Ship AI features that actually work in production
Custom machine learning systems and LLM-powered features, built and deployed end to end — not a notebook that never ships.
The problem
What this solves
Most teams know they need AI in their product but don't have the in-house ML engineering to go from idea to a reliable, monitored, production system. Off-the-shelf AI wrappers get you a demo, not a durable feature.
Deliverables
What's included
- Model selection, evaluation, and integration (open-source or hosted APIs)
- RAG pipelines and vector search for domain-grounded answers
- Custom inference services with monitoring, logging, and fallback handling
- Prompt engineering and evaluation harnesses for LLM features
- Data pipelines for training, fine-tuning, and continuous evaluation
Process
How it works
Scope
Define the ML problem, success metrics, and constraints (latency, cost, accuracy).
Prototype
Build a fast offline prototype to validate feasibility before committing to infra.
Productionize
Wrap the model in a monitored service with proper error handling and fallbacks.
Iterate
Ship, measure against real usage, and tune the system with production feedback.
Tools & stack
What we use
Related work
Case studies
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An LLM-powered triage layer that routes and summarizes inbound support tickets in real time.
DocuParse — Structured Document Extraction API
An API that turns messy PDFs and scanned documents into clean, structured JSON on demand.
FAQ
Common questions
Available for new projects
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