Dashboards, Chatbots, APIs, Applications
Model Training, Serving, MLOps, Feature Store
Data Lakehouse, ETL/ELT, Streaming, Cataloging
Cloud / Hybrid, GPU Clusters, Kubernetes, Storage
Unified storage combining data lake flexibility with warehouse performance. Supports structured, semi-structured, and unstructured data.
Real-time and batch ingestion with ELT/ETL workflows. Event-driven architecture for streaming analytics.
Automated data quality checks, lineage tracking, master data management, and access controls.
Centralized repository for ML features with versioning, serving, and monitoring capabilities.
Notebook environments, hyperparameter tuning, experiment tracking
Distributed training on GPU clusters, automated retraining
Model evaluation, bias testing, explainability analysis
Containerized serving, A/B testing, canary rollouts
Drift detection, performance alerts, feedback loops
Natural language input
Vector search over knowledge base
Relevant docs injected into prompt
Grounded, accurate response
GPT-4, Claude, Llama, Gemini — API and self-hosted
Domain-specific adaptation with LoRA, QLoRA, RLHF
Guardrails, templates, chain-of-thought, tool use
Pinecone, Weaviate, pgvector for semantic search
Multi-step reasoning, tool orchestration, memory
Enterprise AI isn't just technology — it's a strategic transformation.
Let's architect it together.