Our machine learning development services take you from raw data to production-ready models: built on your data, validated against your accuracy targets, and maintained through the full lifecycle. Senior ML engineers, evals before shipping, and full ownership at handover.
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150+
ML Models Shipped
60+
Engineers In-House
96%
Client Retention
Built for teams at a real inflection point.
Make the right call, before you commit to a direction.
Every ML project starts with the same question: build a custom model, call a pre-built API, or use an AutoML platform. Each path makes sense for a different situation. Here is the honest trade-off that matters most at production scale.
| What matters |
Recommended
Custom build
|
Common alternative
Off-the-shelf SaaS
|
Other path
Low-code platform
|
|---|---|---|---|
| Fit to your data | Trained on your specific data and domain | Generic models, broad coverage | Automated but domain-agnostic |
| Accuracy ceiling | Optimised for your exact task and distribution | Fixed by vendor model quality | Limited by automated feature engineering |
| Data ownership | Your data stays in your environment | Data processed by the vendor | Data processed by the platform vendor |
| Inference cost at scale | Controlled: your infrastructure, your cost model | Per-call pricing grows with volume | Platform pricing tiers apply at scale |
| Integration depth | Built around your systems and data pipelines | Standard APIs, limited customisation | Platform-dependent integration options |
| Compliance posture | Full control over data residency and audit trail | Vendor handles processing; you inherit constraints | The platform controls the data path |
| Long-term maintainability | You own the model, the pipeline, and the evals | Vendor deprecates versions on their schedule | The platform roadmap determines your options |
| Time to first value | 4 to 12 weeks to production | Days for basic integration | Weeks, faster for simple classification |
From first model to production platform, we cover the full ML engineering spectrum. Start with the capability your business needs most and build the full stack from there.
Models designed, trained, and deployed around your data, your targets, and your accuracy requirements.
Domain-specificEnd-to-endProduction-ready
We design, train, and deploy machine learning models built around your specific data, your prediction targets, and your accuracy requirements. From feature engineering through model selection, hyperparameter tuning, and production deployment, every model ships with an eval harness, performance benchmarks, and the documentation your team needs to maintain it.
Forecasts, risk scores, and demand models that turn historical data into forward-looking signals.
ForecastingRisk scoringDemand planning
Structured predictive models that turn your historical data into forward-looking signals: demand forecasts, churn scores, credit risk ratings, maintenance predictions, and lead scoring. Each model is validated on held-out data, calibrated for your decision thresholds, and built to be retrained as your data grows.
Production vision systems for document processing, quality inspection, and object detection.
Object detectionImage classificationOCR
Production computer vision systems for document processing, quality inspection, object detection, and image classification, built on PyTorch or TensorFlow, fine-tuned on your labelled data, and deployed to the cloud, edge, or on-device depending on your latency and connectivity requirements. Accuracy benchmarked per class, per environment.
Custom language pipelines for classification, extraction, and document generation.
Text classificationEntity extractionDocument generation
Custom natural language processing pipelines for text classification, named entity recognition, sentiment analysis, document summarisation, and structured data extraction from unstructured text. Built on transformer-based architectures, fine-tuned on your domain corpus, and evaluated on real production documents before deployment.
The infrastructure layer that keeps models performing: retraining, registry, drift monitoring.
CI/CD for MLModel registryDrift monitoring
The infrastructure layer that keeps your models performing in production: automated retraining pipelines, model versioning and registry, A/B testing frameworks, drift detection, and eval dashboards your team can act on. Built on MLflow, Kubeflow, or Vertex AI depending on your stack, with full observability from training run to live inference.
Statistical and ML anomaly detection for fraud, infrastructure health, and quality control.
Real-time alertsUnsupervised learningTime-series
Statistical and ML-based anomaly detection systems for fraud, infrastructure health, quality control, and operational monitoring, designed for your data volumes, your latency requirements, and your acceptable false positive rate. Covers both supervised and unsupervised approaches, with explainability outputs your operations team can act on.
A good ML model starts with the right problem definition.Tell us your use case. We will tell you what is feasible and what it will take.
ML across every application, by problem type.
Different ML problems need different architectures, different evaluation frameworks, and different production considerations. Here is how we approach each category.
Making confident decisions from structured data.
Customer churn, fraud detection, lead scoring, intent classification, and document routing, trained on your labelled data with calibrated probability outputs and defined decision thresholds.
Sales forecasting, inventory demand, energy consumption, pricing optimisation, and financial projection models, validated on time-series holdouts with confidence intervals your planning team can use.
Personalisation engines, content ranking, product recommendations, and search relevance models, built on collaborative-filtering, content-based, or hybrid approaches depending on your data volume and cold-start requirements.
Turning unstructured visual and document data into structured outputs.
Invoice processing, contract analysis, medical record extraction, and form digitisation, built to handle real-world document variation, low-quality scans, and multi-language inputs, with structured JSON output.
Defect detection, product quality grading, damage assessment, and compliance inspection, trained on your labelled image sets and deployable to factory-floor cameras, mobile devices, or edge hardware.
Object tracking, activity recognition, crowd analytics, and real-time detection pipelines, optimised for your frame rate, latency, and compute constraints, with alert logic your operations team configures directly.
