Infrastructure for connected AI
QUASR builds the connective layer between AI models and the real systems they need to be useful — production-grade MCP servers, agentic tooling, and the protocols that let models actually do work.
Models keep getting smarter. The systems around them haven't caught up.
The frontier of AI is no longer the model itself. State-of-the-art inference is a commodity — what matters is what a model can reach: filesystems, databases, internal APIs, real-time data, and the long tail of tools that a business actually runs on.
Model Context Protocol, released by Anthropic and rapidly adopted by every major lab, is becoming the substrate for how models connect to the world. It's the moment between proprietary tool-use APIs and a shared protocol — the equivalent of HTTP for agentic AI.
QUASR builds in that gap. We ship MCP servers, integration tooling, and the operational glue that production AI systems need but don't yet have — the unglamorous infrastructure that turns impressive demos into reliable software.
The bet is simple: every company deploying AI in the next decade will need this layer. Most will buy rather than build it. We're positioning to be the default choice.
Protocol-first
MCP isn't a feature — it's the architecture. We build natively on the open standard, not proprietary connectors.
Production-grade
Real auth, real telemetry, real fault tolerance. Built for systems that run, not demos that look good.
Model-agnostic
Claude, GPT, Qwen, local — the infrastructure shouldn't care which model is on the other end.
Four products. One coherent stack.
Each piece is useful standalone. Together they form an opinionated path from a model to a production agentic system.
Domain-specific connectors
Production-ready MCP servers for filesystems, databases, dating profiles, real estate (UAD), and semantic image search. Each ships with auth, observability, and OAuth 2.1 support.
Local LLM integration layer
Custom Python bridge connecting local models (Qwen, GPT-OSS) to the MCP ecosystem. Brings tool-calling parity to self-hosted inference.
Hosted agentic infrastructure
FastAPI-based orchestration layer with HTTP wrapping, telemetry, and rate limiting. Turns experimental MCP servers into deployable services.
Developer SDK & templates
Scaffolds, patterns, and reference implementations for teams building their first MCP server. Reducing time-to-first-tool from days to hours.
Built, shipped, running.
QUASR isn't a deck. The infrastructure exists, in production, on real hardware. Here's what's already been built.
A builder, not a deck-maker.
I'm Lex — an AI infrastructure engineer with a long background in systems and adtech. For roughly six years I was a client-facing solutions engineer at FreeWheel (Comcast), specializing in large-scale video ad delivery. I learned how to build systems that don't fail at scale and how to translate technical capability into business outcomes.
In late 2025 I started building MCP servers — first as side projects for Claude Desktop, then as the foundation of QUASR. I've since shipped a Python bridge connecting local LLMs to MCP, a semantic image search system running on local vision models, a Next.js streaming chat client, and a growing portfolio of domain connectors.
QUASR is the consolidation of that work into a real company. I'm raising a pre-seed round to go full-time, hire a small team, and build the GTM motion to put QUASR's infrastructure into the hands of every team deploying AI in production.
Let's talk about what AI infrastructure should look like.
QUASR is currently raising a pre-seed round and is open to conversations with investors, design partners, and engineers interested in MCP-native infrastructure.