'/%3e%3crect%20width='1440'%20height='900'%20fill='url(%23light)'/%3e%3crect%20x='690'%20width='750'%20height='900'%20fill='url(%23grid)'/%3e%3ccircle%20cx='1160'%20cy='490'%20r='390'%20fill='none'%20stroke='%236caaa0'%20stroke-width='2'%20opacity='.14'/%3e%3ccircle%20cx='1160'%20cy='490'%20r='310'%20fill='none'%20stroke='%23d0ff59'%20stroke-width='2'%20opacity='.09'/%3e%3cg%20transform='translate(805%20144)'%3e%3crect%20x='35'%20y='45'%20width='522'%20height='374'%20rx='23'%20fill='%23132c36'%20stroke='%2376abb3'%20stroke-width='4'/%3e%3cpath%20d='M35%20107h522'%20stroke='%2376abb3'%20stroke-width='3'/%3e%3ccircle%20cx='75'%20cy='77'%20r='9'%20fill='%23ceff52'/%3e%3ccircle%20cx='109'%20cy='77'%20r='9'%20fill='%23528f91'/%3e%3ccircle%20cx='143'%20cy='77'%20r='9'%20fill='%23528f91'/%3e%3cpath%20d='M104%20161l-49%2045%2049%2045M191%20153l-53%20111M222%20161l49%2045-49%2045'%20fill='none'%20stroke='%23ceff52'%20stroke-width='9'%20stroke-linecap='round'%20stroke-linejoin='round'/%3e%3cpath%20d='M320%20160h175M320%20199h123M320%20238h159M81%20322h417'%20stroke='%2378aeb1'%20stroke-width='10'%20stroke-linecap='round'%20opacity='.76'/%3e%3cpath%20d='M250%20420v40m-98%200h197'%20stroke='%2389a9a8'%20stroke-width='14'%20stroke-linecap='round'/%3e%3c/g%3e%3cpath%20d='M0%20890H1440'%20stroke='%23ceff52'%20stroke-opacity='.12'%20stroke-width='7'/%3e%3crect%20width='1440'%20height='900'%20fill='url(%23shade)'/%3e%3c/svg%3e)
AI where it earns its place
We use models as components, not as excuses. The product still needs clear inputs, useful outputs, failure states, evaluation, security, latency targets, and a reason to exist.
Custom design
Production engineering
Senior & staff-level expertise
Launch + operations
No AI slop. No template churn.
AI can accelerate research, implementation, and iteration. It does not get to make product, design, architecture, testing, security, or launch decisions unattended.
We build the thing people will actually use: opinionated design, conventional engineering discipline, explicit quality gates, and enough operational thinking to keep the launch from becoming someone else's cleanup project.
Outcomes worth shipping
- 01A specific user job improved by AI instead of an AI-shaped demo
- 02Evaluation criteria tied to product behavior and business risk
- 03Human review and fallback paths where uncertainty actually matters
- 04Conventional software boundaries around model calls, data, and permissions
Strategy through production
- AI product discovery and workflow design
- LLM and multimodal feature integration
- Retrieval-augmented generation (RAG), semantic search, and grounded answers
- Model Context Protocol (MCP) servers and tools, agent workflows, and model routing
- Evaluation harnesses, regression tests, tracing, and feedback loops
- Prompt, data, access-control, and human-in-the-loop design
- Cost, latency, reliability, and model-provider resilience
- MLOps and LLMOps integration for model deployment, evaluations, and production monitoring
Bring us a real constraint
Teams with a real workflow that could benefit from model assistance
Companies trying to turn an internal AI experiment into a dependable product
Products that need to replace AI slop with measurable quality
Before you call
Do you offer AI development consulting and custom AI integrations?
Yes. We help teams evaluate useful AI opportunities, integrate language or multimodal models into existing products, and implement the engineering, security, and review workflows around them.
Can you build RAG applications and LLM-powered workflows?
We can design retrieval-augmented generation (RAG), semantic search, MCP tools, and agent workflows with source grounding, quality evaluation, monitoring, and human fallback paths.