Extracting signal from the text your business already generates.
Support ticket triage, email categorisation, regulatory document classification, and content moderation, fine-tuned transformer models evaluated on your specific document types and label taxonomy.
Structured data extraction from contracts, clinical notes, financial filings, and operational reports, built for your entity types, your domain vocabulary, and your throughput requirements.
Automated report generation, meeting summary extraction, document condensation, and structured output from unstructured source material, evaluated for factual accuracy and format consistency on real production documents.
ML for every vertical.
How we build, every model the right way.
Rigorous methodology, production-first thinking, and models that earn their place in your stack. Here is what that looks like in practice.
Every engagement starts with your data: its volume, quality, label coverage, feature completeness, and the class imbalances or distribution shifts that will affect performance. We scope the model only after we understand the data, so the accuracy targets we set are grounded in what your data can actually support.
We run structured experiments across multiple model families, baselines first, then progressively complex approaches, so the final model earns its complexity. Every training run is tracked in MLflow with reproducible configs, so you always know what was tried and why the winning approach was selected.
Models are evaluated on the metrics that matter for your task: precision, recall, AUC-ROC, calibration error, latency at P99, and cost of error per class. We set eval thresholds before training begins and only ship models that clear every bar, with full benchmark documentation at handover.
The final deliverable is a production service: containerised, versioned, observable, and connected to your data pipeline. Inference endpoints, feature stores, model registries, and monitoring dashboards are part of every engagement, not optional extras. Models go to production the same way software does.
Every model is evaluated on your data before deployment. Accuracy benchmarks are set at the start of each engagement and documented in the handover report. We do not ship models that do not clear the agreed evaluation thresholds.
Our engineers have taken models from notebook to production with feature pipelines, inference APIs, monitoring, and retraining logic. When we scope your engagement, we are scoping something we have done before, on data that looks like yours.
Every model is trained on your data, deployed to your infrastructure, and handed over with full ownership. We use open-source frameworks (PyTorch, scikit-learn, Hugging Face, MLflow), so there are no licence fees, no vendor lock-in, and no dependency on us to keep things running.
We agree on evaluation metrics and thresholds at the start of every engagement. If the model falls short of the agreed bar on your data, we surface that finding with a clear explanation before a deployment decision is made. Benchmarks are documented and reproducible.
We cover the full ML lifecycle: data preparation, feature engineering, model training, evaluation, deployment, monitoring, and retraining. The same team that builds the model operates the pipeline, so nothing gets lost between handoffs.
Selected work Real outcomes.
Selected ML engineering engagements across financial services, healthcare, logistics, and enterprise operations.
View All Case StudiesOur achievements display our capabilities
Trusted voices. Real outcomes.
“We truly appreciated their dedication, technical expertise, and problem-solving approach.”
Young Onion
Department Head
“I was blown away by the knowledge the team had about creatives, e-commerce, website design, and optimization.”
Decathlon Sports India
Image Leader
“They have a good team of designers and project managers who help us with the designs using HTML, Angular, and React.”
Instarama
COO
“Their creativity stands out. A collaborative team that delivered high-quality solutions working closely with us.”
CodeGama LLP
Business Developer
“We truly appreciated their dedication, technical expertise, and problem-solving approach.”
Young Onion
Department Head
“I was blown away by the knowledge the team had about creatives, e-commerce, website design, and optimization.”
Decathlon Sports India
Image Leader
“They have a good team of designers and project managers who help us with the designs using HTML, Angular, and React.”
Instarama
COO
“Their creativity stands out. A collaborative team that delivered high-quality solutions working closely with us.”
CodeGama LLP
Business Developer
“The product has become more intuitive and user-friendly. Load times dropped significantly after their work.”
Qoruz
Co-Founder
“Simply put, the quality of their code is excellent. They integrated third-party software and ensured GDPR compliance.”
VIA IOM
Director
“What impressed us most was how well they understood our brand and translated it into clean, thoughtful designs.”
GD Farm Fresh
Director
“The product has become more intuitive and user-friendly. Load times dropped significantly after their work.”
Qoruz
Co-Founder
“Simply put, the quality of their code is excellent. They integrated third-party software and ensured GDPR compliance.”
VIA IOM
Director
“What impressed us most was how well they understood our brand and translated it into clean, thoughtful designs.”
GD Farm Fresh
Director
A structured four-phase ML engineering process, from data assessment to production deployment. Every model ships with evals, documentation, and a monitoring setup your team can operate.
Book a call{ 01 }· 1 to 2 weeks
We profile your data volume, quality, label coverage, class distribution, and feature completeness. We define the prediction task precisely, agree on evaluation metrics and minimum accuracy thresholds, and scope the model architecture options viable on your data. You walk away with a clear view of what is achievable and what the build involves.
{ 02 }· 2 to 6 weeks
Structured experiments across baseline and advanced model families, tracked in MLflow with full reproducibility. Feature pipelines are built as production code from the start, not notebook scripts cleaned up later. Every training run produces benchmark outputs against the agreed evaluation criteria.
