Custom AI and machine-learning solutions, built to production
By Vishal · published 25 October 2026
Custom AI/ML means a model or system built for a problem that off-the-shelf tools do not solve: scoring, matching, classification, routing, recommendation, anomaly detection. VAI Five starts with a two-week feasibility study, builds a fixed-price pilot on your real data, and takes the winner to production with monitoring and a handover your engineers can maintain.
Problems we build for
| Problem | Example | Typical method |
|---|---|---|
| Scoring and ranking | Lead scoring, credit or risk scoring, candidate ranking | Gradient-boosted trees, calibrated probabilities |
| Classification and routing | Ticket triage, document type detection, content moderation | Fine-tuned small language models or classical NLP |
| Matching and deduplication | Product catalogue matching across marketplaces, customer record merging | Embeddings plus rules |
| Recommendation | Next product, next content, next action for a sales rep | Collaborative filtering, two-tower models |
| Anomaly detection | Fraud, machine faults, unusual spend | Isolation forests, autoencoders, statistical control |
| Extraction | Fields from invoices, KYC documents, prescriptions | Vision-language models with validation rules |
Buy, build or wait
The feasibility study answers this first, and "buy" or "wait" are real answers we give. Build when the problem is specific to your data, when the decision is frequent enough to pay for a model, and when a baseline (a rule, a spreadsheet, a person) exists to beat. Buy when a SaaS product already does 80% and your data is not the differentiator. Wait when the data is not being collected yet; we will tell you what to start logging.
How we build
- Baseline first. The first model is the simplest thing that could work, measured against what you do today. Everything after must beat it.
- Boring, well-supported tooling. Python, scikit-learn, PyTorch, open-weight language models where they fit, Postgres, your cloud's managed services. No framework your team cannot hire for.
- Evaluation you can read. Metrics tied to the business decision — rupees saved, hours saved, errors avoided — not only accuracy.
- Monitoring from day one. Data drift, prediction drift and a weekly report, so the model does not quietly decay.
- Small models before large ones. Many problems are solved by a model that runs on a ₹2,000-a-month instance; we reach for large language models when the task needs language, not as a default.
How a project runs
- Feasibility note (free) — a call and a look at your data profile; one page with the problem restated as a prediction, the baseline, the data gaps, and the pilot price.
- Two-week pilot (fixed price) — a model on a slice of real data, the evaluation against the baseline, and a go/no-go.
- Build (4–10 weeks) — production pipeline, integration with your systems, monitoring, documentation.
- Handover and care — a session with your engineers, a runbook, and optional monthly care for retraining and drift.
What you own at the end
The repository, the trained weights, the evaluation set, the documentation and the monitoring dashboard. There is no proprietary platform in the middle, so you can maintain it yourselves, hire someone else, or come back to us. We think that is the only honest way to sell machine learning to a company without a data-science team.
Questions people ask
How much does custom AI/ML development cost in India?
Pilots are fixed-price and quoted after the free feasibility call. Production builds depend on the number of integrations and the infrastructure, and are scoped in stages so you can stop after any stage with something usable.
Do we need a data scientist on our team?
No. We design the handover for software engineers without ML experience: documented pipelines, a runbook and monitoring that tells them when to call for help.
Which cloud do you work on?
AWS, Google Cloud and Azure, in Indian regions where data residency matters, or on your own servers. We use your account so you keep the billing and the access.
Can you fix a model another vendor built?
Yes. A review starts with the evaluation set — if there is none, that is usually the problem — and a written assessment before any code changes.
Tell us the problem, not the model.
A 30-minute call and a one-page feasibility note, free. If a two-week pilot makes sense, we scope it with a fixed price. If it does not, we say so.
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