What we talked about
Dan Perera explains what JX does in simple terms: they help organizations:from small businesses to large enterprises:solve real operational problems using technology, including workflow automation, dashboards, custom software (ERP/CRM/SaaS), AI integrations, and even blockchain solutions when it fits. Dan emphasizes that every system and process ultimately comes back to people, so JX starts by understanding the human side of the problem before building anything.
Show notes
Dan Perera spent 20 to 25 years working across five continents, Australia, New Zealand, Singapore, Dubai, the UK, and now North America, before concluding that most organizations don’t actually solve their problems, they just keep patching them. His firm JX built its entire methodology around the habit he developed of walking into meetings, staying quiet, and then naming the elephant in the room that no one else would touch.
What we covered
- Dan’s path to starting JX began in corporate boardrooms across multiple countries, where he noticed the same pattern everywhere: committees would spend hours discussing symptoms while the real problem sat unaddressed. As an outside consultant, he had the political freedom to name it, something employees couldn’t do without risking their jobs.
- JX’s discovery process starts by mapping a client’s full weekly workflow horizontally, what happens Monday through Friday, touch point by touch point, before identifying any pain. Only then does Dan’s team ask what the ideal future state looks like, combining current pain removal with a five-year strategic roadmap into a single proposal.
- On the question of trusting AI in automation, Dan described a middleware algorithm JX built that sits between a client’s system and external AI models like OpenAI. The algorithm sends prompts out but blocks data from returning, meaning the client’s data stays within their own infrastructure. JX has tested this approach with 16 different AI models.
- Dan explained why locally trained models outperform general models for business applications: a model trained only on a company’s own data has a much smaller, more specific dataset to reason from, making it increasingly accurate over time compared to a model drawing from the entire global web. He described this as the key to making AI consistent and scalable.
- He pushed back on the idea that AI should be implemented everywhere by default, citing a car dealership where AI call routing frustrated customers who just wanted to speak to their usual service contact. His argument: the right question isn’t “do we need AI?” but “where in this specific workflow does AI actually improve the human experience?”
- His biggest early business mistake was ignoring cash flow management. He went back and completed a short course in entrepreneurship and finance, and now advises every early-stage founder to learn accounting, local regulatory requirements, and data privacy laws before launch, not after.
About Dan
Dan Perera is the founder and CEO of JX, a technology company that helps businesses modernize and scale through AI-driven software, intelligent automation, and human-centered product design. With a career spanning over two decades and multiple continents, he brings both technical depth and a background in public speaking and executive coaching to client engagements.
- LinkedIn: https://www.linkedin.com/in/dan-perera-jxdesign
- Website:
Episode 103 of the PreVetted Podcast.