AI for your business
For businesses that want AI to solve a real problem and need to know what is feasible and how to deploy it safely.
Capability
Check a system is ready before you release it
We apply testing and validation practices to improve the reliability of applications, data pipelines, cloud infrastructure and AI-enabled systems.
In short
Quality engineering checks that a system works, performs and recovers as intended before it reaches users. Nexstella tests applications, data pipelines, infrastructure and AI outputs against acceptance criteria agreed before release, with testing proportionate to the system's risk and business criticality.
Last updated
01 When it matters
When should quality engineering come in?
A system that works in a demonstration may not be reliable, secure or maintainable in production. Quality gates and acceptance criteria should be established before release.
Testing should be proportionate to the system's risk and business criticality, and it covers more than applications: data pipelines, cloud infrastructure and AI outputs need validation too.
A system combines an application, data pipelines and an AI-enabled feature, and the business needs acceptance evidence that it is reliable before it is released.
We would agree quality gates and acceptance criteria before release, then run integration, performance and recovery tests, data reconciliation and AI evaluation, proportionate to the system's risk and business criticality.
02 Services
What does this include?
- Functional and integration testing
- API and contract testing
- Automated regression testing
- Data reconciliation and schema validation
- Pipeline reliability testing
- Performance, load and stress testing
- Failure-recovery and resilience testing
- Security testing and vulnerability assessment
- AI output and groundedness evaluation
- Retrieval relevance and citation validation
- Adversarial testing for AI applications
- Production readiness reviews
03 Applications
Where does this usually apply?
- Reconciling data and validating pipelines as part of making business data usable
- Running integration and performance testing, recovery testing and user acceptance before a production release
- Testing and releasing in a controlled way when modernizing existing technology
- Evaluating an AI capability against defined acceptance criteria before it goes to production
04 Technology
How do we choose the technology?
Testing approaches and tools are chosen to fit the system, its compatibility with your existing environment, your team's operational capacity and maintainability over time.
05 Deliverables
What do you get?
- Quality gates and acceptance criteria agreed before release
- Test results across functional, integration and performance testing
- Remediation records for defects found
- Acceptance evidence
- An operational readiness assessment
06 Needs
Which needs does this serve?
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Growing technology
For growing businesses whose infrastructure, data and releases need to keep pace with more users and complexity.
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Modernizing systems
For businesses with systems that are costly to maintain, hard to scale or poorly integrated with each other.
07 Questions
What do people usually ask?
A check of functional correctness, reliability, security and operational readiness before a system goes live. It can draw on integration and performance testing, recovery testing and the acceptance criteria agreed up front.
Yes. Data reconciliation, schema validation and pipeline reliability testing are part of our quality engineering, alongside functional, API, regression, performance and failure-recovery testing.
It depends on the system. Quality gates and acceptance criteria are established before release, with testing proportionate to the risk and business criticality of what is being released.
Solutions
Which solutions do we build for this?
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MVP to production
We help translate a product concept into a technically sound, production-ready first version, on a foundation that can evolve as your business develops.
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Web apps and portals
We build customer-facing and employee-facing business applications, with sign-in, permissions and connections to your other systems designed in from the start.
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Agentic workflow automation
We build workflows where AI coordinates steps, uses only approved tools, checks intermediate results and escalates the decisions that need human judgment.
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Legacy modernization
We assess the system you have, then refactor or replace it in stages, with testing and controlled releases, so operations can continue while it improves.
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Systems integration
We integrate CRM, ERP, payment, analytics and operational systems so that data flows between them reliably, with validation and monitoring.
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DevOps automation
We automate how code and infrastructure are built, tested, scanned and deployed, with approvals and rollback, so releases rely less on manual steps.
If this sounds like you: Ship releases without the regressions customers find first