Become an AI-native company.
If you treat AI as a tool you bolt on, you'll get a faster version of the old company.
But build it as the operating system you run on, and a small team can outpace much bigger rivals.
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What it means to be AI-native
An AI-native company isn't one with more AI tools. It's built on three things.
A company brain
Everything your company knows, captured so AI can actually use it.
AI at the center
AI runs routine operations; people do the work that needs a human.
Systems that self-improve
They watch their own results, catch what's not working, and get better on their own.
Who you're working with
Experimental Technology was founded by Huw Walters. Over the past year we've worked hands-on with small and mid-sized businesses putting AI at the center of how they operate. Huw has worked in AI since 2018, including as a Staff AI Product Manager at Meta and as Head of Product at an AI startup acquired by TikTok, and holds a degree in philosophy and psychology from the University of Oxford.
We're not a platform or a big consultancy. We're a small, senior team that works alongside yours, blending advice, engineering, and hands-on teaching so your people can run what we build themselves.
Things we've built
A regulation brain over a vast legal corpus.
- Reasons over a huge corpus of scanned legal and policy documents
- Answers plain-English questions, grounded only in that material and drawn from real source passages
- A custom interface lets experts inspect each passage behind an answer, highlighted against the original document
- Turns a body of material too large to wade through into something they can explore and trust
A company brain for an engineering team.
- A living, queryable knowledge layer over the team's projects, decisions, and tooling
- One trustworthy place to ask what the status of something is or why a choice was made
- Because the full context sits in one place, AI can draw on it to write highly contextualised product requirements, tests, and documentation
Turning sales calls into usable insight.
- Maps what was said on sales calls to a structured value-and-pain framework
- Surfaces which pains came up, who raised them, and how strongly
- Aggregates recurring themes across many calls
- Lets commercial teams act on consistent, evidenced signal rather than memory or one-off summaries
A marketing engine for fast, on-brand campaigns.
- A shared library of AI skills for producing on-brand assets from the same building blocks
- Available as a Claude Cowork plugin, one front end onto those skills
- And as a Notion workflow layer that leverages the same skills: an operator enters their requirements on a card and kicks off generation
- An agent produces the full set of campaign assets, a person reviews them, and approved assets publish out to the website and social
An AI researcher that feeds the company brain.
- A research agent that works across the open web and other sources, grounded in the company's own knowledge base
- Produces briefings good enough to inform real decisions and to seed published writing
- What it learns feeds back into the company brain, so research compounds into shared knowledge rather than one-off answers
An AI data scientist for teams without one.
- Works like a data scientist on call
- Helps you brainstorm which questions are worth asking of your data given your goals
- Turns plain-language questions into the underlying analysis
- Returns the answer, sometimes as a direct figure, sometimes as a clear visualisation
Get in touch
Ready to explore what becoming an AI-native company could look like for you? Let's start a conversation.