Seattle, Washington

AI Integration & AI Product Development in Seattle.

RAG pipelines and autonomous LLM workflows built into your product. Mojo Studio delivers ai products for founders and businesses in Seattle, senior engineers only, weekly demos, full IP ownership.

How we build it

Built right,
shipped fast.

We integrate production-grade AI into real products — custom RAG pipelines over your own data, autonomous LLM agent workflows, and semantic search, built with the same engineering rigor as the rest of your stack. This isn't a chatbot bolted onto a landing page; it's AI wired into your actual product logic, with the guardrails to keep it from hallucinating in front of customers.

Custom RAG pipelines over your own data
LangChain / LangGraph agent workflows
Semantic search & vector database integration
Guardrails against hallucination in production
Local context

Built for Seattle's market.

Seattle's identity as a cloud-and-enterprise-software town — shaped by Microsoft and Amazon's gravitational pull on the local talent market — means engineering salaries here are among the highest in the US, which pushes even well-capitalized startups to look offshore for execution capacity on non-core product surfaces. Companies spun out of, or competing against, the big cloud platforms often need mobile or customer-facing web apps built fast while their core teams stay heads-down on infrastructure, a natural split for an India-based studio to own end-to-end. Seattle's gaming and aerospace-adjacent tech scene also creates steady demand for specialized, high-polish app work outside the enterprise-cloud mainstream.

Seattle's cloud & enterprise software and e-commerce businesses have specific needs when it comes to ai products — this is the local context that shapes how we scope every Seattle engagement, not a generic playbook applied everywhere.

Cloud & enterprise softwareE-commerceGamingAerospace-adjacent techLogistics tech
Our Process

From brief to launch.

01

Use-case scoping — where AI actually adds value vs. hype

02

Data pipeline & vector store architecture

03

Model integration (OpenAI, Gemini, Claude, or fine-tuned)

04

Evaluation, guardrails, and production monitoring

Common Questions

AI Products FAQ.

Grounded retrieval (RAG) over your actual data, tight prompt scoping, output validation layers, and explicit fallback behavior when the model isn't confident — hallucination is a design problem, not something you patch after launch.

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