20M+ LOC under Sage
Healthcare Platform
- One developer completed a 5-month integration project in 5 weeks
- Critical feature timeline pulled forward by 9 months
- Expanded to 9 systems across the full portfolio
NewSage AI Technology: The Digital Architect
Automating complex IT cognitive workstreams.
Sage reads fragile legacy systems and turns them into validated Cognitive Models for humans and AI agents.
8 hours + 2 days
Automated analysis plus SME validation replaces months of discovery.
Cognitive Model
Verified system intelligence for humans and AI agents.
// What Sage builds
Verified
system intelligence.
Sage builds a detailed knowledge graph organized by business processes, technology aspects, and architectural construct lenses, then vends slices of that model through MCP.
- Model lens
Business processes
Organizes workflows, rules, and operating behavior into a queryable system graph.
- Model lens
Technology aspects
Surfaces frameworks, dependencies, integrations, data flows, and implementation constraints.
- Model lens
Architectural constructs
Identifies bounded contexts, aggregate roots, integration boundaries, and modernization seams.
- Model lens
MCP-ready model
Vends validated slices of the Cognitive Model to AI tools through a controlled MCP service.
System layers
Two layers turn analysis into persistent understanding.
Builders create the Cognitive Model. The Run Layer exposes it through secure MCP services for people, tools, and AI agents.
Build Layer
A Python 3.12-based builder environment orchestrates ingestion, extraction, feature modeling, and AI processing.
- Web UI for initiating and monitoring builds
- Configurable attractors for code and documentation organization
- LLM inference through OpenAI API standards such as AWS Bedrock
Run Layer
A containerized MCP service vends models and documentation produced by the builders.
- API and UI for interacting with model content
- OAuth2 and Active Directory access control
- Model lifecycle support for build, deploy, review, and versioning
How it works
From ingestion to MCP.
Sage combines deterministic extraction, AI synthesis, configurable attractors, and SME validation to produce a persistent model that can be queried and reused.
- 01
Ingest code and documentation
Builders read repositories, docs, and bootstrap metadata with SME guidance.
- 02
Extract deterministic facts
Procedural analysis pulls structural facts before inference enters the loop.
- 03
Synthesize with AI and attractors
AI inference interprets the extracted facts through configurable architectural attractors.
- 04
Validate the knowledge graph
SME review, explicit gap flagging, and iterative builds improve accuracy over time.
- 05
Vend slices through MCP
The Run Layer exposes model content to users and AI agents with lifecycle controls.
Sage in the field
Recent traction from feared systems.
Common thread: teams and private equity operators bring Sage the codebases they cannot afford to misunderstand.
PE portfolio intelligence
Hospitality Platform
- CTO oversees 12 companies with 5-15M LOC each
- Self-serve visibility into portfolio health without interrupting engineers
- Meetings shifted from status updates to strategic direction
Knowledge cliff averted
Medical Coding System
- Lead architect announced retirement from a poorly documented system
- Sage built a comprehensive model in 3 working days
- Team now has an AI guide for life after the architect leaves
Proven results
Structure you can validate against.
Sage produces queryable outputs that support scoping, API design, risk assessment, migration planning, and technology decisions.
Swiss reinsurance giant
Multiple complex systems under Sage analysis · Replaced a 6 month discovery phase · First-ever portfolio visibility across the IT estate
Medical practice platform
Untamable codebase from multiple acquisitions · Sage revealed exact integration points and data flows · Conversations changed from status updates to fixing the payment stack
Logistics automation system
50 years of PICK BASIC stored procedures · Sage extracted all business rules in 4 days · First viable migration path in 50 years
Aggregate Roots
4531 merged from multiple sources
Bounded Contexts
23Maps to 23 proposed microservices
Business Rules
16395.6% code-derived, not inferred
Workflows
11273 are multi-step processes
Integration Boundaries
16842 RPC endpoints, 46 file integrations
Implementation Constraints
19073 compatibility, 41 architectural
FAQ
Copilot accelerates coding. Sage accelerates understanding.
Sage is built for modernization foundations: verified, queryable structure that AI tools and executives can trust.
Discuss a proof of conceptWe already bought Copilot. Why do we need Sage?
Copilot accelerates coding. Sage accelerates understanding by producing a verifiable atlas of complex systems.
Our team produced documentation using AI. Is that the same?
If you cannot query it, validate new code against it, or show a completeness guarantee, you have documents, not a modernization foundation.
Can't we just do the same thing with ChatGPT?
Large-context chat produces plausible text. Sage produces verified system knowledge that distinguishes critical business rules from incidental code.
Blog
Field notes
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Open blogCustomer signal
Bring us your worst system.
Modernization is where Sage is pulled first because uncertainty is existential. Start with the complex, outdated software your team cannot safely ignore.