Predictive modelling, recommendation engines, NLP and text analytics, RAG systems, LLM fine-tuning, and end-to-end MLOps — move from raw data to automated decisions and intelligent product features.
The gap between a promising proof-of-concept and a production AI system that drives business value is where most projects stall. These are the challenges we're built to solve.
Data science teams build models in notebooks, but 85% never make it to production. MLOps, serving infrastructure, and monitoring are afterthoughts.
Generic LLMs answer confidently but incorrectly on proprietary data. RAG and fine-tuning require an architecture that most teams haven't built.
Every new model iteration requires weeks of data preparation. Without a feature store, engineering work is duplicated across teams.
Production models degrade silently as data distributions shift. Without monitoring, you learn about failures from stakeholders, not dashboards.
Regulatory requirements for explainability (EU AI Act, HIPAA) demand audit trails and bias checks that most model deployment pipelines lack.
Models are built but their business impact — revenue lift, churn reduction, cost savings — is never instrumented or tracked to prove ROI.
We treat every AI and ML engagement as a production engineering challenge, not a research project. That means MLOps infrastructure — experiment tracking, model CI/CD, serving, and drift monitoring — is built in from the first sprint, not bolted on when the model is "ready."
For GenAI, we take a retrieval-first approach: build RAG on your existing data before investing in fine-tuning. Most enterprise GenAI use cases can be solved with good retrieval architecture, the right embedding model, and prompt engineering — without the cost and complexity of custom model training.
From RAG systems and LLM fine-tuning to predictive models and recommendation engines — built for production, not demos.
Retrieval-augmented generation pipelines on your private data — vector databases (Pinecone, Weaviate, pgvector), embedding pipelines, and LangChain or LlamaIndex orchestration for accurate, cited responses.
Domain-specific fine-tuning of open-weight models (Llama, Mistral, Falcon) using your proprietary data — PEFT, LoRA, and QLoRA techniques with full evaluation benchmarking.
Churn prediction, demand forecasting, fraud detection, lead scoring, and propensity models — from feature engineering and model training to production deployment and drift monitoring.
Collaborative filtering, content-based, and hybrid recommendation systems for e-commerce, content platforms, and SaaS products — serving millions of predictions per day at low latency.
Sentiment analysis, document classification, NER, topic modelling, and information extraction from unstructured text — custom models and pre-trained transformer fine-tuning.
End-to-end ML pipelines — feature engineering, experiment tracking (MLflow, W&B), model registry, CI/CD for model deployment, and production monitoring with drift detection and automated retraining.
A structured process that validates business value before investing in full production infrastructure — avoiding the most common AI project failure mode.
Identify and prioritise AI/ML use cases by business impact, data readiness, and feasibility. Define success metrics and ROI baseline before writing code.
Audit training data availability, quality, and labelling requirements. Identify feature sources, historical data depth, and any data collection gaps.
Build proof-of-concept models on a representative dataset. Validate that the signal exists in your data before committing to full production build.
Build feature pipelines, train candidate models, run hyperparameter tuning, and evaluate against held-out test sets and business benchmarks.
Package models for serving (FastAPI, BentoML, SageMaker), set up CI/CD for model updates, implement drift monitoring and alerting, and document rollback procedures.
Instrument business impact metrics (A/B test lift, revenue influence), schedule retraining on fresh data, and evolve the model as user behaviour and data distributions change.
We work across the modern AI stack — from open-weight LLMs and vector databases to cloud-managed ML platforms and custom serving infrastructure.
GenAI & LLMs
Vector Databases
ML Frameworks
MLOps
Model Serving
Feature Stores
Production AI systems delivering measurable business outcomes across enterprise, fintech, e-commerce, and professional services — in India, UAE, USA, Europe, and Australia.
RAG system over 200k internal documents (Confluence, Notion, PDFs) using Pinecone + GPT-4 — reducing tier-1 support tickets by 34% and onboarding time by 40%.
Gradient boosted churn model trained on 3 years of behavioural data — 78% precision at 30-day prediction horizon. Integrated into CRM for proactive outreach campaigns.
Hybrid recommendation system combining collaborative filtering and content signals — 22% uplift in average order value, serving 1.2M predictions/day at <50ms latency.
Fine-tuned Llama model for contract clause extraction, risk flagging, and obligation tracking — reducing manual review time from 4 hours to 20 minutes per contract.
We build models with MLOps from day one — feature pipelines, experiment tracking, model serving, and monitoring. Your models work in production, not just in demos.
Explainability, bias testing, and audit trails built into every model — aligned with EU AI Act, HIPAA, and internal governance requirements from the start.
Teams delivering AI solutions in India, UAE, USA, Europe, and Australia — covering regulated industries (healthcare, finance) and high-scale consumer contexts.
We build the data platform that your models train on — one team owns the full stack from raw ingestion to production predictions.
We instrument business impact from day one. Every model is tied to a revenue, cost, or quality metric — so you know the ROI before and after deployment.
