AI Product 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
Specific work, 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
Document ingestion, chunking chosen for your document shape rather than a default, embedding selection, hybrid search and reranking.
Computer vision
Detection, classification and tracking over image and video streams, with alerting into the workflow that needs it.
Evaluations and guardrails
Golden sets, graded rubrics, regression checks on every model and prompt change, and refusal behaviour you can point at.
AI retrofits
Adding AI to a product that already has users, without a rewrite and without breaking what works.
Cost and latency engineering
Caching, prompt compression, model routing and hard ceilings — the work that decides whether the unit economics survive success.
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
Work we have shipped.
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.
Computer Vision for Classroom Engagement
Real-time video classification detecting reading, writing and hand-raising, with live alerts to teachers.
In their words
“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. Always a pleasure working with such a professional.”Director, Data Science — Pack Moose LLC
Questions
What clients ask us first.
Can you add AI to a product we already have in production?
Yes, and it is the majority of our AI work. We treat the existing product as a constraint rather than a starting point. The AI feature ships behind a flag with a fallback path that keeps the original behaviour available, so a model outage degrades the feature instead of taking down the product.
Which models do you work with?
We work across the major hosted families and the open-weight models, and we choose per task rather than committing to one vendor across a whole system. A classification step and a long-form generation step often belong on different models with very different costs. We also build the abstraction that lets you switch later, because you will want to.
How do you know the AI is working?
We build an evaluation harness before we build the feature. It holds a golden set of inputs with known good outputs, graded automatically or by rubric, and it runs on every prompt change, model upgrade and retrieval change. That is what turns a model upgrade from a gamble into a decision.
How much does an LLM application cost to run?
It depends on token volume, model choice and how much you cache, and we cannot give you a figure before the assessment. What we can give you is a cost and latency envelope at the end of it, before you commit to a build. If the envelope does not work, that is a useful outcome.
Do you build with RAG or fine-tune the model?
Retrieval first, in almost every case. RAG lets you update knowledge by updating documents rather than retraining, and it lets you cite sources. Fine-tuning earns its place when you need a consistent output format or a specialised tone at volume. The two are not alternatives; systems often use both.
How long before we see something working?
The assessment takes two to four weeks. A first working slice usually follows within four to six weeks of the build starting — in your environment, with your data, not as a demo. We would rather show you something narrow and real than something broad and staged.
Get in touch
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.
Prefer email? sales@irasoftwares.com