Sage AI Technologies: Frequently Asked Questions

What is Sage?

Sage is a deep contextual analysis platform for enterprise software. It turns source code into a structured, queryable context graph that your whole team can use, not just developers. Sage’s patented cognitive pre-compiler reads your codebase and produces plain-language insights about architecture, business logic, data flows, integrations, and risks. Unlike code generators, Sage does not write code. It reveals what your systems actually do, in the language your business already speaks. Think of it as a permanent, compounding memory of your entire software estate.

How is Sage different from tools like GitHub Copilot or SonarQube?

GitHub Copilot generates code. SonarQube checks code quality and security. Sage does neither. Sage creates a structured, fact-based model of your codebase (the context graph) and serves that context to frontier AI models via Model Context Protocol (MCP). The difference matters: without Sage, tools like Copilot guess because they have never seen your code in context. With Sage, those same tools receive precise targeting data and produce production-grade results. In one validated case, an SSO migration estimated at 30 person-months was completed in six weeks by a single engineer using Sage-targeted AI. Sage makes your existing AI tools dramatically more effective rather than replacing them.

What problems does Sage solve?

Sage addresses five enterprise challenges that share a common root: your systems are too large and complex for anyone to fully understand manually.

• Unlock knowledge from legacy systems before the people who understand them retire (the knowledge cliff).

• Accelerate modernization and cloud migrations by giving AI models the targeting data they need to produce reliable output on real production code.

• Onboard new engineers faster, with measured time-to-first-production-commit of three days in deployed environments.

• Generate audit-ready compliance documentation, data-flow diagrams, and architecture maps directly from the code.

• Support M&A due diligence and post-merger integration by producing a defensible technical assessment within a deal clock.

How does Sage work?

Sage connects to your codebase via Git or CI/CD pipelines. The platform’s cognitive pre-compiler ingests your entire software estate and builds a structured context graph: a queryable model of architecture, data flows, business logic, integrations, and dependencies. This typically takes less than one week for a million-line application. Your team queries the context graph through the Librarian interface using natural language, like asking an expert engineer who has read every line of code. Your AI tools access the same context through Model Context Protocol (MCP), so they operate on verified facts rather than guessing from raw text.

What is the first step to getting started with Sage?

Point Sage at your codebase. This is usually done via a secure cloud connection to your Git repository, but on-premises deployments are also available. From there, Sage builds a contextual model of your system automatically. No changes to your source code or development workflow are required. The setup is non-invasive: Sage reads your code, it does not modify it. Most organizations start with a single application or domain as a 90-day pilot, measure the impact on a specific decision they are already facing, and then decide whether to scale.

Does Sage support my tech stack?

Very likely. Sage has catalogued systems spanning legacy and modern stacks, including:

• Legacy and mainframe: COBOL, IBM S/370 assembler, Micro Focus ERP, C++.

• Enterprise platforms: Java, C#, .NET, SAP-centered finance stacks, regulatory calculation engines, reinsurance contract hubs.

• Modern frameworks: Python, JavaScript, TypeScript, React, TensorFlow, and AI orchestration tools.

• Environment types: monorepos, multi-repo environments, and mixed-architecture estates.

Sage is particularly effective in complex, mixed-architecture environments where no single person understands the full system.

How does Sage help with understanding complex codebases?

Sage identifies that only 17 to 22 percent of a typical legacy codebase is actual business logic. The rest is scaffolding, repeated patterns, and boilerplate. The cognitive pre-compiler separates signal from noise and builds a structured context graph, a fact-based model of what your software actually does. Your team can then ask natural-language questions about architecture, dependencies, data flows, and business capabilities through the Librarian interface. The result is architecture analysis that takes hours instead of months, with traceable, auditable outputs grounded in verified structure rather than guesswork.

Can Sage help map business logic within a codebase?

This is one of Sage’s core capabilities. Most legacy codebases contain millions of lines of code, but only 17 to 22 percent represents actual business logic. Sage’s cognitive pre-compiler separates business logic from infrastructure scaffolding and maps it into a structured context graph. The output includes architecture diagrams, data-flow maps, capability inventories, and integration maps that engineers, architects, and business stakeholders can query in natural language. This is particularly valuable for organizations that need to understand what their systems do before modernizing, migrating, or explaining them to auditors.

How does Sage accelerate legacy modernization?

Sage creates a complete understanding of your existing system before any code is changed, which is the step most modernization projects skip or underestimate. The context graph feeds directly into frontier AI models (such as Claude, GPT, or Gemini) via Model Context Protocol, giving those models the precise targeting data they need to produce production-grade modernization output rather than plausible guesses. With Sage targeting, agentic AI achieves 80 to 90 percent first-pass completion on modernization tasks, rising to 95 to 96 percent iteratively. In one validated case, an SSO migration estimated at 30 person-months (five engineers over six months) was completed in six weeks by a single engineer using Sage. The platform supports COBOL, mainframe, and enterprise platform modernization across codebases of 12 million lines and beyond.

How does Sage improve developer onboarding?

The biggest barrier to onboarding is not the developer’s skill. It is the gap between what the codebase does and what is documented about it. Sage closes that gap by producing living, queryable documentation directly from the code. New developers get instant access to architecture maps, integration inventories, business-logic explanations, and natural-language search across the entire codebase through the Librarian interface. In measured deployments, this has reduced time-to-first-production-commit for new engineers to three days. Sage also compounds knowledge over time: as senior engineers retire or move on, their understanding of the system is preserved in the context graph rather than lost.

Can Sage generate compliance artifacts automatically?

Yes. Sage produces compliance-ready artifacts directly from your codebase, including system maps, data-flow diagrams, architecture documentation, integration inventories, and traceable business-logic extraction. Because these artifacts are generated from the actual code (not manually written documentation), they reflect the system as it is and can be regenerated on demand as the codebase evolves. Sage also identifies PII and PHI data flows automatically. This is particularly valuable in regulated industries such as financial services, insurance, and healthcare, where auditors require demonstrable traceability between systems and their documented behavior.

Can Sage be deployed on-premises?

Yes. While Sage is built for cloud integration, enterprise customers can opt for a secure on-premises or VPC deployment if required. This is common for regulated industries (financial services, insurance, healthcare) where data residency and security policies restrict cloud-based processing.

Does Sage access personal data?

No. Sage analyses technical assets only: your codebase, configuration, and metadata. It does not access user data, customer data, or personally identifiable information. The platform includes an automatic shutdown on sensitive data holdings (PII/PHI detection) as a built-in safety mechanism.

Is Sage GDPR compliant?

Yes. Sage meets enterprise-grade security and privacy standards, including GDPR. Code is processed securely, not stored after analysis, and deployments follow best-practice security protocols. On-premises and VPC deployment options are available for organizations with strict data residency requirements.

How accurate are Sage’s insights?

Sage’s context graph is built from verified code structure, not from LLM inference, so the underlying model is deterministic and traceable. When Sage’s context is fed to frontier AI models for tasks like modernization or code analysis, the targeted output achieves 80 to 90 percent first-pass completion, rising to 95 to 96 percent with iterative refinement. For organizations with domain-specific language or unusual architectures, Sage can be tuned further to increase contextual precision. All outputs are auditable and traceable back to the source code.

Where can I schedule a demo of Sage?

Request a demo at sage-tech.ai. Demos typically run 30 minutes and show Sage working on real code, including legacy COBOL systems and modern enterprise platforms. The team can tailor the demo to your specific technology stack and use case, whether that is mainframe modernization, developer onboarding, compliance documentation, or AI transformation readiness.