AI is only as good as the problem you choose to solve, the decisions you make when specifying it, and your ability to evaluate the result. Snogren Labs brings 12 years of domain expertise in healthcare, finance, and insurance to all three.
The technology is there. The tooling is improving fast. Success is entirely human — the quality of the problem, the precision of the direction, and the discipline of the evaluation.
Our biggest limitation is we don't know what to ask. Domain expertise is what turns "we should use AI" into a specific, solvable problem. Twelve years inside healthcare data, financial systems, and insurance workflows means we arrive knowing which problems are worth solving before the first conversation ends.
The secret is knowing what to specify and what to leave to the model. The decisions you make when specifying it determine everything downstream. Over-constrain and you get brittle, literal output that misses the intent. Under-specify and the model fills the gaps with its own assumptions. The skill is finding the minimum specification that produces the result you actually want — a discipline that sits at the heart of quality engineering. Our TPO framework applies this directly to AI direction.
Read about TPO →Most AI workflows end with a human being dazzled. We treat AI output the way QA treats software: systematically, with defined criteria, looking for the failure mode that looks like a pass. We designed our own LLM evaluation combining RAG, instruction-following, and code generation testing. Our regression agent caught a bug it wasn't supposed to be able to catch — and we still don't fully understand how.
Read about the regression agent →Risk-driven quality engineering for teams where escaped defects have legal, financial, and physical consequences. We build autonomous testing pipelines, implement AI-assisted analysis, and restructure QA around risk rather than coverage. Domain expertise in HIPAA, HITRUST, healthcare payment systems, and financial data.
We identify where AI fits into your workflows, build agents and integrations that keep you in control, and implement them in production. Scoped engagements with working software as the deliverable.
Custom software built for your business — AI-powered or conventional, depending on what the problem needs. Quality engineering is part of the build, not a phase at the end.
12 years building software products, transforming quality engineering, and implementing AI systems that solve real business problems.
Built and deployed five production AI agents on a healthcare payment accuracy platform serving 1,000+ hospitals — autonomous regression testing, requirements analysis, change-impact analysis, SDET automation, and exploratory testing, coordinated as a full SDLC pipeline. Shifted a 4.6:1 dev-to-QA ratio to 16:1 while expanding from one product to three, with the same team.
Built and shipped independently:
jobeval.ai — Job search on your own terms. User-defined scoring criteria, multi-API scraping, and AI ranking. You define what you want — not a job board's algorithm.
annas-list.vercel.app — A task management system built from years of paper-based practice. Capture ideas, prioritize, estimate, and order.
Whether you need software built, AI integrated, or a QA function that scales — it starts with ideation, communication, and evaluation.