AI/ML Solutions & Data Engineering
Models that reach production, and stay useful there.
AI/ML Projects, in plain terms
End-to-end machine learning delivery: the data engineering that makes modelling possible, the modelling itself, and the MLOps that keeps a model accurate months after launch. We are as interested in the pipeline and the monitoring as in the model architecture, because that is where most ML projects quietly fail.
What you get
- Models measured against a business metric, not only accuracy
- Pipelines that survive schema changes and late data
- Drift detection so degradation is caught before users notice
Who it is for
- Businesses sitting on data they have never turned into a decision
- Teams with a notebook prototype that cannot reach production
- Companies whose deployed model has silently drifted
- Enterprises evaluating generative AI for a concrete workflow
Everything in scope
No line item here is an upsell. This is what the engagement covers as standard.
Predictive analytics & forecasting
Demand, churn, risk and capacity models validated against a business metric, not just a validation score.
Natural language processing
Classification, extraction, summarisation and search over documents, tickets and conversations.
Computer vision
Detection, classification, OCR and quality inspection pipelines tuned for your imaging conditions.
Recommendation systems
Collaborative and content-based recommenders with online evaluation and cold-start handling.
Data pipeline engineering
Ingestion, transformation, feature stores and data quality checks that make modelling repeatable.
MLOps & monitoring
Versioned datasets and models, automated retraining, and drift detection with alerting.
How this engagement runs
You always know which conversation comes next and what it produces.
- 01
Frame the business problem
Define the decision the model will drive and the metric that proves it works.
- 02
Assess & engineer data
Audit availability and quality, then build the pipeline that feeds the model.
- 03
Model & validate
Baseline first, then iterate, with offline and business-metric validation.
- 04
Deploy to production
Serving infrastructure, latency budgets and safe rollout behind a feature flag.
- 05
Monitor & retrain
Drift detection, scheduled retraining and periodic model reviews.
What we build with
We choose from this set based on your constraints and existing stack, not on what we most enjoy using.
- ML
- PyTorchTensorFlowscikit-learnXGBoostHugging Face
- Data
- Apache AirflowdbtSparkKafkaSnowflakeBigQuery
- MLOps
- MLflowKubeflowSageMakerVertex AIFeature stores
- Generative AI
- Fine-tuningRAGEmbeddingsEvaluation harnesses
Discuss an AI/ML project
Tell us where you are now and what you are trying to reach. You will get a considered reply from a person who has done this work.
We respond to every enquiry within 24 business hours.
Or call +91 8412009002
Other services
- Software DevelopmentRequirement gathering to cloud cost optimization, under one roof.
- Staff AugmentationVetted engineers from 6 months to 20+ years, deployed fast.
- AI AgentsCustom agents that do the work, not just answer questions.
- Custom SoftwareSoftware shaped around your process, not the other way round.
Ready to talk about ai/ml projects?
Bring the constraint you are stuck on. We will be direct about whether we can help and what it takes.