{ 03 }· Before deployment
Final model evaluation against the agreed thresholds on held-out test data. Calibration review, latency testing at target inference volume, bias assessment where relevant, and documentation of every benchmark result. Only models that clear every threshold proceed to deployment.
{ 04 }· Deployment and ongoing
Containerised inference endpoint deployed to your infrastructure, integrated with your data pipeline, with a model registry entry and live dashboards covering accuracy, latency, and data drift. Retraining triggers defined and documented, handed over to your team with a full operational runbook.
Engagement models, built to fit.
Senior ML engineers engaged from day one. Choose the shape that matches your use case, your timeline, and how much internal ML capability you already have.
A scoped ML engagement with a defined use case, evaluation criteria, and deliverable. Fixed price, a clear timeline, and a production model with full documentation at handover.
A senior ML engineering team embedded in your data and product workflows, building models, maintaining pipelines, and expanding your ML capability across multiple use cases as the programme grows.
We cover the full range of supervised and unsupervised ML: classification, regression, forecasting, ranking, anomaly detection, NLP, and computer vision. The common thread is that every model ships with defined evaluation criteria, benchmark results on your data, and a production deployment.
A focused single-model engagement, one use case, clean data, and defined labels, typically runs 4 to 8 weeks from data assessment to production deployment. More complex builds with multiple model components, significant data preparation, or custom MLOps infrastructure run 8 to 16 weeks. Timelines are confirmed in scope before any work begins.
The amount and quality of data depend on the task: classification problems typically need thousands of labelled examples per class, while forecasting models depend on the length and consistency of your historical series. The scoping call is where we assess what you have and what the task realistically requires.
You do, fully. The trained model weights, the feature pipeline code, the training scripts, the evaluation results, and all supporting documentation are yours at handover. We build on open-source frameworks (PyTorch, scikit-learn, Hugging Face, MLflow), so there are no licence fees and no dependency on us to run the system.
We agree on evaluation metrics and minimum accuracy thresholds at the start of every engagement, before any training begins, and document them in the project scope. Models are evaluated against these thresholds on held-out test data. If a model falls short of the agreed bar on your data, we surface that finding with a clear explanation before a deployment decision is made.
Every deployment includes monitoring dashboards covering accuracy, latency, and data drift. We document retraining triggers and provide a full operational runbook. For ongoing support, our embedded ML team engagement covers monitoring, retraining, and expansion to new use cases each month. You can also take the system fully in-house.
Yes. We build to integrate with the tools your team already uses: dbt, Snowflake, BigQuery, Redshift, Databricks, Spark, Airflow, and the most common feature store and ML platform options. The scoping call is where we map your existing stack and confirm the integration approach before any build begins.
Yes. If you have models in production but the infrastructure around them, pipelines, monitoring, retraining, versioning, needs strengthening, we scope and build the MLOps layer as a standalone engagement. This typically runs 3 to 8 weeks, depending on the complexity of your existing model estate.
Cost transparency, before the call.
We put numbers on the page. Here is the honest band by engagement type, plus the five variables that move the number once we scope your specific situation.
| Tier comparison | One use case, defined scope Single Model Build $15K - $45K ₹12L - ₹37L |
Most chosen
Most chosen Full ML Engagement $45K - $150K ₹37L - ₹1.2Cr | Platform infrastructure layer ML Platform and MLOps $40K - $120K ₹33L - ₹1Cr | Ongoing capacity Embedded ML Team from $15K / month from ₹12L / month |
|---|---|---|---|---|
| Timeline | 4 to 8 weeks | 8 to 20 weeks | 6 to 16 weeks | Ongoing monthly |
| What you get |
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Clean, labelled, well-structured data is faster to work with. Significant cleaning, label creation, or pipeline work from raw sources adds meaningful scope before any training begins.
A single binary classifier and a multi-model system with feature sharing, ensemble logic, and separate inference endpoints are very different engineering problems.
Computer vision and NLP models often need labelled training data that does not yet exist. Annotation cost and timeline depend on volume, label complexity, and whether human review queues are needed for ambiguous cases.
A model that must respond in under 100ms at 10,000 requests per hour needs a different architecture, infrastructure, and optimisation effort than a batch model running nightly.
A basic deployment with monitoring is included in every engagement. A full ML platform with model registry, A/B testing, automated retraining, and multi-team feature store support is a separate scoped engagement.
Bands include ML engineering, data engineering, deployment, documentation, and monitoring setup. Annotation services and cloud infrastructure are billed at cost.
Tell us the prediction problem you are trying to solve and what data you have available. A senior ML engineer replies within one working day. Direct conversation, no SDR, no qualification ladder.
Step 1 - Tell us the problem
The prediction or detection task you want to solve, and the data you have to work with. We sign an NDA before any specifics.
Step 2 - Speak to an ML engineer
A senior ML engineer joins within two working days to assess your data, your accuracy targets, and the shortest path to a production model.
Step 3 - Get a real plan
A recommended approach, agreed accuracy thresholds, a deployment path, and a cost band you can plan against.
Tell us the prediction problem you are trying to solve and what data you have available. A senior ML engineer replies within one working day. Direct conversation, no SDR, no qualification ladder.
