Shipwise55
Automating the Chief Product Officer for AI-built software.
The specification & memory layer that turns product thinking into editable text and visual system graphs — so AI can actually ship production software instead of crashing it. The bottleneck where vibe-coders hallucinated and broke backends is solved.
Vibe-coding has no CPO — so it crashes production.
- Speed
- Instant demos
- Fast prototyping
- Memory
- Architecture
- Specifications
- Long-term consistency
- Iterative reasoning
The Chief Product Officer role — deciding what to build, designing the architecture, owning NFRs and edge cases — is being skipped. Tools like Lovable hallucinate backends and crash in production because there is no persistent spec or system memory to reason against.
As systems grow
- Prompts become enormous
- Token costs explode
- Models forget prior decisions
- Architecture drifts
- Small changes create new bugs
- Non-technical stakeholders lose visibility
Current AI coding tools treat software engineering like a single conversation. Software engineering is not a conversation — it is an evolving memory system.
Bigger context windows alone don't solve it.
- Larger context windows
- Brute-force token scaling
- More expensive inference
- Know what matters
- Remember past decisions
- Retrieve exact architecture state
- Localize changes
- Reason hierarchically
- Preserve consistency over time
The real bottleneck is not token count. The real bottleneck is persistent, structured memory.
The missing layer in AI software engineering.
Current products
Lovable, Cursor, Replit, Devin, Windsurf — all focus on:
- Generating code quickly
- Compressing developer workflows
- Accelerating implementation
What none solve
- Persistent architectural memory
- Specification-driven engineering
- Long-context orchestration
- Modular system reasoning
- Enterprise-grade maintainability
- Foundation models
- ↓ Coding agents
- ↓ Shipwise55 memory layer
- ↓ Enterprise software systems
Automate the CPO. Make software maintainable in text and graphs.
"Prompt an LLM, hope the backend doesn't crash."
"Edit the spec or the system graph in plain English — only the affected modules regenerate."
Shipwise55 automates the CPO
- Requirements extraction from PRDs, transcripts, ideas
- Architecture and service decomposition
- Non-functional requirements and constraints
- Edge cases and dependency mapping
- Spec-first validation before generation
Editable internals — text + graph
- Every decision stored as human-readable text
- Every relationship visible as a navigable graph
- Localized edits — change one node, regen one module
- No full-context rewrites, no architectural drift
- Maintainable production software, not throwaway demos
The Shipwise55 workflow.
- Step 01 — Upload
Product idea, PRD, meeting notes, whiteboards, architecture docs, business requirements.
- Step 02 — Extract
Use cases, actors, requirements, permissions, entities, APIs, workflows, dependencies, edge cases.
- Step 03 — Generate
Specifications, architecture maps, system diagrams, UI mockups, service decomposition, implementation roadmap.
- Step 04 — Review
User reviews and edits specifications. Architecture validated before any code is generated.
- Step 05 — Build
AI coding agents generate modular implementations against the validated specification.
- Step 06 — Persist
A living memory layer tracks system evolution. Future edits localize to affected modules.
Specifications are cheaper than code.
Why traditional engineering works
- Humans review requirements before implementation
- Architecture decisions happen before coding
- Stakeholders understand system behavior
Modern AI coding skips this
- Uncontrolled generation
- Expensive rework
- Architecture instability
- Hallucinated dependencies
- Fragile systems
Shipwise55 restores
- Architectural reasoning
- Human-readable system understanding
- Iterative requirement refinement
- Localized system updates
Changing specifications is dramatically cheaper than rewriting generated code.
The Hierarchical Memory Transformer.
- Pure sparse attention
- Pure RAG
- Pure LongRoPE scaling
- Brute-force context windows
- Local transformer reasoning
- Learned long-term memory
- Sparse block routing
- Semantic KV-cache compression
- Exact retrieval fallback
Massive effective context windows — with architectural consistency.
How we solve the context window problem.
- 01
Local active reasoning
High-quality short-range coding reasoning preserves code quality and instruction following.
- 02
Specification memory
Persistent understanding of product requirements across sessions.
- 03
Architecture graph memory
Relationships between services, APIs, flows, and dependencies tracked as a living map.
- 04
Sparse retrieval routing
Selective retrieval of only relevant architectural context — slashing token cost and hallucinations.
- 05
Exact source retrieval
Precise retrieval of implementation details and prior decisions when consistency matters.
This creates
- Scalable reasoning
- Lower token consumption
- Stable architectural evolution
- Modular AI engineering
Memory is the substrate of engineering — not an optimization on top of it.
The production crash problem — solved at the root.
- Lovable-style hallucinated backends and crashes
- Models forget prior decisions every session
- Codebases drift and become unstable
- Small changes trigger cascading regressions
- Non-technical owners lose all visibility
- An always-current spec in plain English
- An editable system graph of services, APIs, flows
- Localized regeneration — change one node, ship one module
- Architecture-aware coding agents that don't drift
- Production-grade software you can maintain, not just demo
The bottleneck wasn't the model. It was the absence of a CPO and a persistent system memory. Shipwise55 supplies both.
Documentation as the source of truth. Code as a side-effect.
