About quickler
A company built around one stubborn pattern.
The job gets done first. The reliable structure arrives too late, so the finished output has to be rebuilt by hand. Quickler exists to close that gap, and it grew out of years of delivering exactly that kind of fix.
The founder
Why Philip Ross started quickler.
Philip founded quickler in Scotland after years building practical systems for organisations that had already done the hard part of the job but were still losing time to the cleanup afterwards. The company is new. The pattern it solves is not.
Background
Engineering, AI, and real delivery.
He studied Electrical and Mechanical Engineering at the University of Strathclyde, then worked at Dijuno, an AI analytics startup, building experience in LLM testing, prompt engineering, and Python automation. The stronger thread came through delivery: systems that solved awkward repeated work in the real world, not in theory.
The company
Quickler Ltd, incorporated March 2026.
Work on the site began on 6 March 2026, the name was settled on 9 March, and Companies House shows Quickler Ltd incorporated on 16 March 2026 (SC882439). The decision: instead of treating each workflow as a one-off, build one company around the pattern itself.
What we build now
Structure without the second pass.
Workflow systems that turn evidence from the job into reliable outputs, and AI that loads the real context before it helps. If the context matters, the system has to respect it.
Ready to see if it fits?
Try the live demo on WhatsApp.
It takes about two minutes. Any team with one repeated report or one document-heavy process is a strong fit.
About quickler
Quickler is a UK company building software for repeated operational work: the kind of job where the work happens first and the reliable structure arrives too late. It was founded by Philip Ross and incorporated as Quickler Ltd (company number SC882439) on 16 March 2026.
Founder: Philip Ross
Philip founded quickler in Scotland after years of building practical systems for organisations that had already done the hard part of the job but were still losing time to the cleanup afterwards. He studied Electrical and Mechanical Engineering at the University of Strathclyde, then worked at Dijuno, an AI analytics startup, where he built experience in LLM testing, prompt engineering, and Python automation. The more important thread came through delivery work: building systems that solved awkward, repeated work in the real world rather than in theory.
Across that delivery work the recurring problem was simple: the useful context already existed, but somebody still had to reconstruct the finished output by hand.
Why a company, not consulting
March 2026 was the point where that delivery experience turned into a clearer company direction. Instead of treating each workflow as a one-off consulting exercise, the better route was to build one company around the pattern itself: repeated operational work that becomes slower, shakier, and more expensive because the structure comes too late.
What we focus on
Quickler's work now focuses on two connected areas. The first is workflow systems that turn evidence from the job into better-structured outputs without making the process vague, brittle, or hard to trust. The second is AI agents that load the right context before helping with repeated operational work, so the answer is grounded in rules, history, and the actual task rather than guesswork. The common thread is straightforward: thoughtful systems, real-world usefulness, and software designed to remove admin without removing structure.