I’m preparing documents for my Tech Nation Global Talent (Exceptional Promise) application under the Digital Technology route, and I’d really appreciate some expert feedback before I finalise everything.
My background
I completed my PhD in the UK last October after 4 years of study. During my PhD, I worked as an Associate Lecturer and Research Assistant. I have 4 first-author publications and 2 co-authored papers.
After that, I started working as an AI Engineer at a UK company in June 2024, first part-time and then full-time from October 2024 until now.
Outside of my day in this year, I have been developing an MVP for a startup (not my own). I began working with co-founder at the idea stage early this year, and the company was formally registered not long ago.
At my current company, I have built 4 major AI projects:
Tracking system
Anomaly detection system
SOP tool
Bidding tool
Here is a structured summary of my documents and evidence for review.
Recommendation Letters 1. Former Automation Manager (just left the company last month)
Focus: my technical leadership across the 4 AI projects
2. Head of R&D
Focus: the impact and adoption of my AI projects for business value
3. PhD Supervisor
Focus: academic achievements and research capability
Mandatory Criteria (2 documents) 1. Evidence of leading bidding tool.
Documents like design, workflow, slide, code, and demo.
Supporting letter from Dev Project Lead, focusing on my role for the bidding tool project
2. Evidence of paper publications.
List of papers with title, date, link, cites, and explanation of impact and relevance to industry.
Optional Criteria
Optional Criteria 3 – Significant Contribution to Product-Led Companies (3 documents) 1. Evidence of work on the core product of a start-up.
Documents like PoC, slide, code, and demo.
Supporting letter from co-founder of startup (part my contribution).
2. Evidence of leading in anomaly detection system.
Documents like system architecture diagram, code, report.
Supporting letter from data engineer colleague.
3. Evidence of leading in SOP system.
Documents likes system architecture diagram, code, presentation, and company announcement.
Supporting letter from colleague and department lead involved in the project.
Optional Criteria 4 = Contribution to academic research (3 documents) 1. Evidence of student led teaching award (2024).
Associate lecturer contract and payslip during 2022-2024.
2. Evidence of presentation award (2023).
Certificate.
3. Evidence of research assistant project.
Project summary, model development, and evaluation results, research supervisor’s letter, contract, and payslip. (Note: this project was separate from my publications).
Hi @minkfm — you have a solid foundation, but the application needs further refinement to strengthen your overall case.
Mandatory Criteria (MC):
This section is currently weak. Simply leading a tool within your company and describing your role does not demonstrate industry recognition and may be viewed as part of your regular job rather than a significant achievement, which is what Tech Nation expects here.
The accompanying letter also reads like a description of a capable engineer rather than highlighting exceptional leadership or impact.
Your MC evidence should instead focus on a major initiative that created substantial impact for your company and resulted in recognition from the wider industry.
Optional Criterion 3 (OC3):
Ensure that all OC3 letters and evidences include quantified impact on core company metrics — such as revenue growth, user expansion, or efficiency improvements — directly linked to your contribution. Listing only your responsibilities or qualitative outcomes will not be sufficient for this criterion.
Optional Criterion 4 (OC4):
Confirm whether the award was given for outstanding applied work supported by strong academic excellence (e.g., first-class degree or distinction).
Position the recommendation letter at the top of this section, ensuring it explicitly endorses your potential to reach a world-class standard.
If you have multiple published papers, consider moving one from MC to OC4 — ideally one published in a top-tier, peer-reviewed journal.
Your application has a strong foundation, especially your combination of academic achievements and industry experience. I’ve seen similar profiles succeed when they position their evidence correctly. The main area to strengthen is your Mandatory Criteria approach - building tools internally at your company doesn’t demonstrate industry recognition by itself, which is what Tech Nation looks for.
For your MC evidence, focus on external validation of your work rather than internal achievements. Your publications are excellent for MC, but consider adding speaking opportunities about your research or industry recognition through technical blog posts that get cited. I’ve worked with PhD holders who strengthened their MC by publishing their industry insights on established tech platforms and getting invited to present at conferences based on their expertise.
Your Optional Criteria structure is well thought out, but ensure each OC3 evidence clearly shows quantified business impact with specific metrics. The startup work is promising - make sure to document concrete outcomes like user growth, revenue impact, or efficiency improvements your contributions created. Your academic awards work well for OC4, but position them to show how they demonstrate exceptional potential rather than just academic excellence.
This application has strong potential with the right evidence positioning. Your PhD background combined with industry AI projects creates a compelling narrative and matches successful candidates - when framed around innovation and recognition rather than just technical capability. Focus on demonstrating how your work has been recognized beyond your immediate workplace and you’ll have a much stronger case.
