Resources
Case studies, practical tools and first-hand thinking from our work.
Products, platforms and delivery experience, with the detail behind what was built and what changed.
Answer a few questions about where you are and see which route is most likely to fit.
Work through the friction, opportunity and current way of working to identify areas worth investigating.
Articles on AI adoption and the work, judging and delegating to AI, agents, technology decisions and delivery.
The detail behind selected products and delivery work.
TechGuidr engagement · Economic research
A research business needed customers to find its current thinking across a large archive. Answerable brought cited research answers into daily use; Foreseeable brought AI into its account teams’ workflow.
Answerable: in production and daily use. Foreseeable: in production.
Read the case study: Two AI products built for a research businessTechnology leadership · Learning and wellbeing
A learning and wellbeing platform needed its applications, data and deployment foundations brought into a coherent system. An architectural review led to a rebuild of both principal applications and the iOS and Android apps, and a migration of the cloud estate.
Both rebuilt applications and the iOS and Android apps entered production.
Read the case study: Rebuilding the foundations of an enterprise platformSee the wider TechGuidr portfolio and experience in one place.
A guided way to work out which TechGuidr starting point best matches your situation.
Find your routeA short assessment of the work, friction and potential opportunity, with areas worth investigating and the assumptions behind the result.
Take the Value CheckA free worksheet for one part of the business: what is holding it back, the starting point, the options, a fair test and the next decision. Read, print or download it.
Open the briefFirst-hand thinking on AI adoption and the work, human judgement, agents, technology decisions and delivery.
A polished recommendation should survive questions about its evidence, assumptions and limits.
Name the task, the decisions and the consequences before choosing how much autonomy a system should have.
Familiar tools become useful when they fit the job. The same test belongs in your AI investment decisions.
People need to know when to trust an AI tool, as well as how to use it. Practical learning should cover both.
An AI conversation only becomes useful later if its conclusions, evidence and next steps can be found again.
Dan’s LinkedIn newsletter on technology, AI, leadership and the decisions around them.
