AI software development that ships — not experiments that stall.

Custom LLM apps, RAG pipelines, predictive models, and autonomous agents — designed, built, and deployed on your infrastructure by a pod of senior AI engineers.

Six ways AI earns its keep.

Custom LLM Applications

Chat interfaces, copilots, and internal tools — with your prompts, your data, your brand voice.

RAG & Knowledge Systems

Retrieval-augmented generation over your docs, tickets, and code — with permissions and audit logs.

Autonomous Agents

Task-level agents that run workflows across your SaaS stack with observability built-in.

Predictive Analytics

Forecasting, churn, demand, risk — ML models deployed as APIs or dashboards.

NLP Pipelines

Sentiment, intent, classification, summarization, and entity extraction at production scale.

Computer Vision

Object detection, OCR, visual search, and quality control — on-device or cloud.

Four weeks from call to commit.

01

Discovery

2-hour workshop. We map data, users, success metrics, and constraints.

02

Prototype

Week 1 delivery. Working prototype against real data, not mock demos.

03

Production

Week 2–4 hardening. Evals, monitoring, permissions, and deployment.

04

Compound

Optional retainer. We tune, expand, and improve as your usage grows.

Opinionated, but stack-agnostic.

We pick the best model and framework for the job — and integrate with what you already run.

Models

  • • OpenAI (GPT-4o, o-series)
  • • Anthropic (Claude Sonnet/Opus)
  • • Meta Llama, Mistral, DeepSeek
  • • Self-hosted & fine-tuned

Infrastructure

  • • AWS · GCP · Azure
  • • Vercel · Fly · Railway
  • • Pinecone · Weaviate · pgvector
  • • LangSmith · Arize · Braintrust

App layer

  • • Next.js · Remix · Tauri
  • • Python (FastAPI, Django)
  • • Node (NestJS, Hono)
  • • React Native · Flutter

Have a scoped idea? Let's build it.