Topaas.AI

Topaas (Topology-as-a-Service) is a codebase intelligence layer built for enterprises running large, legacy systems. Its core engine — Laplace — applies spectral graph theory to transform codebases of 1M+ lines into agent-ready system intelligence: dependency graphs, Laplacian spectra, component maps, and behavioural risk surfaces.

AI coding agents fail on large codebases because they see tokens, not structure. Topaas computes the topological invariants of a codebase — clusters, bottlenecks, connectivity — and hands the agent a specification it can execute at a scale no context window holds.

Early benchmarks on open-source production substrates (incl. Apache Hadoop): 117× token reduction per query, 93.75% file localization accuracy (top-5), 93% vs. 36% correctness on full codebase rebuild. 3M LOC resolved into a single graph (~117K nodes, ~419K edges).

In stealth. Onboarding design partners. Team alumni: AWS AI, Meta, Microsoft, Wharton.

Pain point: AI coding agents are now widely adopted, but large engineering organizations are still seeing only incremental productivity gains — often around 20–30%, not full organizational transformation. The hard part is enterprise-grade understanding of millions of lines of existing code, because most companies were built long before AI agents existed. Solution: We build the missing data layer (Laplace Engine) between legacy codebases and AI coding agents. The Laplace Engine converts large codebases into structured feature graphs, enabling agents to understand system context, generate specs, implement changes, and pass risk-based production-grade review. We use this wedge to help traditional software companies modernize core systems and become AI-native.