Custom model development
Classification, regression, ranking and anomaly detection models designed around your KPIs.
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ML, Deep Learning & NLP
When off-the-shelf AI is not accurate enough, a custom model trained on your data usually is. We build machine learning, deep learning and NLP models for prediction, classification and language tasks, and run them reliably in production.
What this covers
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
Who it is for
We start from the business metric, not the algorithm. Simple, explainable models are used where they perform well and deep learning where the problem demands it. Every model ships with data pipelines, drift monitoring and a retraining plan.
Discuss your requirementCompanies with years of transactional or sensor data to learn from
Product teams needing classification, scoring or ranking models
Businesses processing large volumes of text in English and Indian languages
Teams whose AI prototype never made it to production
Capabilities
Classification, regression, ranking and anomaly detection models designed around your KPIs.
Neural networks for images, audio, time series and complex patterns where classical models fall short.
Text classification, entity extraction, sentiment and intent detection, including Hindi and Hinglish text.
Reliable pipelines that turn raw operational data into model-ready features.
Models served through APIs with versioning, CI/CD, A/B testing and rollback.
Drift detection, performance dashboards and scheduled retraining so accuracy holds over time.
How we work
You always know what happens next, who is responsible and what you will receive at each stage.
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
The decision to improve and the metric to optimise are agreed.
Data is profiled, cleaned and labelled where needed.
Baselines and candidate models are trained and compared.
Results are tested on unseen data and explained to stakeholders.
The model goes live with monitoring and a retraining schedule.
Deliverables
Technology & standards
We recommend tools based on your scale, budget and existing systems, not on what is fashionable. Every choice is explained in the proposal.
Engagement models
A 3–4 week pilot on your own data with agreed accuracy targets, so you see real results before scaling.
Hardened integration, guardrails, monitoring and admin controls, delivered in milestones with a fixed quote.
Ongoing prompt and model tuning, evaluation runs, cost monitoring and feature additions on a monthly plan.
How pricing works: AI projects are priced in two stages: a fixed-price proof of concept, then a production quote based on what the pilot proves. Model and API usage costs are estimated upfront and billed at actuals.
Get a quoteFAQs
It depends on the problem. Many tabular prediction tasks work well with a few thousand good examples, while image and language tasks can start from pre-trained models. We assess your data in the first week and tell you honestly what is achievable.
LLMs are great for open-ended language tasks. For structured predictions such as churn, demand or risk scoring, a custom model is usually more accurate, cheaper and easier to explain. Many solutions combine both.
We monitor input data and prediction quality in production, alert on drift and retrain on fresh data on a schedule or whenever performance drops.
Yes. The trained model, code, pipelines and documentation are transferred to you, and models can run in your own cloud account.
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