Applied-AI engineering studio
IQMaxs designs and engineers AI systems that hold up under real workloads: agentic platforms, retrieval systems, machine learning and data science — reliable, observable and owned by your team.
What we do
Engineering intelligent systems across the modern AI stack — from autonomous agents and enterprise knowledge systems to machine learning and production deployment.
Recent work
A selection of delivered projects. Client work is shown as case studies without source code.
How we work
Four stages, each with a concrete deliverable. No stage depends on trusting a black box.
We pin down the decision or workflow to improve, the data available and what production actually means in your environment.
A working slice against real data with honest evaluation, so we learn what holds up before committing to scale.
Guardrails, monitoring, tests and MLOps. The system becomes reliable, observable and safe to run unattended.
Documented and owned by your team. No black boxes and no lock-in — you can run and extend it yourselves.
Why IQMaxs
Every build is held to the same standards — the things that decide whether an AI system survives its first month in production.
Modular services with clear boundaries, so a system can be extended, audited and handed to another team without a rewrite.
The target is a running system, not a notebook. Retries, fallbacks and failure paths are part of the first version.
Least-privilege access, read-only paths where writes are not required and secrets redacted before they reach a log.
Workloads that hold up as volume grows — queued, batched and horizontally scalable rather than tuned for a demo.
Structured logs, traces and progress reporting, so you can see what the system did and why it decided to do it.
Schema validation, deterministic scoring where it matters and human approval gates on anything consequential.
Versioned models and data, reproducible pipelines and a retraining path that does not depend on one person.
The studio
IQMaxs is a focused applied-AI studio for teams that want systems that reach production — not a proof of concept that stalls.
You work directly with the engineer building your system. That means fewer handoffs, honest scoping and architecture decisions explained in plain language. Everything is delivered documented and readable, so your team can run and extend it without us.
Tech stack
Selected per project rather than by default — these are the platforms and frameworks the studio works in most.
Applied AI systems that run in production: agentic and multi-agent platforms, retrieval-augmented assistants, machine learning and deep learning models, plus the data pipelines around them. The work spans scoping, engineering, evaluation and handover.
With a conversation about the decision or workflow you want to improve, the data you already have and what 'production' means in your environment. That produces a scoped plan before any code is written.
You do. Systems are delivered documented and readable, so your team can run, extend and audit them. There are no black boxes and no dependency on the studio to keep something running.
By treating reliability as an engineering problem rather than a prompting one: structured schema validation, deterministic logic where determinism matters, retries and fallbacks, evaluation against real data and monitoring once the system is live.
Yes. Most engagements involve integrating with databases, internal APIs, cloud platforms and collaboration tools already in place. The aim is to fit the system into your environment rather than ask you to move to a new one.
Yes. Several projects in the portfolio are shown as case studies without source code or client identification. Work is covered by whatever confidentiality terms your organisation requires.
Start a project
Tell us what you're trying to ship. You'll get an honest read on the practical path to production — including whether it's worth building at all.
Free 30-minute consultation over Google Meet — pick a time that works for you.
