AI teams waste thousands monthly on unnecessary fine-tuning when prompt engineering or RAG would deliver better ROI, or vice versa - they over-rely on expensive API calls when a small fine-tuned model would pay for itself in weeks.
Data scientists prototype brilliant models in Jupyter notebooks, then their code sits unused for months because productionizing requires complete rewrites - the "notebook-to-production" gap kills 70% of ML projects.
RAG systems often retrieve 10 chunks that say the same thing in different words, wasting context window space and degrading LLM output quality - developers need semantic diversity scoring, not just similarity ranking.
Data engineers have decades of complex SQL logic in stored procedures and ETL jobs that need to be rewritten in Python dataframe libraries - manual translation is error-prone and takes weeks per pipeline.
As AI agents like Claude Skills and custom MCP servers proliferate, developers face a new dependency hell: incompatible skill versions, conflicting tool definitions, and broken agent workflows. This platform automatically resolves skill dependencies, tests compatibility, and ensures your AI agent stack works together seamlessly.
After another high-profile DRM bypass (Amazon Kindle web DRM just got cracked), companies need proactive security testing. This platform automatically scans DRM implementations for common vulnerabilities, misconfigurations, and bypass techniques - finding weaknesses before attackers do.
Gemini 3.0 just dropped via A/B test, GPT-5 rumors swirl, and Claude keeps shipping new models - but how do you safely test them in production? This platform orchestrates multi-model A/B tests across your entire LLM stack, automatically measuring quality, cost, and latency to identify the optimal model for each use case.
As Cloudflare Sandbox SDK and similar platforms proliferate, developers need confidence their sandboxes actually isolate untrusted code. This platform automatically tests sandbox environments with thousands of escape techniques, verifying isolation guarantees before production deployment.
"Your data model is your destiny" - but every schema change is a leap of faith. This platform uses AI to predict the exact impact of database migrations before you run them: query performance changes, breaking API calls, data integrity issues, and rollback complexity. See the future of your schema changes before committing.
The pain of building AI agents from scratch when most capabilities (web scraping, data extraction, API integration) are repeatedly reimplemented across thousands of projects.