PeakStart Knowledge Assistant

AI isn't just changing how code gets deployed — it's changing how teams work, and reshaping the roles that make up a squad.

Among those changes are AI-powered tools that save engineering teams real time and have become essential to how modern development happens. That importance cuts both ways: because these tools sit in the critical path, anything they miss has an immediate, direct impact on the team relying on them. Whether those tools give consistent results run after run matters just as much as whether they're right in the first place.

QA's job has to grow to match that. Verifying quality can no longer stop at automated UI tests on finished features — it has to extend to the tools a squad depends on to stay organized and informed: are they finding what they should, and are their results reliable enough to trust? The same job QA has always done for shipped software — building confidence that it works — now has to extend to the AI systems teams build on top of.

Below is a fictional company, Wasatch Onboard, based in Orem, Utah, building PeakStart — a B2B SaaS platform that streamlines employee onboarding for large corporations. This demo follows one of their development teams, led by Sarah Jenkins, using the team's own documentation — sprint plans, retrospectives, ad hoc discussions, bug reports, acceptance criteria, and more — to show what it actually looks like to verify one of these AI tools before trusting it.

See the Value

Concrete, real comparisons — not marketing claims. Expand any section below and click a question to ask PeakStart's docs and see the actual difference.

The Efficiency Benefit of Smart Filtering

Same question, same answer — see how much less it costs and how much faster it arrives when the AI only reads what's actually relevant.

Where Filtering Can Miss Things: Multi-Source Questions

Not every question favors filtering. For questions that need facts spread across several documents, a tightly filtered search can miss sources that the full document set would have caught. Compare both answers and see how many distinct documents each one actually drew from.

Why Fine-Tuning Matters

Same question, five different strictness settings (the same sweep as your Standard Check benchmark) — notice how much the answer and how much evidence it found can shift with a single setting change.