Intelligent
Systems That
Scale Work.
We build Python AI systems that handle the work your team shouldn't be doing manually. RAG pipelines that understand your domain. Agents that reason through complex workflows. Automation that runs in production, not demos.
What We Build
RAG Pipelines
Retrieval-augmented generation systems that understand your technical documentation, codebase context, and domain knowledge. Built with LangChain, LlamaIndex, or custom vector search. Production-grade embedding strategies, chunking, and retrieval.
- ▸Custom embedding models
- ▸Hybrid search (dense + sparse)
- ▸Multi-modal document ingestion
- ▸Context-aware chunking strategies
AI Agent Systems
Autonomous agents that reason through multi-step workflows, call tools, and make decisions. Built with LangGraph, CrewAI, or custom orchestration. Function calling, tool use, error handling, and state management.
- ▸Multi-agent collaboration patterns
- ▸Custom tool integration
- ▸Agentic workflow orchestration
- ▸Guardrails and validation layers
Automation Backends
Python services that automate repetitive engineering work. Data pipelines, API integrations, scheduled jobs, and background processing. FastAPI, Celery, Redis, and PostgreSQL. Built for reliability.
- ▸ETL and data transformation pipelines
- ▸Third-party API orchestration
- ▸Scheduled task automation
- ▸Event-driven background workers
ML Model Integration
Integrate open-source or proprietary ML models into production systems. Model serving, inference optimization, API wrapping, and monitoring. PyTorch, TensorFlow, ONNX, or cloud AI services.
- ▸Model serving infrastructure
- ▸Inference optimization and caching
- ▸A/B testing frameworks
- ▸Model performance monitoring
Python AI Stack
- LangChain
- LangGraph
- LlamaIndex
- CrewAI
- OpenAI
- Anthropic
- Cohere
- HuggingFace
- Pinecone
- Weaviate
- Chroma
- Qdrant
- FastAPI
- Celery
- Redis
- PostgreSQL
What Teams Use This For
Technical Documentation Search
Engineers ask natural language questions, RAG system retrieves exact context from internal wikis, API docs, and runbooks.
Code Review Automation
AI agents analyze pull requests for security issues, performance problems, and style violations before human review.
Customer Support Triage
Agents classify support tickets, extract key information, and route to correct team with full context.
Data Pipeline Orchestration
Automated ETL systems that extract from multiple sources, transform, validate, and load into data warehouses.
Compliance Document Processing
Extract structured data from unstructured legal or medical documents, flag issues, and route for human review.
Real-Time Reporting Dashboards
Background workers aggregate data from multiple APIs, process metrics, and push to live dashboards.
How We Work
Discovery & Architecture
Understand the problem, evaluate data sources, design system architecture, and define success metrics.
MVP Build & Integration
Build core system, integrate with your infrastructure, implement key workflows, and run initial testing.
Production Hardening
Add monitoring, error handling, rate limiting, and edge case coverage. Deploy to production.
Most AI automation projects go from kickoff to production in 6-8 weeks. Larger systems may take 10-12 weeks. Retainer engagements allow continuous iteration and expansion.
Ready to Automate?
Tell us what your team is doing manually. We'll design a system that handles it.