AI & ML

Your AI Strategy,
Engineered to Production.

AI & ML engineering, from data to production.

From ML model development and MLOps pipelines to production AI systems and managed AI services — we engineer the full data-to-deployment lifecycle so your models do more than impress in demos.

Explore ROC
AI & ML
50+ AI models deployed
across enterprise production
MLOps & Model Ops
production-grade AI pipelines
50+ ML Models in Production
10× Faster Model Deployment
24/7 AI System Monitoring
Zero Model Drift Incidents (Managed)
What We Do

AI engineering, MLOps & production AI

BootLabs takes machine learning from notebook to production. Our AI engineering and MLOps practice covers data pipelines, model development, deployment, monitoring, and managed AI services — so your models stay accurate, observable, and governed in the real world, not just impressive in a demo.

It underpins our agentic AI systems, plugs AI into delivery with AI in the SDLC, stays reliable in production via our Resilient Operations Center, and is proven in the case studies.

Capabilities

AI & ML engineering services we offer.

Three production-grade AI & ML capabilities — covering the full data-to-deployment lifecycle.

ML Engineering & Model Development

We build end-to-end ML systems — from data ingestion and feature engineering to model training, evaluation, and packaging. We work across supervised, unsupervised, and reinforcement learning paradigms.

PyTorch TensorFlow Scikit-learn Feature Engineering Model Evaluation
MLOps & Model Lifecycle Management

We build MLOps platforms that automate the model lifecycle — training pipelines, experiment tracking, model registry, A/B testing infrastructure, and automated retraining triggers.

MLflow Kubeflow SageMaker Model Registry CI/CD for ML
Production AI Systems

We deploy ML models into production with low-latency inference infrastructure, model serving layers, drift detection, and observability — so your models stay accurate and performant at scale.

Model Serving Triton BentoML Drift Detection Inference Optimization
Technology Depth

AI models, strategies & frameworks we work with.

Deep expertise across the full AI/ML ecosystem — from foundation models and reasoning strategies to production frameworks.

Foundation Models
OpenAI GPT-4o / o1 Anthropic Claude 3.5 Google Gemini 1.5 Pro Meta Llama 3.1 / 3.2 Mistral Large AWS Bedrock Azure OpenAI Service Cohere Command R+ Google Vertex AI
AI Strategies
Retrieval Augmented Generation (RAG) Agentic AI & Multi-Agent Fine-tuning & RLHF Prompt Engineering Model Distillation Vector Search & Embeddings Guardrails & AI Safety Structured Output & Tool Use Long Context & Reasoning
Frameworks & Skills
LangChain & LangGraph LlamaIndex Kore.ai Platform AutoGen & CrewAI Haystack HuggingFace Transformers MLflow & Weights & Biases Ray / Anyscale Pinecone / Weaviate / pgvector
Our Approach

How we deliver production AI.

01
Frame

Define the ML problem, data availability, and measurable success metrics

02
Data & Features

Curate, clean, label, and engineer features from raw data sources

03
Model Dev

Train, evaluate, and iterate on models — from baseline to production-ready

04
MLOps

Build training pipelines, deploy to serving infra, and wire monitoring and retraining

05
Scale & Govern

A/B testing, model versioning, drift detection, and compliance reporting

Client Outcomes

AI in production, at a leading bank.

Financial Services · AI Agent
Email Classification & Response Agent for a Leading Private Sector Bank
A large private sector bank processed thousands of inbound customer emails daily across 20+ product lines — each with 30–40 distinct intents. Manual triaging created backlogs, inconsistent responses, and SLA breaches. BootLabs deployed a fully on-premise AI agent to classify, route, and draft responses automatically.
20+
Banking Products
600+
Intents Modelled
70%+
Auto-Routed
On-Premise LLM Mistral / LLaMA CRM Integration GenAI Agent
Read case study
Financial Services · AI Pipeline
Universal Document Processor That Cuts AI Cost by 60% at a Leading Private Sector Bank
The bank handled 15+ document types — from structured forms to unstructured correspondence — each requiring different processing logic. Running everything through large LLMs was expensive and slow. BootLabs built an intelligent routing pipeline that classifies documents and sends each type to the most cost-efficient model.
60%+
AI Cost Reduction
15+
Document Types
95%+
Extraction Accuracy
Document AI Model Routing Cost Optimisation On-Premise
Read case study
FAQ

AI engineering, answered.

What is the difference between AI engineering and data science?

Data science explores data and builds models; AI engineering makes those models work in production — the pipelines, deployment, monitoring, guardrails and MLOps that keep a model accurate, observable and cost-effective once real users depend on it. BootLabs focuses on that production discipline.

What does MLOps include?

MLOps covers the automation and operations around machine learning: reproducible training pipelines, model versioning and registry, CI/CD for models, automated evaluation, deployment (batch, real-time, on-prem or cloud), and monitoring for drift and performance. It's DevOps applied to ML.

Do you build with open-source or commercial models?

Both — we're model-agnostic. Open-source LLMs and classic ML you host yourself, or commercial APIs (OpenAI, Anthropic, Vertex AI, Bedrock), chosen per use case, data-residency need and cost. Many regulated clients run models on-premise or in their own cloud.

Production AI, built by engineers who have shipped it at scale.

Book a discovery call with our AI engineering team. We'll assess your data, infrastructure, and use cases — and define a path to production.