Catch the defect before your users do.
We build and run the quality layer that lets you ship faster without shipping bugs: AI-built test automation, regression suites, plus performance, API, and exploratory QA.
The same pre-release discipline that holds a 200+ location chain at twice-a-week releases with zero critical defects — wired into your CI and assigned to you.
Because most teams test the way they always have while trying to release the way frontier companies do — and the two don’t fit. Coverage is thin and hand-written, so it can’t keep up with the change rate; releases get batched and slowed to stay safe; and the defects that slip through are caught by customers instead of a suite. The result is the worst of both: slow and fragile.
The cost of that gap is not abstract. The Consortium for Information & Software Quality puts the price of poor software quality in the US at $2.41 trillion for 2022, including roughly $1.52 trillion in accumulated technical debt — the rework that piles up when quality is deferred (CISQ, “The Cost of Poor Software Quality in the US: A 2022 Report”).
Good software testing services exist to move that number on your books: catch the defect early, when it costs a fraction of what a production incident does, and make speed and stability the same decision instead of opposing ones.
Testing is not one activity. It is a set of distinct disciplines, each guarding a different failure mode. For each: what it does, the benefit it produces, and how that plays out.
Automated unit, integration, and end-to-end suites cover the paths that must hold on every release, run on each change rather than on a sampled subset. Benefit — release confidence at speed; defects caught in minutes, not by customers. A broken checkout or login surfaces in the pipeline instead of in production.
For example, a developer changes a shared pricing function on a Friday; the suite flags the three downstream flows it quietly broke before the merge, so the bug never reaches a customer’s cart over the weekend.
Durable coverage of everything that already works, expanded and maintained as the product grows, so new code can’t silently re-break old behavior. Benefit — lower defect-escape rate as the codebase ages. The fear of “what else did this touch?” stops slowing every release.
For example, a feature flag toggled for one tenant would have reopened a year-old bug for everyone else — the regression suite catches it in CI instead of in a support queue.
Realistic traffic modeled and run against the system to surface slow queries, resource contention, and the breaking point — before real volume finds them. Benefit — protected uptime and conversion on the days that matter most. The peak-traffic outage becomes a tuning ticket caught in a test, not an incident.
For example, a checkout path that holds fine at normal load but collapses at 5× is found in a load test weeks before the seasonal spike, not during it.
Suites that exercise contracts, authentication, error handling, and edge cases at the service boundary, so integrations don’t break silently when a payload shape shifts. Benefit — fewer integration failures and faster, safer service changes.
For example, a backend team tightens a field’s type; the contract test fails the build immediately instead of letting a malformed response reach a partner’s system unnoticed.
Skilled human testing that probes for what a script never thinks to check — confusing flows, broken states, the bug that only appears when a real person uses the product oddly. Benefit — caught usability and edge-case defects automation can’t see.
For example, a tester following an unusual but plausible refund-then-reorder sequence surfaces a state bug that no scripted path would have exercised.
AI accelerates the slow parts of QA — generating coverage for new code, analyzing gaps, and triaging failures so engineers chase real breaks, not flaky noise. Benefit — far broader coverage for the same team, maintained as the product moves.
For example, a new module ships with a generated regression set on day one instead of waiting weeks for someone to hand-write it — and stale tests are flagged before they rot into false alarms.
The scope below is the difference between coverage that holds your releases and a test folder that everyone ignores because it’s always red.
We start by finding the highest-risk gaps — the flows where a defect costs you the most — and write a test plan ranked by risk, not by what’s easy to automate. You get an honest map of what’s covered, what isn’t, and what to fix first.
We build unit, integration, and end-to-end suites and wire them into your pipeline so they run on every change and block a bad merge before it ships — not on a nightly sample that finds the defect a day late.
Using our patent-pending Aegis AI process, we generate and maintain regression coverage well past what a team could write by hand, and keep it current as the product changes — so the suite stays trustworthy instead of decaying into ignored failures.
We model realistic traffic to find bottlenecks and breaking points before peak volume does, and cover service contracts, auth, and error handling so integrations don’t fail silently.
Skilled human testers probe the flows and edge cases scripts miss, covering the usability and state bugs that only surface when a real person uses the product.
You get defect and coverage dashboards, the full test suite in your own repositories, and a team trained to run and extend it — so the quality discipline stays after we step back.
What you get when you hire us — all assigned to you under full IP transfer
This is distinct from our DevSecOps services, which secure the pipeline and supply chain, and from DevOps services, which automate build and release — testing is the layer that proves each change is correct before either ships it.
One accountable lead, fixed scope, no handoffs — the same delivery model behind all our AI development work, tuned for quality.
Audit current coverage, release process, and defect history; rank the highest-risk gaps.
Output: a prioritized test plan & the quality baseline we’ll be measured against
Build the automated, regression, performance, and API suites against that plan, in your own repositories.
Output: a working test suite covering the flows that matter most
Wire the suites into your CI/CD so they run on every change and gate releases, with results visible to the whole team.
