LLMWare.ai

Model HQ lets you build multi-step AI workflows without writing code. You chain models, data sources and logic into a repeatable process, from a simple summarize step to a multi-stage pipeline that reads a document, extracts fields and routes a result. That moves the product past a chatbot. It runs business tasks end to end, and the company positions the no-code builder as the piece that opens AI automation to people who never touch Python.

The platform ships a catalog of more than 250 small language models, prepackaged in quantized formats so they run on device GPUs and NPUs. It lists the SLIM family (Structured Language Instruction Models) for function calling and classification, the DRAGON models tuned for RAG question answering, and BLING models for quick prototypes. It also supports leading cloud models from OpenAI, Anthropic and Google, so you mix local and cloud where each makes sense.

Model HQ includes a built-in retrieval-augmented generation pipeline, the same technology behind the open source llmware framework. You point it at a folder of PDFs, Word documents or spreadsheets; it parses, chunks and indexes the files, then answers questions using that context. The company connects it to a range of vector databases, including FAISS, Milvus, Pinecone, Qdrant, Postgres (PG Vector), Redis and Chroma, for teams that already run one.

The client agent is built to use the silicon already on a modern laptop. It runs across CPUs, GPUs and NPUs, and is optimized for chips from Intel, AMD, Apple, NVIDIA and Qualcomm Snapdragon. The company cites average download times of around 10 seconds for the app and under 30 minutes for a set of models onto a device. Models up to 32B parameters run on the latest AI PCs, so you get useful capability without a server.

Because inference runs on your own hardware, there are no per-token charges and no metered API bill. The company puts the expected incremental per-token cost on an AI PC at $0. Most workflows run without a network connection, which matters for field teams, secure facilities and anyone who would rather not depend on an outside service being up. Your data stays in your environment by default.

Model HQ is designed to run in three places: on AI PCs, in your data center for batch inference and RAG over sensitive data, or in a private cloud for time-sensitive and large operations. The three can link together for a mixed setup, so a laptop agent pulls from a central deployment when it needs bigger models. Enterprises can build AI agent apps and deploy them to staff devices across the organization.

The platform bundles the controls a regulated buyer asks for. It includes AI explainability, PII filtering, toxicity and bias monitoring, and hallucination detection. Enterprise deployments add monitoring, updates and scaling across mixed hardware. Combined with on-device execution, that gives compliance teams a way to answer where the data went and what the model saw.