Ollama
Ollama runs open-weight language models on your own machine or server. One command downloads a model and serves it behind a local API that most existing AI tooling can talk to, so an application built against a hosted provider can point at your own hardware instead. Sensitive data stays inside your network, there are no per-token fees, and it keeps working with no internet connection at all.
View the repo · ollama/ollama ↗Firms that want to test AI on private data without sending it to a third party, and developers who want a fast local loop while prototyping. It also suits regulated environments where data residency ends the hosted-API conversation before it starts. It is the wrong choice when you need frontier-grade reasoning on hard tasks, or when nobody wants to own a GPU server; in those cases a hosted model behind a careful data agreement is more practical.
Install itcurl -fsSL https://ollama.com/install.sh | sh
ollama run llama3.1Model quality depends heavily on your hardware. Small quantised models run on a decent laptop, while anything capable enough for production work generally wants a dedicated GPU with plenty of memory. Open models are improving quickly and still trail the frontier on reasoning-heavy tasks, so test on your real workload before committing. The server binds locally by default; if you expose it across a network, put authentication and TLS in front, because it ships with neither.
Where Blash AI comes inWe help you decide, task by task, where a local model is good enough and where a hosted frontier model earns its fee, then build the workflow around that split. A common pattern pairs Ollama with LiteLLM from this library, so the same code routes routine work to a local model and hard cases to a hosted one, with the switch made in configuration rather than a rewrite.
When you want this running on your real stack, that is the engagement
Book an AI audit →n8n →
n8n is workflow automation you run on your own infrastructure. It connects the applications a business already uses through a visual editor, with hundreds of prebuilt nodes, a code step for anything bespoke, and native support for calling language models mid-flow. Think of it as a self-hosted alternative to Zapier or Make that you own outright, including every piece of data passing through it.
LiteLLM →
LiteLLM is a gateway that lets you call models from Anthropic, OpenAI, Google and many other providers through one consistent API. It runs as a Python library inside your code or as a standalone proxy server in front of your whole stack, with cost tracking, per-key budgets, rate limits and automatic fallbacks for when a provider has a bad day.
Chroma →
Chroma is an open-source vector database, the store behind retrieval-augmented generation. You embed your documents, Chroma indexes the embeddings, and at question time it returns the passages closest in meaning to the query so a model can answer from your own content with citations. It runs embedded inside a Python process for prototypes or as a standalone server for shared use.