About — Straight Up AI

Who You'll Work With

Straight Up AI is a specialist AI consultancy built on a decade of getting models into production — senior, hands-on delivery focused on outcomes, from problem framing to live systems.

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Daniel Miller
Founder & CTO

Straight Up AI is led by Daniel Miller. Daniel's spent the past decade getting AI into production — across large enterprises, mid-cap businesses and startups.

His path runs the length of the ML stack. He's lead ML infrastructure buildouts at easyJet, Dunelm. As a consultant he worked on novel recommendations research at Depop and re-architected complex vision systems at Tracr.

Daniel has also worked on generative and agentic applications at Blend 360 and HG Capital, developing evaluation frameworks to facilitate token reduction and accuracy in incomplete data contexts.

Daniel also recently taken on a CTO engagement for a client managing the full technology buildout, from product specification through to delivery. This combined experience positions Straight Up AI to operate across the stack and ensure that tech is alignged with outcomes, not the other way around.

What that means for you

A track record of shipping

A decade of AI projects that actually reached production — delivered across large enterprises, mid-cap businesses and startups, from recommenders serving millions to agentic systems built from scratch.

The full stack, end to end

We cover the full ML lifecycle and CTO responsibility, from product specification through to delivery.

We act as a partner and ensure AI becomes the solution, not the problem.

Outcome-focused delivery

Success defined by business results, not vanity metrics. Clear scoping, an honest read on feasibility, and models built for observability, cost and correctness from day one.

Selected results

A sample of outcomes delivered for clients across sectors.

3 min → 30 s
Re-architected a production ML platform for 4× horizontal scale and cut core workflow latency roughly six-fold.
Enterprise ML platform
50k / week
Brought new users into offline recommendations every week with no degradation in performance for existing users.
Marketplace recommendations
−75%
Cut average token consumption by grouping correlated LLM calls into semantically batched requests.
Healthcare GenAI
67% → 98%
Lifted summarisation accuracy with a purpose-built evals framework mapping meetings to structured summaries.
Financial services AI
Built & shipped AI for
easyJet Sainsbury's Dunelm Depop Tracr