AI Solutions
AI & Machine Learning Development
Models trained on your data, deployed with monitoring that catches drift.
Classical and deep learning work — forecasting, classification, recommendation, and computer vision — delivered with the MLOps around it, because a model without monitoring is a liability with a good demo.
What the engagement includes
- Problem framing — Translating a business question into a measurable prediction target.
- Feature and data pipelines — Reproducible feature engineering with training/serving parity.
- Model development — Baselines first, complexity only when it beats the baseline measurably.
- Deployment — Batch or real-time serving with versioned artefacts and rollback.
- Drift monitoring — Input and prediction drift alerting before accuracy quietly degrades.
- Retraining pipelines — Scheduled retraining with automated validation gates.
Capabilities
How we approach this work
Every ai & ml development engagement runs on the same delivery model — the specifics below change, the discipline does not.
Problem framing
Translating a business question into a measurable prediction target.
Feature and data pipelines
Reproducible feature engineering with training/serving parity.
Model development
Baselines first, complexity only when it beats the baseline measurably.
Deployment
Batch or real-time serving with versioned artefacts and rollback.
Drift monitoring
Input and prediction drift alerting before accuracy quietly degrades.
Retraining pipelines
Scheduled retraining with automated validation gates.
Process
How a project runs
- 01
Requirements
Workshops with your stakeholders to establish scope, constraints, and success criteria.
- 02
Research
User, competitor, and technical research to challenge assumptions before they become code.
- 03
Architecture
System design, integration mapping, and a written record of the trade-offs we chose.
- 04
Design
Flows, screens, and every non-happy state, delivered as a working component system.
- 05
Build
Two-week increments, each ending in something demo-able on a real environment.
- 06
Test
Automated regression plus exploratory passes under poor-network and low-end device conditions.
- 07
Launch
Staged rollout, store submission, monitoring, and a rollback plan agreed in advance.
- 08
Measure
Instrumentation reviewed against the success criteria set in step one.
- 09
Support
Maintenance retainer covering OS upgrades, dependency patching, and SLA-backed fixes.
FAQ
AI & ML Development questions
How do we start, and what does it cost?
With a Product Requirement Analysis for ₹3,500: a call with the people who will build it, your requirements worked through, and a written quotation and timeline. Pay on our Razorpay page, then pick a time at elevatebox.in/book.
How do you price engagements?
Fixed-price for well-defined scopes, and time-and-materials for evolving products. We recommend fixed-price discovery first so the build estimate is based on evidence rather than assumption.
Who owns the code and infrastructure?
You do, throughout. Repository, cloud accounts, and deployment credentials are in your name from the first commit, not transferred at the end.
How long does a typical project take?
A focused MVP is usually eight to sixteen weeks. Platform work and multi-app products run longer. We give a range with a confidence level, and narrow it as unknowns resolve.
Can you work with our existing team?
Yes. We embed in your repositories, stand-ups, and review process, or run a parallel workstream with defined interfaces — whichever fits how you already work.
What happens after launch?
A maintenance retainer covering OS and dependency upgrades, monitoring, and SLA-backed fixes. You can also take it entirely in-house — the documentation and handover support that with are included.
Do you sign NDAs and custom contracts?
Yes. We work under client MSAs and NDAs routinely, including UK, EU, UAE, and US terms, and we can complete security questionnaires as part of onboarding.
Which time zones do you cover?
We commit to a daily overlap window agreed before the contract starts. UAE and Europe get near-full overlap; US clients get a committed morning or evening window.
Will you tell us if we should not build something?
Yes, and we do. If a simpler tool or no build at all serves you better, we say so during discovery rather than after invoicing the build.
Start work with us
Tell us what you are building. We will come back with scope, an honest range, and the parts we think you should cut.