What we do

Our Services

01

Data & ML Infrastructure

  • Data lakehouse architectures on Databricks, Snowflake, and Spark for large-scale analytical and operational workloads
  • Data mesh architectures for domain-oriented data ownership and decentralized data platform design
  • Batch and streaming ETL pipelines with dbt, data quality checks, anomaly detection, and end-to-end observability
  • Workflow orchestration using Airflow, Prefect, and Dagster for reliable, scheduled data and ML pipelines
  • High-throughput streaming systems on Kafka and Flink for real-time processing and event-driven architectures
  • Reverse ETL pipelines syncing warehouse outputs back into operational tools and business systems
  • Vector database-backed retrieval and recommendation engines for semantic search and ranking
  • ML platforms covering feature engineering, model registry, serving, and production monitoring with MLflow and Weights & Biases
  • Infrastructure for distributed multi-GPU training, automated deployment, and continuous model evaluation
  • CI/CD pipelines for reproducible, production-grade ML and data system deployments
02

AI & ML Systems

  • LLM-powered agents, RAG pipelines, and GenAI workflows using LangChain and LangGraph for automation and internal tooling
  • Structured output, function-calling, and tool-use systems for production integrations across OpenAI, Anthropic, and Gemini APIs
  • Fine-tuning and RLHF pipelines for domain-specific model adaptation using HuggingFace and PyTorch
  • AI evaluation frameworks covering benchmarking, regression testing, and red-teaming of model outputs
  • Computer vision systems for detection, classification, and retrieval - from architecture to production
  • Speech and audio AI systems including ASR, TTS, and speaker diarization
  • Multimodal AI systems that unify heterogeneous data sources into a single inference pipeline
  • Inference optimization using Triton, TensorRT, and ONNX for low-latency production serving
  • Self-supervised, contrastive, and generative learning with JAX and PyTorch for low-label and large-scale training regimes
  • Reinforcement learning systems for optimization, simulation, and sequential decision-making
03

Backend & Platform Engineering

  • Cloud-native distributed systems on AWS and Kubernetes with fault tolerance, auto-scaling, and observability
  • Infrastructure as Code using Terraform and Pulumi for reproducible, version-controlled cloud environments
  • Database engineering covering schema design, query optimization, and migrations across Postgres and MySQL
  • Caching and session infrastructure with Redis for high-performance, low-latency backend systems
  • REST and GraphQL API design, service mesh patterns, and inter-service communication for high-throughput backends
  • Full-text and semantic search infrastructure using Elasticsearch and OpenSearch
  • Containerized deployments and service orchestration with Docker and Kubernetes
  • SRE practices covering SLO/SLA management, incident response, and reliability engineering
  • Authentication, authorization, and secrets management for secure, production-grade systems
  • PII encryption, compliance tooling, and data governance for regulated environments