Delivered in Production. Engineered for Scale.
A documented archive of our production AI systems. Every deployment runs with deterministic guardrails, sub-second inference budgets, and 100% proprietary client IP inside private VPC boundaries.
Inspect Production Deployments & Verified Outcomes.
Switch between architectural topologies, before-vs-after empirical benchmark data, and container stack manifests for our flagship production releases.
Autonomous Codebase Indexing & Living Architecture Docs from Git Commits
Engineering teams were spending 15–20% of sprint time writing and updating docs that went stale within days of merging. Architecture diagrams lived in Notion pages nobody trusted or updated.
We built a GitHub Actions-triggered AST parser using Tree-Sitter that reads every PR diff, extracts symbol graphs, and feeds them into a Claude 3.5 Sonnet agent that regenerates only the changed documentation sections — keeping docs perpetually current with zero developer effort.
Every Release Meets Three Hard Contracts.
Architectural guarantees built into all production deliveries, ensuring long-term resilience, deterministic safety, and enterprise compliance.
Zero Data Retention & VPC Isolation
All models, vector indices, and customer audio/text frames execute strictly within client-owned AWS, GCP, or on-premises Kubernetes infrastructure.
Pydantic & AST Deterministic Gates
Every probabilistic AI generation passes strict schema validation, symbol graph verification, and safety harnesses before state commit.
Safe Atomic Rollback Windows
Broadcast, telephony, and agent pipelines feature shadow-queuing, staged delivery, and operator kill-switches within SLA limits.
All models, vector indices, and customer audio/text frames execute strictly within client-owned AWS, GCP, or on-premises Kubernetes infrastructure.
Your next release runs on our stack.
Book a 30-minute strategy call. We'll audit one workflow, benchmark it against our best-of, and hand you a written roadmap — free.