AI PoC development services
AI projects that reach production
AI and ML development, from AI proof of concept to production.
If your AI pilot works in a notebook but not in your business, you’re not alone. The gap between a promising prototype and a system people rely on is where most AI projects stall — and it is the work we specialize in.
- Scoping01
- Data readiness02
- Proof of concept03
- Deployment04
- Monitoring05
- Retraining06
Where AI projects get stuck
Four reasons good pilots never ship
Data
The data that looked clean in the pilot isn’t clean at scale.
Definition of done
Nobody agreed on what “good enough” means, so the project never reaches a decision.
Ownership
The model works, but nobody owns getting it into production.
Drift
Once it is live, accuracy quietly drifts and no one notices until users do.
What we deliver
Every stage, from scoping to retraining
Including the MLOps consulting services that keep a model healthy after launch.
- Stage 1
Scoping
The business decision the system supports, and how success is measured.
- Stage 2
Data readiness
What your production data can actually support, and what it will take to close the gaps.
- Stage 3
Proof of concept
The smallest build that tests the riskiest assumption.
- Stage 4
Deployment
A production pipeline integrated with your systems, with rollback.
- Stage 5
Monitoring
Alerts on drift, quality, latency and cost before users notice.
- Stage 6
Retraining
New model versions that are evaluated before they replace old ones.
Built on our own products
We run what we recommend
We build our own AI products. The practices we bring to your project are the ones we rely on for systems we are accountable for ourselves.
Our AI products →Capabilities · what we have delivered
- Document intelligence and summarizationProven in Aalam Doc AI. See Aalam Doc AI →
Engagement
Ways to work with us
Project delivery
We own the build from scoping to production.
Proof of concept first
AI proof of concept development as a small, fixed-scope engagement to test feasibility before you commit.
FAQ
AI and ML project questions
Something we haven’t covered? Tell us the role, the stack and your AI tool policy, and ask us directly.
Tell us the role you need →How long does a proof of concept take?
Six to eight weeks for most problems, assuming the data exists. Our last one ran from mid-June to live on 7 July. If the data is not ready, that comes first and we will say so before taking the work.
What happens if the proof of concept does not work?
You get the evaluation and the reason, and you do not build on it. A proof of concept that honestly fails in six weeks is cheaper than one that is talked into production and fails in eighteen months. We will tell you which it is.
How do you decide a model is good enough?
Against the thing it replaces, measured the same way. On our last project the existing engine and the new one were tested on identical data with grouped validation, so a repeated case could never appear in both training and test. The client's own data reviewer challenged the result and we measured his alternative live in the application.
Where does the model run?
Wherever you want it. Cloud, your own servers, or fully offline. Our most recent system runs in Docker on the client's own hardware with no external services at all.
Do you keep maintaining it after it goes live?
If you want us to. Models drift as the real world changes. The system we built in July has been retrained three times in three months, because the plant's standards moved and the model had to move with them.
Where is your project stuck?
Tell us which stage your AI initiative has stalled at. We’ll tell you what it would take to move it forward.