Practical AI, engineered and tested like any other software
AI can remove hours of manual work, but only if it behaves reliably. We help you pick the right use cases, build them into your systems, and put evaluation and guardrails around them before they reach users.
Impressive demos, unpredictable production
AI prototypes are easy to build and hard to trust. Without structured evaluation, it is difficult to know whether a model will give the right answer tomorrow, or what happens when it does not.
- Unclear use casesTeams start with a model instead of a measurable business problem.
- No way to measure qualityOutputs are judged by impression rather than defined criteria and test sets.
- Integration gapsAI features sit beside core systems instead of working with them.
- Data and privacy questionsSensitive information flows into prompts without clear rules.
Quality engineering applied to AI
We treat AI features as software that needs requirements, tests and monitoring. That means defining what a good output looks like, building evaluation sets, testing edge cases and failure modes, and deciding where a person should stay in the loop.
We work with leading model providers, including Anthropic’s Claude and OpenAI models, and choose based on your requirements, data policies and budget.
Capabilities
AI-enabled applications
Search, summarization, drafting and classification features built into your product.
AI workflow automation
Multi-step workflows that combine AI with your existing systems and approvals.
Business process automation
Reduce repetitive handling of documents, requests and data entry.
AI-assisted testing
Use AI to speed up test design, data generation and analysis, with human review.
LLM application validation
Evaluation of accuracy, consistency, safety and failure handling in LLM features.
AI integration
Connect model APIs securely to your applications, data and identity controls.
How we work
Identify
Find use cases with clear value, available data and acceptable risk.
Prototype
Build a narrow working version and define how success is measured.
Evaluate
Test against representative cases, edge cases and misuse scenarios.
Integrate
Connect to your systems with logging, access control and fallbacks.
Monitor
Track quality in production and re-evaluate as models and data change.
What it means for your business
Technologies we use
- Claude
- OpenAI
- Python
- TypeScript
- Node.js
- REST APIs
- PostgreSQL
- Azure DevOps
- GitHub
Measurable outcomes
AI work is tied to time saved, quality improved or throughput increased.
Known behaviour
Evaluation results show how the feature performs before launch, not after.
Safer adoption
Guardrails, data rules and human review are designed in from the start.
Maintainable systems
AI features are versioned, tested and monitored like the rest of your software.
Frequently asked questions
Which AI models do you use?
We are model-agnostic and work with providers such as Anthropic and OpenAI. We recommend a model based on task quality, data handling terms, latency and cost.
How do you test an LLM feature?
We build evaluation sets from real or representative inputs, define scoring criteria, test edge and adversarial cases, and re-run evaluations whenever prompts, models or data change.
Will our data be used to train models?
That depends on the provider and plan. We review provider data terms with you and design the integration to follow your data policies.
Can you validate an AI feature someone else built?
Yes. LLM application validation is a standalone service, and a common starting point for teams that already have a prototype.
From our Insights
How to Evaluate an LLM Feature Before It Reaches Customers
LLMs don’t behave like traditional software, so they can’t be tested like it. A practical evaluation approach before launch.
Related services
Talk to Qanovix about AI & intelligent solutions
Tell us where you are today and what you need to achieve. We will recommend a practical next step, even if it is small.