How we Create Content at Azumo

We are an AI company, so we use AI to help produce the content on this site. We are also engineers who ship production systems for a living, so everything we publish is grounded in work we have actually done and is reviewed by the people who did it. This page explains who writes our content, how we make it, and the standard every page has to meet before it goes live.

Who creates it

Azumo's engineers, technical leads, and subject-matter practitioners write our content. It is not produced by an anonymous content team or a freelance network. Our authors write about what they build: AI systems, data engineering, and software delivery for clients in healthcare, finance, media, and other industries. Where a piece reflects a specific person's expertise, we attribute it to them.

How we make it

We built a proprietary engine that accelerates the development of our content. That engine is initiated by us through discussion and meetings and data and lastly a content brief. We run that brief through a multi-stage process that uses more than one AI model (including Anthropic's Claude, OpenAI's ChatGPT, Google's Gemini, Qwen, and Perplexity) deliberately. We use each model at the stage where we believe it does best:

1. Research. Before drafting starts, we gather primary sources and our own project records where we have permission to share.
2. Drafting and editing. Specialized models handle outlining, drafting, and copy-editing against a fixed set of voice and quality rules.
3. Quality checks. Every draft runs through an automated check for structure, evidence, and style, then a human reviewer reads it line by line.
4. Human sign-off. The owner of the brief approves the angle, the outline, and the final draft. Nothing publishes itself (that would be uncool).

From our perspective AI speeds the work but our engineers and team own the result.

Our evidence standard

We hold our content to the same bar we would apply to a vendor we were evaluating:

• ‍First-hand experience first. When we describe a result, it comes from a real engagement or a system we operate, with the specifics named. For example: an LLM intake-to-quote system we built for Angle Health that cut processing from 45 minutes to 5.
• ‍Named sources and real numbers. We cite the source and the figure, or we cut the claim. We try our best to not use "studies show" or "experts say" in place of named sources. We use Perplexity to source much of our 3rd party research. We use our own RAG pipeline built atop Qwen and Claude to manage our internal sources of evidence.
• ‍Honest about limits. When we do not have direct experience on a topic, we say so and rely on analysis and clearly attributed sources. We never attach a real result to an unrelated claim, and we never invent a credential.

Why we publish

Historically, we wrote for the technical buyers we work with: CTOs, VPs of Engineering, and the teams who have to run the systems we helped developed in production. We also write for non-technical domain experts who today have the ability to code unique applications purpose built for the problems they deal with and workflows they manage everyday.

The goal of a page is to help you make a better decision on us as a vendor. For instance Google has demoted pages they deem unhelpful to users through a lens they consider to be useful to audiences globally.

Our perspective is that there are very few clear-eyed perspectives on what makes for a good tool or good vendor in the market. Long ago we decided to use our insights on vendors for good and from time to time publish our thoughts on suppliers who are doing good work even when the SERPs ignore them.

We work in the market and know the market. If a page would not be useful to someone who came to us directly, we will not publish it.