AI & Machine Learning - From models to production

Over 70% of enterprises now integrate AI and ML into their operations. Yet only 15% of models reach production without proper MLOps. We bridge this gap - from agentic LLM applications and RAG systems to classical ML and predictive analytics - with governance, evaluation, and operational excellence built in.

Agentic AI & LLM Applications

Agentic systems represent the next evolution in enterprise AI - autonomous agents capable of complex reasoning, tool use, and coordinated action. Unlike simple chatbots, these systems can plan, execute multi-step workflows, and adapt to changing conditions.

We build with leading orchestration frameworks like LangGraph, CrewAI, and AutoGen that enable multi-agent collaboration. Modern protocols like Model Context Protocol (MCP) provide standardized interoperability between AI clients and backend systems.

Our implementations prioritize human-in-the-loop governance, ensuring AI systems remain auditable and aligned with business policies. We focus on domain-specific models that prioritize effectiveness over sheer size - smaller, tailored models trained on your enterprise data.

Capabilities

Multi-agent orchestration
Tool use & function calling
MCP integration
Memory & context management
Human-in-the-loop
NIST AI RMF governance

Enterprise RAG & Knowledge Systems

Retrieval-Augmented Generation (RAG) has become foundational infrastructure for enterprise AI. By grounding LLM responses in your organization's data, RAG enables accurate, contextual answers without the cost and complexity of fine-tuning.

We build production RAG systems that go beyond basic vector search. Our implementations include hybrid search combining semantic and keyword retrieval, multi-stage reranking, and graph-enhanced retrieval for complex knowledge relationships.

Enterprise requirements drive our architecture: document-level access controls, RBAC integration with your identity provider, comprehensive audit logging, and policy-aware retrieval that respects data governance boundaries.

Enterprise RAG requires more than vector similarity - it demands governance, access control, and retrieval strategies tailored to your knowledge architecture.

AI Engineering, Prag-Matic

MLOps & Production ML

Over 78% of enterprises now deploy ML models in production, yet only a fraction achieve operational excellence. MLOps bridges the gap between experimentation and reliable production systems, reducing deployment cycles from months to days.

We implement end-to-end ML pipelines with automated training, validation, and deployment. Our infrastructure includes experiment tracking, model versioning, feature stores, and monitoring systems that detect drift and degradation.

For LLM applications, we extend traditional MLOps with specialized evaluation frameworks - LLM-as-Judge systems, golden dataset curation, and automated regression testing that catches quality issues before they reach production.

MLOps Capabilities

Automated Pipelines. Continuous training, validation, and deployment with Kubeflow, MLflow, and cloud-native orchestration.
Model Monitoring. Real-time detection of data drift, performance degradation, and concept drift with automated alerting.
LLM Evaluation. Specialized evaluation frameworks for answer accuracy, groundedness, and agent trajectory assessment.

Traditional ML - Predictive analytics and classical machine learning

Beyond generative AI, classical machine learning remains essential for predictive analytics, classification, anomaly detection, and optimization problems where interpretability and precision matter.

Predictive Modeling. Demand forecasting, churn prediction, lifetime value estimation, and risk scoring using gradient boosting, neural networks, and ensemble methods.
Computer Vision. Object detection, image classification, OCR, and visual inspection systems for manufacturing, retail, and healthcare applications.
Natural Language Processing. Text classification, sentiment analysis, named entity recognition, and document processing beyond LLM-based approaches.
Anomaly Detection. Fraud detection, security monitoring, predictive maintenance, and quality control using statistical and deep learning methods.
Recommendation Systems. Personalization engines using collaborative filtering, content-based methods, and hybrid approaches for e-commerce and content platforms.
Time Series Analysis. Forecasting, trend detection, and seasonal decomposition for financial, supply chain, and operational planning.

Technology Stack - Tools we use

We leverage the latest AI/ML frameworks and platforms to deliver production-ready solutions.

Anthropic

OpenAI

Hugging Face

LangChain

Pinecone

PyTorch

TensorFlow

Python

MLflow

Jupyter

NumPy

Pandas

Scikit-learn

Spark

Databricks

Redis