About

Laconic

Laconic means to the point.

Laconic Ltd. is a single-person AI advisory and consultancy company founded by Oguzhan Gencoglu in 2022. It is located in Helsinki, Finland and operates globally. Our clients vary from early stage start-ups to large corporations.

Me

Hi! I am Oguzhan (Ouz) Gencoglu, a Machine Learning and Decision Science expert. I consider myself cheerful, factual, and curious.

I take a holistic view to Machine Learning:

Science & Tech

Deep tech expertise | 15 years in Machine Learning & AI
Profound understanding of scalable AI development and productionizing | delivered 60+ AI solutions ranging from Computer Vision to Natural Language Processing in various industries
Several peer-reviewed scientific publications on machine learning

Business & Strategy

Previously co-founded Top Data Science in 2016 and served as Head of AI. Grew the business and the team, steered the company strategy as a board member which resulted in an acquisition by Morpho Inc.^⧉ from Japan (listed in Tokyo stock exchange) in 2018
Nowadays building an evaluation & observability platform for GenAI applications at Root Signals AI^⧉
Serving as an external advisor or board member in different companies

My Business Profile

AI Expert - Tech | Strategy | Leadership

Leadership, Mentoring & Community

Mentor and supervisor to my teams
Peer reviewer to scientific publications, contribution to open source @ GitHub^⧉
Avid speaker with appearances @ MLOps Community^⧉, CVPR^⧉, PyData^⧉, Finnish Center for AI^⧉, MyData^⧉. Given ~100 talks, workshops, and trainings on machine learning. Some of my podcast appearances at Spotify:

Policymaking

I advise OECD on AI policymaking

OECD AI

Oguzhan Gencoglu

Expert Group on AI Compute

Articles

My articles aim to serve as live documents that are updated whenever necessary rather then static runestones. I try to keep my articles to-the-point by spending significant time to make them shorter. Quality is prioritized over quantity. As I am willing to update my analyses or views when presented with new data, I would appreciate corrections, suggestions, or general comments.

Each article has a topical tag (e.g. AI^⧉). This is how external links^⧉, tooltips (hover mouse or click)^◥, and citations^{[1, 2]} look like in the articles.

Upcoming Talks

Evaluating GenAI Applications in Healthcare

Abstract: Reliability, accuracy, observability, and auditability are crucial in building LLM workflows in healthcare. All of these rely on the ability to measure LLM automations at scale. But as the metrics we care about in GenAI applications (e.g. hallucinations, adherence to a policy, etc.) are complex, traditional machine learning or NLP metrics are not relevant anymore. These measurements can only be conducted by other LLMs that are tuned specifically for judging, i.e., LLM‑Judges. As the evaluators are LLMs themselves in this paradigm, they also need to be observed, measured, and tuned to prevent drifts from expected behaviour. This talk will delve into LLM‑Judges in the context of healthcare LLM workflows and agents.
Date & Time: April 2, 15:45 ET
Location: Online
Event Link