AI Engineering
AI that survives contact with your users.
We design, build and run AI systems that work inside real products — retrieval that returns the right document, models that hold up under load, and evaluations that catch a regression before a customer does.
The problem
A demo is not a system.
Getting a model to answer one question well takes an afternoon. Getting it to answer thousands of questions, from real users, on your data, at a cost you can defend — that is a different discipline, and it is mostly not about the model.
It is retrieval quality. It is what happens when the answer is wrong. It is throughput against latency, and knowing which one your users actually feel. It is having an evaluation set before you have a launch date.
What we do
Six things, and the judgement to know which you need.
LLM application design
Assistants, copilots and guided workflows that sit inside a real process rather than beside it.
Retrieval-augmented generation
Answers drawn from your documents, knowledge bases and product content — not from a model’s training data.
Open-source model deployment
Setting up and serving large open-source models on your own infrastructure, including concurrency and latency engineering.
Computer vision
Real-time classification over image and video streams, with alerting into the workflow that needs it.
AI retrofits
Adding AI to a product that already has users, without a rewrite and without breaking what works.
Evaluations and guardrails
Cost and latency budgets, regression tests, and the boundaries that decide whether a system survives contact with users.
How we work
Assess, build, operate.
Assess
We start with your workflow, your data and your constraints — not with a model choice. Two to four weeks. You leave with an architecture, a scope, a cost and latency envelope, and an honest answer on whether the thing is worth building at all.
Build
A small senior team works inside your process, not alongside it. Environments, CI/CD, evaluations and security are set up in the first week rather than bolted on before launch.
Operate
We stay on after go-live — scaling, new features, model and dependency updates, incident response. Most of our engagements are measured in years.
Proof
AI work we have shipped.
Serving a 72B Open-Source Model Under Concurrent Load
Deploying Qwen 2.5 72B in-house with concurrent API request handling, serving architecture and performance stability under load.
Computer Vision for Classroom Engagement
Real-time video classification detecting reading, writing and hand-raising, with live alerts to teachers.
AI Assistant for an Obesity Care Programme
A conversational AI assistant supporting patient interactions in a semaglutide-based obesity care programme.
Retrieval-Augmented Generation for an Education Platform
A retrieval pipeline over an education platform's own content, so answers come from the course material rather than the model's training data.
“Very professional, great turnaround time. I was also given plenty of time for Q/A so that I could handle maintenance independently. I plan to rehire IRA Softwares in the future.” Director, Data Science — Pack Moose LLC
Questions
What clients ask us first.
Do we need to move off our current model provider?
No. Most of our work integrates with whatever you already use. We recommend self-hosting only when data residency, per-token cost at volume or latency makes the case for it — and we will tell you when it does not.
How long does an assessment take?
Two to four weeks. You get an architecture, a scope, a cost and latency envelope, and an honest answer on whether to build.
Can you work with our existing engineering team?
Yes, and it is usually the better outcome. We work inside your process — your repo, your review flow, your standup — rather than delivering over a wall.
What happens after launch?
We stay on. Scaling, new features, model and dependency updates, incident response. Most of our engagements are measured in years.
Do you handle regulated data?
We have shipped HIPAA-bound healthcare systems since well before the current AI cycle. Compliance is a design input for us, not a final review.
What if the honest answer is not to build it?
Then we say so in the assessment. We would rather lose a build than ship something you have to unwind in a year.
Planning an AI product or an internal tool?
Tell us the workflow and the constraint. We will tell you what we would build, what it would cost to run, and whether it is worth doing.