Abdul Aleem Khan
AI Enthusiast & Serial Entrepreneur · Applied AI, LLMs & edge computer vision
City of London, United Kingdom
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Abdul Aleem Khan is a UK-based software engineer and technical co-founder who has been building production AI systems since 2018. His work ranges from computer vision running on edge devices in retail stores to real-time news pipelines that detect and rewrite biased and toxic content. As co-founder and CTO of two AI startups, he built and led engineering and AI teams, designed microservice and edge architectures, and created proprietary datasets where public ones did not exist. Products he built have been recognised by national and Asia-Pacific technology awards, and he was selected for Google for Startups' first AI Academy APAC. He speaks about practical, production-grade AI: what works, what breaks, and how to keep humans in the loop.
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Detecting 70 Kinds of Bias with a Model You Own: Replacing LLM API Calls in a Real-Time News Feed
Prompting a frontier LLM to flag bias in news works well in a demo. In production, with articles arriving continuously from publishers and social media, the cost, latency and inconsistency soon become a problem. This talk describes how we built a system that classifies around 70 types of bias (from framing and omission to loaded language and ideological slant) and rewrites stories to be balanced. We began with LLM prompts, used their outputs plus human review to build a large, politically balanced dataset of news stories, and trained an in-house model that replaced most of the API calls.
You'll see how we designed the taxonomy, how we kept the dataset balanced, where prompting still beats the small model, and the human-in-the-loop checks that stop "debiasing" from turning into censorship. I'll also cover how we measured cost, latency and agreement with human reviewers before and after the switch.
Key takeaways:
1. A practical path from prompt prototype to labelled dataset to an owned model
2. Designing and balancing a multi-label bias taxonomy
3. Where to keep humans in the loop and how to measure fairness
Beyond the Ban Hammer: Designing a Trust and Reputation System for Automated Moderation
Most moderation systems make one decision about one post: remove it or keep it. That treats a first-time heated reply the same as a coordinated harassment campaign, and it misses the subtle forms of toxicity (dehumanising language, moral manipulation, dog-whistles) that keyword filters and generic toxicity APIs miss. This talk walks through a moderation system that detects around 100 forms of hateful and divisive rhetoric and combines them with a dynamic trust-and-reputation score for each participant. Interventions then match a user's behaviour over time: gentle nudges, reduced reach, or human review.
I'll cover the label taxonomy, how classifier outputs feed the reputation model, how scores recover so people aren't punished forever, how to defend against gaming, and where a human reviewer stays in the loop. You'll leave with an architecture you can adapt for any community platform.
Key takeaways:
1. Why per-post moderation fails and what a time-decayed, per-user reputation model adds
2. Designing a toxicity taxonomy beyond "offensive / not offensive"
3. Human-review gates and anti-gaming measures that hold up in production
Send Metadata, Not Video: Running Computer Vision on Existing CCTV with Poor Connectivity
Small and mid-sized retailers already own many CCTV cameras, but streaming that video to the cloud for AI analysis is too expensive. In many markets, the internet connection simply can't carry it. This talk shares how we built a computer-vision analytics system for physical stores that runs people counting, age and gender estimation, queue monitoring and heatmaps on NVIDIA edge devices on site, and sends only anonymised metadata to the cloud.
I'll cover the pipeline architecture (DeepStream modules, ONNX conversion, and what runs at the edge versus in the cloud). I'll also explain how we built our own training data when public datasets didn't match the stores' environments, how we kept models updated across distributed devices, and the privacy benefit of never moving video off site, including the mistakes we made along the way.
Key takeaways:
1. A reference edge/cloud split for multi-camera computer vision
2. Building and annotating a domain-specific dataset on a startup budget
3. Operating and updating models on a fleet of edge devices
AI as Your Team, Not Your Tool: An Engineering Workflow for Shipping Production Software with Agents
Most developers use AI like a smarter autocomplete. I've been using AI agents differently: as members of a small product team, each with a defined role (planning, implementation, review, testing, documentation) and clear hand-offs and quality gates between them. Working this way, I've shipped production mobile apps and backend platforms with a fraction of a traditional team.
This talk is a practitioner's walkthrough of that workflow, not a vendor demo. It covers how to break work down so agents can own it, the review and test gates that keep quality high, and where agents reliably fail: architecture decisions, ambiguous requirements and security. That is why a human stays accountable for every merge. I'll share real examples, including what went wrong, and give a framework for deciding which roles to hand to AI and which to keep.
Key takeaways:
1. A role-based structure for AI-assisted development with clear hand-offs
2. Quality gates (tests, reviews, specs) that make agent output safe to ship
3. A checklist for which work to delegate and which a human must own
Build & Launch Your App with AI (2-hour workshop)
Got an app idea but no technical co-founder? AI tools now let one determined person build and ship a real product, but knowing how to use them well is the difference between a demo and a launch.
In this hands-on workshop I take attendees through the full journey live: shaping an idea into something buildable and testable in a week, building a working app with AI coding agents (and spotting where AI gets it wrong), adding the essentials (auth, payments, data and privacy), launching on the App Store and Google Play without the usual rejections, and getting the first users. It ends with a live idea clinic.
Suitable for aspiring founders, product people and developers of any level. Attendees leave with an MVP brief template, an AI prompt pack and a pre-launch checklist.
Abdul Aleem Khan
AI Enthusiast & Serial Entrepreneur · Applied AI, LLMs & edge computer vision
City of London, United Kingdom
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