We build the data foundations, models and guardrails that take AI from demo to dependable production.
We build the data foundations, models and guardrails that take AI from demo to dependable production.
A weekend prototype is easy. What breaks companies is everything after it. The data turns out messier than the pilot assumed. Nobody agreed what accurate enough means, so there is no evaluation harness and every change becomes a guess. Inference costs arrive unbudgeted. Security asks who can see what, and there is no answer. Qalvix exists for that gap. We are engineers first, and we are deliberately opinionated about what production actually requires.
RAG systems, copilots and agentic workflows, built with the evaluation harness and guardrails that let you change them safely.
Pipelines, lakehouses, vector stores and MLOps, so your models run on a foundation that holds up under real volume and real cost.
Document processing, back-office and support workflows automated with AI that knows when to hand off to a human, and logs every decision.
Golden datasets, regression suites, prompt injection defence and audit logging, so you can prove how the system behaves before it faces a customer.
Deployment, monitoring, retraining and inference cost control on AWS, Azure or GCP, so models keep performing long after launch day.
A two-week fixed-price engagement: a prioritised opportunity map, an honest data readiness assessment, and a costed 90-day build plan you keep either way.
Before anyone writes production code we run a fixed-price, two-week audit. You get a prioritised map of where AI will pay for itself, an honest assessment of whether your data can support it, and a costed 90-day build plan. If you are not ready, we will say so, and the plan is still yours.
Every engagement follows the same five steps. Discover: we define the use case narrowly and agree what good enough means as a number, before anything is built. Prove: we build the smallest honest version and measure it against that number. Harden: evaluation harness, guardrails, monitoring, cost instrumentation and security review. Scale: widen scope, integrate properly, train your team. Hand over: documentation, runbooks and knowledge transfer, because we would rather you did not need us forever. We work in three commercial shapes only, and we publish all three: a fixed-price AI Readiness Audit, a fixed-scope Build engagement, or a dedicated AI pod. No open-ended time and materials, and no surprise scope.
Please reach us at hamza@qalvixsystems.com if you cannot find an answer to your question.
We work across sectors, including finance, healthcare, retail, logistics, manufacturing and education. What matters more than the industry is whether the data exists and the decision is worth improving.
With a discovery session where we look at your data, systems and goals. From there we scope a small pilot with success metrics agreed in writing, so you can judge the value before committing to a larger programme.
You do. Code, models, pipelines and documentation are handed over to your team, and we are happy to work inside your repositories and cloud accounts from day one.
That is the normal starting point. Assessing, cleaning and modelling the data foundation is part of the work, and in many projects it is where most of the value comes from.
Yes. We work remotely with teams in other time zones and arrange overlapping hours for stand-ups, reviews and handovers.
The AI Readiness Audit is two weeks. A first fixed-scope build is typically eight to twelve weeks from kickoff to something running in production. Anything much faster than that usually means the evaluation, security and monitoring work was skipped, and that is the work that keeps a system alive after launch.
We work in three commercial shapes rather than hourly rates: a fixed-price AI Readiness Audit, a fixed-price build against an agreed outcome, and a monthly dedicated AI pod. You get a firm number before any work starts. We do not sell open-ended time and materials, and we do not place individual contractors.
We treat it as a design input, not a compliance afterthought: least-privilege access, explicit rules for handling personal data, audit logging on every model call, and open-weight models hosted inside your own region when data is not allowed to leave it. If your requirement is that nothing reaches a third-party API, tell us early and we will architect for that from day one.
Yes. We build inside your repositories, your cloud accounts and your CI pipeline, on AWS, Azure or GCP. We would far rather extend what you already run than start a platform migration you did not ask for.
Yes. Every engagement ends with documentation, runbooks and knowledge transfer so your own team can run the system without us. Where you want us to stay on for monitoring, evaluation and iteration, that is a dedicated AI pod arrangement rather than an open-ended retainer.
Tell us the problem you are trying to solve. We will come back within one business day with an honest view of whether we can help and what it would take.
Open today | 09:00 am – 05:00 pm |
Practical writing on taking AI systems from prototype to production. Roughly monthly, no sales blasts, unsubscribe any time.
We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.