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Digital Transformation · AI Hackathon · AI Data Science

AI Data Science.

Predictive ML, MLOps, model deployment, governance, and monitoring — the full data science delivery stack from problem framing to production operation. We don't just build models; we deploy and operate them.

End-to-End
ML Delivery
MLOps
Production
Governance
Built In
Monitoring
24/7

What We Deliver

  • Predictive ML models for business outcomes
  • MLOps pipelines for model deployment
  • Model monitoring and drift detection
  • Governance, explainability, audit logging
  • Reskilling for your internal data science teams
What It Is

A focused capability — delivered end-to-end

Most ML models never make it to production. We focus on the full lifecycle — from problem framing through model deployment, monitoring, and governance — so AI investments deliver actual business value, not just slide decks.

Problem Framing

Business problem to ML problem translation with clear success metrics

Model Development

Classical ML, deep learning, foundation model fine-tuning

MLOps Pipelines

CI/CD for models, feature stores, deployment automation

Model Monitoring

Drift detection, performance tracking, automated retraining triggers

Governance

Explainability, audit logs, bias monitoring, regulatory compliance

Team Enablement

Training your team to maintain and extend the models we build

Engagement Model

How we deliver

01

Discovery

Assess your current state, identify gaps, scope the engagement against your goals and constraints.

02

Design & Build

Architecture, design, build, and integration — backed by CMMI 3 process maturity and CI/CD delivery practices.

03

Run & Optimize

Managed operations, continuous improvement, capability uplift, governance — partnership for the long haul.

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Our Thinking

Perspectives on AI & Data Science

From data engineering to model deployment and LLMOps — what it takes to build an enterprise AI capability that delivers models in production, not just in notebooks.

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Point of View

MLOps Maturity: From Notebook to Production Pipeline

Most enterprise ML initiatives stall at the notebook stage. We define four maturity levels and the specific infrastructure, process, and culture changes required to move through each one.

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Whitepaper

The Data Quality Imperative: Why AI Fails Without Clean Data

No model architecture compensates for poor-quality training data. Our data quality framework — covering completeness, consistency, timeliness, and lineage — is the foundation of every engagement.

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Insight

Federated Learning: Enterprise AI Without Centralising Sensitive Data

When data privacy regulation or competitive sensitivity prevents pooling datasets, federated learning enables model training across distributed data without moving it. The architecture and trade-offs explained.

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Insight

LLMOps: Operationalising Large Language Models at Enterprise Scale

LLMs introduce new operational challenges — prompt versioning, output evaluation, cost management, and safety guardrails. We share the LLMOps playbook emerging from our enterprise AI engagements.

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Questions we get

Before you ask

What is the difference between a data science project and an MLOps capability?
A project delivers a model and a deck. An MLOps capability delivers a pipeline: versioned data and features, reproducible training, automated deployment, monitoring and a retraining path. Most enterprise models stall because nobody owns them after go-live, so we scope the pipeline, the ownership and the retraining trigger in the same engagement as the model itself.
How much data do we need before machine learning is worth trying?
For tabular classification, expect to need several thousand labelled rows, including at least a few hundred examples of the rare outcome you care about. For demand or revenue forecasting, two to three years of history captures seasonality where one year cannot. Signal quality beats volume: consistent definitions, reliable timestamps and a trustworthy label matter more than size.
How long does a first predictive model take to reach production?
Typically three to five months end to end: two to four weeks framing the business problem and success metric, four to six weeks on data preparation and feature engineering, two to four weeks of modelling and backtesting, then four to eight weeks for deployment, monitoring and handover. Data preparation is consistently the longest phase, not modelling.
What data access and involvement do you need from our team?
Read access to the relevant source systems or a governed extract, a data dictionary, and someone who can explain how the fields are really populated in practice. On the business side, a domain expert to define the target variable and rule on edge cases, plus agreement on how success is measured before modelling starts. We then train your team to maintain what we build.
How do you know the model is still working after go-live?
Through monitoring on three layers: input drift in feature distributions, prediction drift in the output mix, and actual accuracy once ground-truth labels arrive. Alerts fire on threshold breaches. A challenger model trains in parallel and is promoted only when it beats the champion on a held-out period. Explainability output and audit logs support governance and bias review.
When is machine learning the wrong tool for the problem?
When the rule is already known and stable, write the rule instead. Machine learning also fails when no reliable label exists, when the outcome depends mostly on factors absent from your data, or when a regulator needs a fully explainable decision and a simple model performs nearly as well. Optimisation and deterministic logic often win on cost and defensibility.

Let's build what's next — together.

Whether it's setting up your India GCC, modernizing your enterprise stack, or hiring 50 engineers in 30 days — we'd love to scope it with you.

Schedule a Call → Email Business Team