, modular regen — production-grade from day one.
Real software you can maintain with text and visuals, not just demos.
A vision: documentation as the source of truth, code as a side-effect.
Prompt → Code, no internals.
- A user prompt is converted directly into code
- Non-developers have no way to inspect what's under the hood
- Small amendments require rewriting prompts — the context window overflows
- Each fix introduces new bugs and silent regressions
- The backend hallucinates and crashes once complexity passes a threshold
- There is no shared artifact between business stakeholders and the AI
Specifications first. Code is generated against an approved system.
- Modern LLMs build comprehensive system documentation from your idea
- Every actor, flow, API, entity, edge case is written in plain English
- You review and amend the spec — a sentence — before any tokens are spent on code
- Subsequent edits are localized to affected modules, not the whole project
- Code generation runs against a stable architecture instead of a fragile prompt history
- Business stakeholders, founders and AI all read from the same human-understandable document
Writing a spec is dramatically cheaper than writing code — for both humans and LLMs. Reviewing what needs to be built before shipping saves man-hours and tokens. Localized edits keep code changes immune to hallucinations and context-window limits.
Current tools skip the most critical layer.
Prompt → Code (direct)
- Users lose visibility into system logic
- Every prompt rewrite overloads the context window
- Small changes create hallucinations and regressions
- LLMs forget decisions made earlier
- Long projects become unstable over time
- Code generation grows unreliable as complexity grows
What engineering actually needs
- Structured knowledge representation
- Evolving architecture tracking
- Persistent memory across sessions
- Interconnected specifications
- Iterative hierarchical reasoning
- Modular execution with localized edits
- Context-efficient AI orchestration
AI coding without memory does not scale. Software engineering is not a single prompt — it is structured knowledge, evolving architecture, persistent memory, and modular execution.
The five-layer memory architecture.
A Hierarchical Memory Transformer architecture purpose-built to solve the context window bottleneck.
Local active reasoning
Strong local coding intelligence — short-range reasoning stays highly accurate and preserves instruction following.
System specification memory
Persistent architectural memory — APIs, dependencies, flows, constraints, and decisions survive across sessions.
Architecture graph memory
A living system map: service decomposition, data models, dependencies, and interfaces tracked as a structured graph.
Sparse block routing
Dynamic relevance selection loads only the parts of the system that matter — slashing token cost and hallucinations.
Exact implementation retrieval
Source-grounded retrieval when precision matters — architectural consistency guaranteed at generation time.
Specification-first flow
Reason in English. Validate flows, interfaces, and architecture before any code is generated. Changing specs is 10× cheaper than rewriting code.
From idea to production-ready architecture.
- Step 01Input
Prompt, PRD, architecture docs, transcripts.
- Step 02Extract
Use cases, actors, requirements, edge cases.
- Step 03Architect
System graph, modules, NFR-driven design.
- Step 04Generate
Modular implementations, independently verifiable.
- Step 05Persist
Decisions and dependencies update the graph.
Future edits only touch localized modules — never reprocess the entire context window.
A $89B+ addressable opportunity.
$15B (2025) → $45B (2028), 44% CAGR
Annual spend on architecture & spec tools
Emerging memory & orchestration layer
Why now
- LLMs are powerful enough for complex engineering
- Context limitations are now the primary bottleneck
- Enterprises urgently need maintainable AI-generated code
- AI-generated code volume is exploding
- Current workflows fundamentally break at scale
- Coding agents require persistent memory to be useful
Phased go-to-market
- Phase 1Startup founders & indie builders
- Phase 2PMs & CTOs at growth-stage companies
- Phase 3Enterprise engineering organizations
- Phase 4Platform for AI-native software companies
Founders who know how to get from 0 to 1 — and keep going.
Balanced R&D and revenue. Long runway by design.
- Split focus between deep R&D and revenue-driving product surfaces from day one.
- Cash burn held under a managed cap — runway measured in years, not quarters.
- Repeat experience taking companies from 0 → 1 and raising the next rounds on milestones, not narrative.
Small offline bases in Lisbon and New York.
A distributed company with physical anchor points — reachable in person, by mail, or by phone — so investors, enterprise partners, and early customers always have a real address and a real voice on the other end.
Direct access to top neural-net talent from MIPT.
We have a working relationship with the Moscow Institute of Physics and Technology (mipt.ru) community — including young researchers pursuing PhDs in neural networks. That's the talent pool we draw from to attack the LLM memory problem at the architecture level, not at the prompt level.