Make legacy systems ready for governed AI engineering.
SpecFocaL C2S turns legacy code into a reviewable baseline of behavior, contracts, architecture, evidence, and gaps. It helps engineering teams modernize safely and move toward agent-based development in controlled phases.
When sending your email, please include your name, company, official email, role, a brief project introduction, and repository type.
Explore evidence-backed research →A SPEC baseline for controlled change
Decades of software evolution leave behind undocumented behavior, hidden constraints, and design decisions that are difficult to recover from code alone.
The C2S engine makes that system reviewable. It correlates implementation, documentation, tests, build configuration, and execution evidence into a structured engineering baseline.
What teams gain
- ✓ A clear view of current behavior and system boundaries.
- ✓ Feature lists, component views, and recovered system structure.
- ✓ HLD and LLD artifacts for stakeholder review and correction.
- ✓ Product and machine contracts that clarify responsibilities.
- ✓ Patterns and API limitations, with recommendations for improvement with minimal risk.
- ✓ Red flags for risky multithreaded pattern usage.
- ✓ Actionable insights linked to evidence, impact, risks, and next steps.
- ✓ Explicit gaps, risks, assumptions, and unresolved questions.
- ✓ A baseline for reducing unintended regression during change.
C2S capabilities
Specification recovery and engineering analysis for complex brownfield systems.
Multi-dimensional SPEC
Recover behavioral, architectural, environment, dependency, compatibility, and operational contracts from the existing system.
HLD and LLD recovery
Make hidden design decisions, interfaces, state transitions, constraints, and regression obligations available for human review.
Gap and evidence analysis
Show what is supported by evidence, what is inferred, what is missing, and where customer review is needed.
Architecture review
Assess components against their contracts and identify structural constraints, dependency risks, red flags, and opportunities for better design patterns.
Performance insight reports
Identify throughput-limiting interfaces, inefficient interaction patterns, and actionable improvement opportunities using code, SPEC, tests, and supplied execution evidence.
Agent-based SDLC readiness
Create the contracts, boundaries, and review workflow needed to introduce AI-assisted engineering while continuing to support the existing product.
From baseline to better design
C2S is more than a specification builder. The recovered SPEC gives a principal engineer or architecture team a structured way to examine how a component behaves and where its design limits the system.
The result is an actionable report: what is constrained, why it matters, what could change, and which compatibility or regression obligations must be preserved.
Analysis outputs
- → Throughput-limiting API or component boundaries.
- → Design patterns suited to the observed constraints.
- → Proposed interface or endpoint improvements.
- → Red flags in usage, integration, and interaction patterns.
- → Evidence, expected impact, risks, and next actions.
Evidence-backed research
SpecFocaL prototypes C2S across open-source and systems projects to test contract recovery, design analysis, and actionable engineering recommendations on real brownfield code.
Research focus
- Behavioral and source-contract recovery from selected open-source and systems repositories.
- Evidence-linked HLD, LLD, gap, and change-impact reports.
- Architecture and performance analysis without requiring access to a customer production runtime.
- SPEC boundaries that make AI-assisted engineering more reviewable and predictable.
Supported repository types and findings are reported as part of the assessment outcome.
View open-source research on GitHub →About the founder
Principal Engineer & Independent Researcher with more than two decades of experience at HPE, including architecture leadership for TeMIP and complex enterprise systems. Focused on spec-driven SDLC engines for deterministic AI agents and evidence-linked intelligence for brownfield modernization.
Beyond systems design
Photography and systems engineering share a discipline: recognizing patterns, framing structure, understanding depth, and paying attention to detail.