BrainHatch is a UK applied-AI company developing persistent, personalised services designed to help people overcome adversity and make measurable progress in the real world.
Applied AI
Machine learning built for tangible human growth.
Behavioural Science
Deep insights into human motivation and change.
Evidence-led
Proven interventions for real-world impact.
Adaptable Model
Personalised test-and-adapt framework.
Personalised AI for Human Progress
Building confidence through technology
Meet Capability Coach
Personalised support for sustainable employment and greater independence.
Capability Coach will be BrainHatch's first service. We are developing it for people who may need more than conventional job-search support to find, return to or remain in suitable work.
Capability Coach builds an evolving understanding of the individual — their health, capabilities, confidence, skills, circumstances, aspirations and barriers to work. It provides personalised support as those needs change: from rebuilding routine and confidence, to navigating the challenges of starting and staying in employment.
One person. Changing circumstances. Continuous support.
How it works
From conversations to outcomes
Some of the hardest human problems aren't solved by providing more information.
Finding sustainable work, rebuilding capability after illness or changing long-established behaviour requires support that understands the individual, adapts as circumstances change and is available when it is needed.
BrainHatch is exploring how persistent AI can provide that continuity at scale.
Our approach is simple:
Understand → Identify → Intervene → Observe → Measure → Learn → Improve
The objective isn't more engagement with AI. It's better outcomes in the real world.
First Principle
AI always human-backed
AI can provide continuity, remember context, support reflection and preparation, and help maintain momentum between human interactions.
But not everything should be automated.
Human expertise remains essential for safeguarding, clinical judgement, complex cases and decisions affecting people's rights or benefits.
Capability Coach is designed to extend the reach of human support, not replace the people who matter most.
Second Principle
Evidence before claims
Capability Coach is being developed. Its effectiveness has not yet been demonstrated in a controlled trial.
That's important.
We want to test whether persistent, personalised AI support can improve sustainable employment and work retention while protecting health, wellbeing, agency and trust.
Our approach is:
Build → Test → Measure → Learn → Improve
We intend to develop the evidence through rigorous real-world evaluation and collaboration with research and delivery partners.
Because knowing that something doesn't work can be just as important as discovering that it does.
Build the Evidence with Us
We are interested in conversations with researchers, universities, public-service and employment-support organisations, employers, behavioural scientists, technology partners and early-stage investors.
If you're interested in helping test where personalised AI can — and cannot — improve real-world outcomes, we'd like to hear from you.
Research partnerships
Capability Coach pilots
Technology & investment
Maya, 29 — health, confidence and a disrupted career
Starting point: Maya worked in hospitality management until a period of ill health. She has been out of work for 14 months. Her health is improving, but her sleep is irregular, confidence is low and applications feel overwhelming. She tells her work coach that she is “looking for jobs”; in reality she often avoids opening job sites because they make her anxious.
Weeks 1–6 — rebuild before job search. Capability Coach discovers through repeated conversations that Maya's immediate constraint isn't lack of vacancies or CV skills. It's routine and confidence.
They agree on very small actions: getting up consistently, leaving the house, reconnecting with a former colleague and gradually spending time thinking about work without immediately applying.
Crucially, Maya can say:
“I didn't do any of it this week. I basically hid from everything.”
She is willing to say this because she understands that the conversation is not a compliance report to the benefits system. Capability Coach can challenge avoidance without Maya fearing that an admission of a bad week will automatically be interpreted as non-compliance.
Months 2–4 — the problem changes. Maya's routine improves. Capability Coach remembers that she enjoyed training junior staff, hated late-night shifts and previously mentioned being good at handling difficult customers. Instead of simply returning her to hospitality, it helps her explore customer success and training roles.
Applications now become useful, so the intervention changes: CV, occupational exploration, application planning and interview practice.
Month 5 — employment. Maya gets a customer-service role. Capability Coach doesn't declare success and disappear.
Months 5–9 — retention. After three weeks she starts saying she is exhausted. The Coach connects this with earlier conversations about recovery and notices that she is slipping back into an all-or-nothing pattern: trying to prove she is “completely better”.
Support changes again: pacing, preparing a conversation with her manager, thinking through adjustments and protecting sleep.
What this case demonstrates: Capability → occupational fit → job search → transition → retention. The objective was never simply to maximise applications.
Born in the UK, Built for Progress
Restoring confidence and employment through human-centric AI. We empower individuals to reclaim their independence and achieve sustainable growth through innovative technology.
