Capabilities

Architecture, data, AI, and production execution.

Data Platforms and Architecture

  • End-to-end extraction, ingestion, normalization, transformation, and analytics pipelines
  • Iceberg, Parquet, Redshift, PostgreSQL, MongoDB, SQL Server, and performance-oriented storage patterns
  • Lakehouse-style architecture, schema strategy, partitioning, query performance, validation, and reproducibility
  • Distributed processing patterns using Spark-class systems, queues, containers, and event-driven processing

Forecasting, Analytics, and Decision Support

  • Time-series forecasting and prediction platforms for operational planning, demand planning, pricing, inventory health, and energy-market analysis
  • Model experimentation, evaluation, comparison, deployment, and dashboard integration
  • Operational dashboards backed by production data pipelines and model outputs
  • Decision-oriented systems built for transparency, correctness, and operational use

AI-Enabled Systems

  • Multi-backend and multi-model AI systems using GPT, Claude, Llama, Gemini, Mistral, DeepSeek, Qwen, and Kimi-class models
  • Document-grounded AI applications using LLMs, embeddings, vector stores, and structured/unstructured document search
  • AI-enabled business applications combining traditional software architecture with LLM interfaces, automation, and decision support
  • Model evaluation and deployment workflows using PyTorch, MLflow, Hugging Face, LoRA/QLoRA, and quantized deployment patterns

Autonomous Development Orchestration

  • Supervisor-driven execution architectures coordinating planning, execution, review, verification, reconciliation, and governed progression across multiple AI coding backends
  • Agentic workflows, task contracts, context contracts, evidence capture, resumable execution state, and human-supervised progression
  • Deterministic verification, structured retry economics, failure classification, reviewer/verifier patterns, acceptance reconciliation, and reproducible execution
  • Sandboxed workflows with governed repository access, git automation, bounded execution, and minimal-blast-radius change management

Modernization and Backend Platforms

  • Legacy platform modernization without operational disruption
  • API-centric business platforms and backend systems in Python and Java
  • Service-oriented and event-driven architectures, REST interfaces, and integration layers
  • Cloud-native and hybrid architectures using AWS, Docker, queues, serverless components, and production deployment practices

Industry Domains

  • Healthcare distribution and hospital-system data extraction
  • Supply chain, pricing, manufacturing, logistics, and operational analytics
  • Pricing systems, contract analytics, and revenue management platforms
  • Data-intensive platforms where scalability, correctness, maintainability, and extensibility matter