Output: a pipeline that blocks defects before they ship
Maintain coverage as the product moves, report escape rate and pass rate, and train your team to own it.
Output: a durable quality layer & a team that runs it
Most engagements reach a steady state in 4–8 weeks, with full work-for-hire IP assignment signed at kickoff and payment tied to the outcomes we agreed to move.
Pre-release quality discipline is exactly what we are known for, and the clearest evidence is BJ’s Restaurants — a 200+ location chain whose software is critical to daily operations. When we began, releases went out roughly every two weeks in heavy, cautious batches.
We applied the Aegis AI process — AI-augmented planning, smaller units of work, and the pre-release quality core that is the heart of this page: AI code review, regression prevention, and test-coverage insight on every change, with continuous production monitoring after.
The result, sustained across four years and ongoing: release cadence moved from every two weeks to twice a week, with zero critical defects the entire time. A traditional, multi-location enterprise now ships at the cadence and stability of a frontier tech company — because the testing discipline made faster releases safer, not riskier. That is the same quality layer we build into your pipeline.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We’ll tell you plainly where you’re over-testing low-risk paths and under-testing the ones that can actually hurt you.
We proved this exact discipline at scale. Twice-a-week releases with zero critical defects across four years at a 200+ location chain (BJ’s) — testing as the thing that enables speed, not the tax on it.
AI-built coverage, not hand-cranked suites. Our Aegis AI process generates and maintains regression coverage past what a team writes by hand, and keeps it current — so the suite stays green-for-real instead of rotting into ignored failures.
Founder-led, one accountable lead. No account managers, no offshore handoff — the person who scopes the coverage answers for the escape rate.
Built to transfer. Every test asset, dashboard, and runbook is assigned to you, and your team is trained to own the quality layer when we step back.
Payments, fraud, and real-time decisioning paths where a single escaped defect is a financial or compliance event; coverage and audit trails built to match. Fintech software →
Patient-data and clinical workflows inside HIPAA-compliant architectures, where correctness is a safety requirement, not a preference. Healthcare software →
Software-critical chains (retail, restaurants, field operations) where a bad release hits hundreds of sites at once and regression coverage is the thing standing between a change and an outage.
What teams want to know before they bring in software testing services.
The full quality layer: QA strategy and a risk-ranked test plan, automated unit/integration/end-to-end suites wired into CI, AI-generated regression coverage, performance and load testing, API and contract testing, and skilled manual and exploratory QA — with coverage and defect-escape reporting and a trained team at handover. We scope to your highest-risk gaps first rather than testing everything equally.
Both, deliberately. Automation owns the repeatable, high-volume regression paths that must run on every change; skilled manual and exploratory QA owns the usability, edge-case, and state bugs a script never thinks to check. The split is decided by risk and change rate per area — we automate what pays to automate and keep humans on what they’re uniquely good at.
Yes — wiring suites into your existing pipeline so they run on every change and gate releases is the core of the work. We build the tests in your own repositories and integrate with whatever you run, so a bad merge is blocked before it ships rather than caught a day later in a nightly run. The result is the continuous-testing pattern that DORA research ties to higher delivery performance (Google Cloud, DORA).
AI accelerates test generation, coverage-gap analysis, and failure triage — but every generated test is reviewed and held to the same standard as a hand-written one, and we measure the suite by defect-escape rate, not test count. The leverage is real: 72% of quality teams report faster automation from generative AI (Capgemini, World Quality Report 2024-25). A suite full of flaky, meaningless tests is worse than none, so triage and maintenance are part of the scope, not an afterthought.
Performance and load testing are part of these services — modeling realistic traffic to find bottlenecks and breaking points before real volume does. Security testing lives primarily in our DevSecOps services, which secure the pipeline, dependencies, and supply chain; for an engagement that needs both, the same accountable lead coordinates them so coverage and security gates run in one pipeline.
Against a baseline set at kickoff. We track defect-escape rate (defects reaching production vs. caught pre-release), test coverage on the highest-risk flows, and pipeline pass rate, and report them every sprint. The honest measure of QA is not how many tests exist — it’s how few defects reach your users, which is exactly the number we hold ourselves to.
You do — completely. Every suite, dashboard, and runbook is built in your own repositories and transfers under full work-for-hire IP assignment signed at kickoff, and your team is trained to run and extend the coverage. Keep us on a reduced retainer or take the keys; the engagement is designed around the handover, not around locking you in.
Most engagements reach a steady state in 4–8 weeks under a fixed-scope arrangement with one accountable lead, starting with the highest-risk gaps so you see escaped-defect risk drop early rather than waiting for full coverage. Cost depends on scope and the size of the surface to cover — our AI development cost guide gives real ranges, and payment is tied to the outcomes we agree to move.
Thirty minutes · No pitch deck
Tell us what you’re releasing and how — we’ll scope the coverage, name the highest-risk gaps, and give you a measured path to shipping faster without shipping bugs.