7FT QaaS · Quality as a Service
We build your QA process,with AI at its core.
We design your QA strategy from scratch — or strengthen the process you already have — and bring AI into every step. 16 years of production QA, turned into a process your product, dev and QA teams can actually run.
Weeks → days
full QA cycles
Hours → minutes
quick-fix validation
Days, not weeks
to get a new team operational
01
What we do
Strategy and process come first. Everything else is built on top.
The core · QA strategy & process
Design your QA strategy, or shore up the one you have
We assess how quality works in your team today and define how it should work: what to test and when, who owns what across product, dev and QA, what gets automated, what evidence a release needs. Then we bring AI into the process itself — so the same team covers more, faster.
Diagnosis
Current process, gaps and risks
Strategy & test plan
Prioritized by risk and business impact
AI in the process
Test case generation, triage, automation, reporting
Team workflow
Product · dev · QA as one loop, wired into Slack and Jira/Linear
Built on that foundation
Release automation
Every build validated, automatically
Regression suites written in natural language with Watchman, our automation engine, running on every build and deploy. Smoke and sanity on each release, quick-fix validation in minutes.
- Automated regression
- CI/CD integration
- Release sign-off with evidence
QA services
Hands-on testing by senior QAs
The work automation can't replace: test strategy and planning, exploratory and functional testing, regression passes, device and browser coverage, and clear bug reports your devs can act on.
- Test plans & test cases
- Exploratory & functional
- Web & mobile
AI product quality
Testing what isn't deterministic
Agents, copilots and LLM features fail in ways a traditional test plan doesn't anticipate. We evaluate them through conversation and real scenarios, not just clicks.
- Agent evaluation
- Regression on model changes
02
Judgment is what we bring
Anyone can automate clicks. Knowing what to test is what sets us apart. Automation and AI without a strategy test everything equally and miss what matters. Our strategies start from 16 years of production QA across very different domains:
16years in gaming
Our origin · Mobile games
We founded a QA company that was acquired by Jam City, and spent 16 years testing mobile games at global scale: thousands of devices, millions of players, constant releases. That's where we learned to find the bugs that only show up under real pressure.
And we apply it at
Salt.AI
Agentic AI platforms
Testing non-deterministic products: bug categories a traditional test plan doesn't cover.
Grailz.com
E-commerce
Critical conversion and payment flows, where every friction point is a lost sale — not just a ticket.
Settle
Mobile app · iOS
A shared-expenses app: flows where every number has to add up, across devices and real-world use.
iTrials
Clinical research / Pharma
QA under strict regulation for global sponsors: evidence discipline and auditable traceability.
That judgment shapes the strategy — and the strategy drives everything else: what we automate, what we test by hand, and what we keep watching in production.
03
QA that keeps up with your releases
Product, development and QA moving as one loop — from the first build to production monitoring.
- 1
Strategy
QA strategy and test plan prioritized by risk: which flows matter, what to break first, what evidence to require.
- 2
Automate
Critical paths turned into regression suites with Watchman, plugged into your pipeline.
- 3
Validate each release
Automated runs plus targeted manual and exploratory testing on what changed. Fixes re-validated in minutes.
- 4
Monitor
After launch, we keep watching production and feed user feedback back into the plan.
What we cover
Functional
End-to-end flows, data consistency (prices, stock, currency conversion), form validation.
Security
Headers and exposed configuration, basic attack surface — observation, never exploitation.
Quality & accessibility
Tone and copy consistency, screen readers, overlapping elements, responsive.
Performance & health
Console errors, mixed content, broken pages, missing images, load times.
04
Results with real teams
Same method, four very different products.
We applied AI-driven practices to accelerate every stage of QA. Product, development and QA started moving as one loop — reaching a pace that wasn't possible before, from the initial release through feedback collection to production monitoring.
- QA cycles
- Weeks → days
- quick-fix validation
- Hours → minutes
- product · dev · QA
- One loop
Working inside an AI-native environment brought onboarding of a brand-new team close to zero. We were operational in days instead of weeks and shipping value from the very first week — people focus on strategic decisions while the AI does the heavy lifting.
Because the environment is fully governed and managed by AI, any extension or improvement can be applied and tested very quickly — cutting costs and time to market.
- onboarding time
- ~0
- to be operational
- Days, not weeks
- delivering value
- Week 1
In clinical research, internal systems have to work every single day — and leave evidence that they did. For iTrials we automated the testing of their internal systems, set up daily release verification, and monitor their systems continuously, with the traceability a regulated environment demands.
- of internal systems
- Automated testing
- verification
- Daily release
- of systems
- Continuous monitoring
Same AI-accelerated QA approach, applied to a mobile product: shorter test cycles, near-instant validation of fixes, and product, development and QA working in sync from launch through user feedback and ongoing monitoring.
- QA cycles
- Weeks → days
- end to end
- Launch → monitoring
05
How we start
- 1
Scoping conversation
We understand your product, your stack, your release cadence and where it hurts most today.
- 2
Process diagnosis
How quality works today, where it breaks, and where AI can take load off the team.
- 3
Strategy & test plan
~2 weeks to define the process, map flows, priorities and evidence criteria, and decide what to automate.
- 4
Up and running
The new process running with AI built in, automation in your pipeline, and the team testing each release.