The 13-Layer Quality Engine
Every conversion passes through 13 layers of deterministic and AI-driven validation. 90%+ of artifacts clear validation, with per-line provenance on every output.
Per-file dialect detection
Each artifact is scanned to detect its source dialect before conversion.
Chunking
Large artifacts are split into context-safe chunks that preserve statement boundaries.
LLM conversion
Complexity-adaptive prompting drives the Claude Sonnet batch conversion.
Sanitization
Fence stripping, comment cleanup and output normalization.
Deterministic rewrites
INTERVAL fixes, type mapping and dialect-specific transforms applied in code.
Schema engine
DDL type fixes and clause cleanup for schema artifacts.
sqlglot transpilation
AST-level transpilation cross-checks the LLM output against a parser.
Syntax validation
SQL parse and Python AST checks catch malformed output early.
Compile-friendly validation
Output is validated to be compile/parse-clean on the target engine.
Auto-repair loop
A capped Claude Haiku repair pass fixes failed conversions automatically.
Sync reconversion
Empty or placeholder outputs trigger a synchronous re-conversion.
Semantic scoring
LLM-based semantic scoring on a sampled subset measures meaning preservation.
Blended quality score
70% deterministic + 30% semantic produces the final confidence score.