QA as a Service · AI-Led Development · Production Reliability
We test and harden software, AI agents, and production workflows. When the fix needs ownership, we build it too.
For software and AI products that cannot afford to fail.
Dedicated quality engineering for software and AI systems: user flows, regression, edge cases, performance, accessibility, and release readiness owned by a team that knows how production fails.
What gets tested
This is the core wedge: validation before, during, and after the build.
End-to-end product builds using AI-assisted engineering, from architecture to interface. We design the workflow, implement the system, and build the UI to match how users actually operate. Custom, not templated. The same team that owns the build owns the quality.
Agents fail silently and confidently. We build eval harnesses, test tool use, enforce output boundaries, and verify escalation behavior before users see the failure.
HIPAA, SOC 2, GDPR, ISO 27001. Access control, auditability, data handling, and credential exposure reviewed before regulators, attackers, or customers discover the gaps.
Need QA coverage or a build team that owns validation?
Scope QA or BuildSystems We Validate & Build
Most teams meet us through QA as a Service. When the gap is architectural, workflow, or product-level, we can own the fix too: complex systems with business logic, model behavior, integrations, permissions, audit trails, and failure modes that need to be validated before production.
Systems that replace manual handoffs, spreadsheet operations, approval queues, and brittle internal processes with software teams can actually operate.
Agentic systems that do useful work inside real constraints: source grounding, tool permissions, escalation rules, evals, and auditability.
Software for healthcare, legal, finance, and other environments where data handling, permissions, evidence, and compliance cannot be bolted on later.
Products designed and built from the ground up: architecture, backend logic, integrations, permissions, and a UI built around the real workflow. For teams that need more than a template, including systems that sit between APIs, data, reviewers, and business rules. QA is embedded from day one.
Why QA-Led Delivery Works
We do not sell prompt-to-app experiments. We use AI to move faster, while the same team owns engineering review, test strategy, security checks, release hardening, and production controls.
Before implementation accelerates, we define the workflows, user paths, business rules, risk areas, and release evidence the system will be judged against.
The UI is designed around how users actually work, not around what a component library provides. For complex or regulated workflows, the interface is a product decision, not a cosmetic layer.
Architecture, permissions, data boundaries, compliance tradeoffs, and business-rule interpretation stay with senior engineers. AI accelerates the work, but it does not own judgment.
Acceptance criteria, evals, regression paths, audit logs, documentation, and known risks are built alongside the product, not attached after the sprint is over.
Zero
production incidents reported after QA-led review across monitored AI deployments
64%
faster review cycles on an AI-assisted regulated workflow, validated in production for 6+ months
72 hrs
to surface critical security gaps before an AI-built product reached real users
100%
custom-designed interfaces; no templates, no generic app shells
How We Work
AI accelerates the build. QA keeps the work honest. The same team that builds owns the validation, so correctness is never handed off to a separate queue.
We map the business process, users, data, systems, constraints, and risk areas before writing code. The build starts from the real workflow.
DiscoveryWe use AI-assisted engineering to move quickly, while senior humans own architecture, integrations, security decisions, and maintainability.
BuildEvery system is tested for user flows, edge cases, permissions, data handling, and release readiness by the team accountable for the delivery.
ValidateMost engagements start as a focused QA review. Some expand into system builds when the fastest path to quality is owning the fix, not just reporting the gap.
The Fit
You are shipping quickly with a small team, AI-assisted code, and real customers waiting. We give you the QA layer you do not have time to build internally.
Your team is strong, but QA is understaffed, release surfaces are growing, and AI has increased the volume of code to verify. We embed as a quality partner, not a report vendor.
Healthcare, legal, financial services, life sciences, and other regulated teams cannot treat speed and control as opposites. We validate the workflows where trust is part of the product.
We're probably not the right fit for throwaway prototypes, simple brochure sites, or low-stakes automation. We work where correctness, workflow depth, and operational trust matter.
Where We Operate
These are examples, not boundaries. The common thread is software where demos are easy, production is unforgiving, and the real work lives in workflows, integrations, permissions, evaluation, and trust.
Clinical documentation, patient operations, medical coding, triage, scheduling, and EHR-adjacent automation where accuracy and auditability matter.
Brand-aligned, compliant content workflows that need MLR awareness, claim checks, regulatory context, and faster commercialization cycles.
Agents that understand websites, classify pages, perform actions, and interact with unreliable web surfaces under privacy and security constraints.
Contact-center and customer-operation systems where AI, CCaaS, and systems of record must execute actions in the right sequence.
Offensive security, attack-simulation, exposure analysis, and AI-assisted security workflows where weak validation creates real risk.
Products built quickly with AI tools that need architecture review, delivery ownership, release hardening, and a path to production.
Case Studies
Details are anonymized in line with client NDAs. The systems, timelines, and outcomes reflect real engagements where quality, reliability, and delivery had to move together.
Proof
64% faster review
Timing
Production monitored for 6 months
A legal technology company needed to automate contract risk extraction at scale. The agent had to stay grounded in source material and escalate uncertainty instead of guessing.
Zero hallucinated clauses recorded in the monitored production workflow.
Read more →Proof
100% standard intake automated
Timing
6-8 hours recovered weekly
A digital health platform was manually verifying insurance claims across multiple payer formats. The process was slow, error-prone, and difficult to scale.
Full intake workflow automated with compliance controls and auditability in place.
Read more →Proof
0 critical issues at launch
Timing
Delivered in 3 weeks
A product team needed a custom internal workflow tool, not a configuration of an existing SaaS. They needed a purpose-built system with its own interface, logic, and integrations. We owned architecture, build, UI, and validation in the same delivery loop.
Zero rework requests in the first 60 days of production use.
Read more →Proof
11 issues found in 72 hours
Timing
Critical fixes completed in 4 days
A fast-moving AI team had used coding agents across the product surface. The system looked ready, but needed a hard security and release-readiness pass before launch.
The product launched on schedule with zero reported incidents after remediation.
Read more →Every AI coding tool optimizes for output speed. None of them proves the software works against your product, users, data, and business rules.
Automated tests only protect the cases someone remembered to encode. The expensive failures live in the behavior nobody thought to check.
AI systems rarely fail by crashing politely. They return the wrong answer, update the wrong record, cite the wrong source, or leak the right data to the wrong place.
Shellexa gives fast-moving teams the quality layer they do not have time to build internally: QA, agent evals, security review, release hardening, and production validation.
The Team
We are a QA as a Service and AI systems validation firm. Small by design, rigorous by practice. We work with funded startups, scaling engineering teams, and enterprise clients across the US, UK, and EU — and we bring the same structured process to every engagement.
Background in browser automation, API security, and enterprise QA infrastructure across clients in the US, UK, and EU. Has spent years learning how production systems fail and how to build processes that stop those failures earlier.
Manages every client engagement from scoping through delivery. Focused on regulated industry workflows and making sure every report we send is something a client can act on immediately.
Based in Noida, India. Incorporated as Shellexa InfoTech Private Limited.
If your team needs QA coverage, AI-agent validation, or a production system built the right way, let's scope the work and decide whether the fastest path is testing, hardening, or owning the build.
We respond within 24 hours. Direct conversation with the people who'll test, harden, and validate the system.