01Week 1–2
Feasibility and data audit
We look at the data you actually have, not the data you wish you had, and tell you plainly whether it can support the outcome you want.
Machine LearningAI where it saves time
AI where it saves time
and nowhere else
Forecasting, image recognition, document extraction and recommendations — scoped against a number that matters.
Typical timeline6–12 weeks, scoped case by case
Starting fromScoped per project
QuoteFixed, before we build
After launch30 days free fixes
Overview
What this actually is
Plenty of ML projects produce an impressive model and no business change. We start from the opposite end: which decision is being made badly today, how often, and what would it be worth to make it better?
If the honest answer is that a rule or a well-built report would fix it, we will tell you that and save you the budget. We only build a model where a model genuinely wins.
When we do build one, it goes into production with monitoring, retraining and a fallback — because a model that silently drifts is worse than no model at all.
Scope
What's included
Everything below is inside the fixed quote. Anything outside it we tell you about before starting, not after.
- Demand and inventory forecasting
- Image recognition and defect detection
- Document extraction from invoices and forms (OCR)
- Recommendation engines for e-commerce
- Churn and lead-scoring models
- Natural language classification and chat assistants
- Model monitoring with drift alerts
- Scheduled retraining pipelines
Process
How a machine learning project runs
The same four stages every time, so you always know what happens next.
02Week 2
Baseline and success metric
We agree the number the model has to beat and by how much to be worth running. Everything after this is measured against it.
03Week 3–8
Model development
Iterative training and evaluation against a held-out set, with results shared in business terms rather than F1 scores alone.
04Week 8–10
Deploy and monitor
The model ships behind an API with drift monitoring, alerting and a documented fallback path.
Handover
What you end up owning
Every engagement ends with the same handover, whether or not you continue with us afterwards. No hostage code, no accounts in our name.
- Model deployed behind an API your systems can call
- A written baseline: what the model beats, and by how much
- Monitoring dashboard with drift and accuracy alerts
- Retraining pipeline that runs on a schedule
- Documented fallback for when the model is unavailable
- Handover notes covering the data assumptions
Stack
What we build it with
Chosen for how long you will have to live with it, not for what is fashionable this year.
Modelling
Pythonscikit-learnPyTorchXGBoost
NLP & Vision
Hugging FacespaCyOpenCVTesseract
LLM
Claude APIRAG pipelinespgvectorLangChain
Serving
FastAPIDockerAWS SageMakerONNX
Ops
MLflowAirflowPrometheusGrafana
Questions
Asked most often
If yours is not here, call +91 91453 10264 and ask — we would rather answer it before you commit.
It depends on the problem. Forecasting usually needs two years of clean history; document extraction can work from a few hundred labelled examples. The data audit answers this in week one, before you commit to a build.
The feasibility phase is deliberately short and separately priced so you can stop there. We would rather lose the build than sell you a model that cannot hit the number.
Where a large language model is the right tool — document understanding, summarisation, chat assistants — yes, usually with retrieval over your own data. For forecasting and classification, a smaller purpose-trained model is normally cheaper and more accurate.
You do — the weights, the training code and the pipeline. If you later take it in-house, everything transfers.
Related
Often paired with
Thinking about machine learning?
Send a brief or call directly. You get a clear plan and a fixed price, usually the same day.