End-to-end AI engineering, from data to deployment.

Custom Machine Learning

We design, train and validate machine learning models for classification, ranking, recommendation, anomaly detection and scoring — starting from a clear business metric and the simplest model that can move it.

• Problem framing with a baseline and target metric agreed up front
• Feature pipeline and reproducible training code
• Model evaluation report: accuracy, bias checks and failure cases

• Python
• scikit-learn
• XGBoost / LightGBM
• PyTorch
• MLflow

Predictive Modelling & Simulation

Statistical and ML-based forecasting, operations-research optimisation and simulation models that let planners test decisions before committing to them — demand, inventory, pricing, capacity and risk.

• Forecasting models with honest prediction intervals
• Optimisation engines (linear / mixed-integer / heuristic)
• Scenario and Monte Carlo simulation tools for planners

• statsmodels
• Prophet
• PyTorch Forecasting
• OR-Tools
• SimPy
• Pyomo

AI & LLM Integration

We integrate AI into the software you already run: retrieval-augmented assistants over your documents, AI agents that act through your APIs, and automation of language-heavy work — with evaluation, guardrails and cost control built in.

• RAG pipelines over your documents with source citations
• Agents and tool-use workflows wired to your internal APIs
• Evaluation suites that measure accuracy before and after release

• Claude / GPT / open-weight LLMs
• LangGraph
• pgvector
• Qdrant
• FastAPI

Computer Vision & Document AI

Detection, segmentation and inspection models for images and video, plus document AI that extracts structured data from invoices, forms, KYC documents and reports — including Indian-language and handwritten content.

• Labelled datasets and annotation guidelines
• Detection / segmentation / OCR models tuned to your images
• Edge or cloud deployment, sized to your hardware

• PyTorch
• YOLO
• OpenCV
• ONNX / TensorRT
• Vision-language models

MLOps & Production Deployment

We build the platform around your models: automated training pipelines, model registry, CI/CD, containerised serving, and monitoring for drift and data quality — on your cloud or on-premise.

• CI/CD for models with automated tests and approvals
• Model registry, experiment tracking and lineage
• Containerised, autoscaled inference services

• Docker
• Kubernetes
• MLflow
• Airflow / Prefect
• Prometheus
• AWS · Azure · GCP

Data Foundations

Most AI projects stall on data, not algorithms. We consolidate sources, build reliable pipelines, and design warehouses and feature stores so your models — and your analysts — work from one trusted version of the data.

• Source audit and data-readiness assessment
• ELT pipelines with tests and scheduling
• Warehouse / lakehouse and feature-store design

• PostgreSQL
• dbt
• Apache Spark
• Kafka
• BigQuery / Snowflake / Databricks