- Prompt → code, directly, with no internals exposed
- Non-developers cannot see what's under the hood
- Small amendments overload the context window
- Every fix introduces new bugs
- Backends hallucinate and crash past a threshold
- No shared artifact between stakeholders and AI
- LLMs build comprehensive system documentation from your idea
- Every actor, flow, API, entity, edge case in plain English
- Review and amend a sentence before any tokens are spent on code
- Edits are localized to affected modules — never the whole project
- Code runs against a stable architecture, not a fragile prompt history
- Founders, business stakeholders, and AI read the same document
A spec is dramatically cheaper than code — for humans and LLMs. Reviewing what needs to be built before shipping saves man-hours and tokens. Localized edits make code changes immune to hallucinations and context-window limits.
Existing AI coding tools are stateless.
| Company | Main focus | Limitation |
|---|---|---|
| Lovable | Rapid generation | No persistent architecture memory |
| Cursor | Developer productivity | Limited long-term context consistency |
| Replit | Fast prototyping | Architecture drift over time |
| Devin | Autonomous execution | Weak persistent system reasoning |
| Windsurf | Coding acceleration | Prompt-centric workflows |
| Shipwise55 | Persistent memory, specification-first workflows | Architecture consistency, modular reasoning, enterprise scale |
“Cursor helps developers write code. Shipwise55 helps organizations think, remember, and evolve software systems with AI.”
Deep infrastructure moat.
Shipwise55 IP
- Proprietary hierarchical memory architecture
- Long-context orchestration engine
- Persistent architecture graph
- Specification decomposition engine
- AI-native system memory layer
- Long-context evaluation datasets
- Modular AI execution framework
- Enterprise architecture consistency engine
Competitors optimize for
- UI
- Generation speed
- Wrapper experiences
We optimize for
- Memory systems
- Architectural reasoning
- Persistent state
- Scalable software evolution
Timing is perfect.
- 1
LLMs are now capable enough for meaningful software generation.
- 2
Context limitations are becoming the dominant bottleneck.
- 3
Enterprises need maintainability, governance, and architectural consistency.
The next generation of AI infrastructure will be memory-native, architecture-aware, long-context optimized, and persistent by default. Shipwise55 sits at the center of that transition.
A massive emerging market.
Markets impacted
- AI coding tools
- Enterprise software engineering
- AI developer infrastructure
- Product management platforms
- Enterprise architecture systems
- AI agent infrastructure
- Long-context AI orchestration
Every large enterprise will need
- Persistent AI memory
- Architecture-aware reasoning
- Modular AI orchestration
- Long-term AI system consistency
Multi-layer revenue model.
SaaS subscriptions
Founders, startups, product teams.
Enterprise platform licenses
Large engineering organizations.
Usage-based orchestration
AI generation and memory compute.
API infrastructure
Persistent memory APIs for AI agents.
Private enterprise deployment
Security-sensitive organizations.
Architecture memory cloud
Long-term persistent system state.
Future potential: AI-native enterprise operating systems.
Phased market expansion.
Phase 1 — Founders & indie builders
AI-generated apps become unstable quickly. We provide structured specifications and stable modular generation.
Phase 2 — Product teams & CTOs
Scaling AI-generated systems across teams. We provide persistent architecture and maintainability.
Phase 3 — Enterprise engineering organizations
Governance, consistency, compliance, architecture drift. We provide long-context orchestration and AI system memory.
Phase 4 — AI-native enterprises
Shipwise55 becomes the infrastructure for autonomous software evolution.
Key technical metrics.
We aim to demonstrate
- Reduced hallucinations
- Lower regression rates
- Reduced token consumption
- Better architecture consistency
- Better long-session memory retention
- Improved large-repo task completion
- Reduced engineering iteration cost
- Higher modular code stability
- Better dependency consistency
Benchmark categories
- Large repository modification
- Architecture drift testing
- Multi-session consistency
- Long-running agent memory
- Dependency resolution accuracy
- Enterprise workflow orchestration
The path to AI-native infrastructure.
- 2026Specification-first AI engineering platform.
- 2027Persistent memory architecture engine.
- 2028Long-context autonomous coding agents.
- 2029Enterprise AI-native software infrastructure.
- FutureSelf-maintaining enterprise software systems.
Long-term vision
- AI-native ERP generation
- Autonomous architecture evolution
- Organizational software memory
- Enterprise digital twins
- Persistent AI engineering organizations
Anton Kravchenko, Founder & CEO.
Background
- Fintech and Web3 infrastructure expertise
- Complex trading and financial systems
- Deep systems thinking
- Product and architecture focus
- Scalable infrastructure
- Long-term vision around AI infrastructure
AI can generate code quickly. But it cannot persistently reason about evolving systems. Shipwise55 was created to solve the memory architecture problem at the core of AI-generated software.
The future of software engineering is persistent AI memory.
The current generation of AI tools helps humans write code. The next generation of AI infrastructure will:
- Remember systems
- Evolve architecture
- Preserve organizational knowledge
- Orchestrate modular reasoning
- Maintain software autonomously
The memory architecture layer for AI-built software.
The operating system for autonomous software engineering.
Software engineering needs memory, structure, and hierarchical reasoning.
Current AI coding tools generate code. Shipwise55 enables AI to:
- Think architecturally
- Remember persistently
- Evolve systems safely
- Reason hierarchically
- Scale software generation reliably
The future of software engineering is not prompt-to-code. It is persistent AI system memory.
Shipwise55 · shipwise55.com · 2026