On AI Benchmarking. Q&A with Archie Chaudhury and Ramesh Chitor
Moderated by Ramesh Chitor.
Q1. Most AI benchmarks today come from model creators themselves or vendor-controlled evaluations. How is LayerLens addressing the critical need for independent AI benchmarking, and what specific challenges have you encountered in building a truly neutral evaluation platform that the industry can trust?
LayerLen’s core mission is to provide an independent, transparent platform for AI benchmarking and evaluation. We independently run every single benchmark that you see on Atlas, our public application. Furthermore, we show the full traces, or every single prompt, within all our benchmarks for transparency. The main challenge here was building the architecture: we had to build our own registry and evaluation framework, entirely from scratch, to allow for on-demand evaluations.
Q2. Traditional benchmarks like MMLU and HellaSwag were designed for academic research, but enterprises need AI models that work reliably in production environments. How does the LayerLens Atlas platform bridge this gap between academic benchmarks and real-world enterprise AI validation needs?
Here, the focus shifts to practical utility. We allow for initial vendor and model selection using benchmarking, and then practical evaluations, which can either be created by the organization or done on demand from an organization’s internal documents through our enterprise platform.
Q3. The AI industry is moving beyond static benchmarks that can become outdated within months. What is LayerLens’s vision for the future of AI evaluation, and how are you addressing challenges like data contamination and benchmark saturation that are plaguing traditional evaluation methods?
We feel that the rate of creation for new benchmarks should catch up to the proliferation of new models. At LayerLens, we are actively exploring ways in which we can create practical benchmarks, benchmarks that reflect how you and I use AI models, at scale and at speed.
Q4. The AI benchmarking market is projected to grow from $1.43 billion in 2024 to $12.61 billion by 2033. As a startup competing against established players, how is LayerLens positioning itself to capture market share, and what role do you see independent evaluation playing in this expanding ecosystem?
AI benchmarking is still a relatively new problem; I would actually argue that there is not one key established player in the market. There are definitely different strategies that companies are pursuing, from crowdsourcing to partnering with established labs, but there has been one that has been proven to work. Our goal is to provide the most intuitive user experience possible, and ensure that we can become a source of truth when it comes to AI benchmarks + evals.
Q5. With increasing regulatory focus on AI transparency and the need for ‘explainable AI,’ how does LayerLens’s evaluation methodology contribute to building trust in AI systems? What role do you see independent evaluation playing in the broader AI governance and regulatory landscape?
We think our service can be used to validate the performance and usage of AI models, from vendor selection to even governance.
Evaluation is centric to use-cases such as safety, governance, and long-term alignment. I see independent evaluations becoming a key part of how organizations, think tanks, and other organizations determine how good their models are performing on different metrics.
Q6. What inspired you to found LayerLens.AI, and how do you see your platform changing the way enterprises evaluate generative AI models?
The origin story behind LayerLens is actually really interesting. I had been exploring ideas at the intersection of AI, distributed systems, and verification, when I managed to reconnect with my current co-founders, Jesus Rodriguez, who has had decades of experience in computer science and technology, and Ram Shanmugam, who has built multiple software and distributed systems businesses. Jesus had actually been incubating several companies in generative AI, and came to me with the idea of creating a startup focused on independent evaluations for frontier AI models.
Q7. How does LayerLens ensure transparency and trust in AI model evaluations, especially compared to traditional or “vibe-based” benchmarks?
We showcase the full traces of every single input and output for each evaluation. An evaluation is, at its core, a set of questions and answers that a generative AI model is tested against. While most platforms just show the final score, we show every single input/output for full transparency.
Q8. Can you walk us through how an end user or enterprise would use LayerLens to benchmark or validate their own AI applications before deployment?
As long as the user is using a generative AI model, they can connect it to our application and start running evaluations for it. They can create their own benchmarks, upload existing datasets, or use our registry of benchmarks for validation.
Q9. With the rapid evolution of generative AI, what are the biggest challenges you see in model evaluation today, and how is LayerLens addressing them?
The biggest challenge for us so far has been creating automated, on-demand ways to execute benchmarks in a no-code fashion, similar to how you would execute unit tests.
This has involved a lot of wrangling of environments and creating automated tools from scratch to ensure that this capability can run without interference.
Q10 Could you share a recent example where LayerLens helped an organization make a critical decision about AI adoption or model selection?
We recently helped an organization validate their selection of a specific type of Google model for their internal use. This fit exactly into the use-case we are trying to build for.
We see LayerLens as being a core pillar in the mission to validate the performance of AI applications and models.
Q11.What new features or capabilities are you most excited about for LayerLens in the coming year, and how do you see the field of AI evaluation evolving
The most exciting and important thing we are trying to develop is on-demand agentic and practical evaluations. It is probably one of the most pertinent problems in generative AI today, and it is something we are excited for.
Q12. Finally, what we can expect from Layerlens in terms of product roadmap over the next 6 months, and any other thoughts?
You can expect us to lean a lot into creating new practical benchmarks. We are exploring different ways in which this can work. You can think of practical benchmarks as benchmarks that more accurately represent how you and I use AI models on a day to day basis. Our goal is to translate this into real world-benchmarks that can be used to measure AI models at scale.
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Archie Chaudhury Co-founder LayerLens.
Archie Chaudhury is a technologist, entepreneur, and engineer. He previously worked on building Adamnite, a venture backed startup building an easier to use base layer blockchain, and served as an advisor to several companies building at the intersection of distributed systems and AI. His writing has been featured in Bitcoin Magazine and Nasdaq.
He is now working on LayerLens, a platform dedicated to benchmarking and evaluating AI applications and models.
Ramesh Chitor
Ramesh Chitor is a seasoned business leader with over 20 years of experience in the high-tech industry working for Mondee. Ramesh brings a wealth of expertise in strategic alliances, business development, and go-to-market strategies. His background includes senior roles at prominent companies such as IBM, Cisco, Western Digital, and Rubrik, where he served as Senior Director of Strategic Alliances. Ramesh is actively contributing as a Business Fellow for Perplexity.
Ramesh is a value and data-driven leader known for his ability to drive successful business outcomes by fostering strong relationships with clients, partners, and the broader ecosystem. His expertise in navigating complex partnerships and his forward-thinking approach to innovation will be invaluable assets to Perplexity’s growth and strategic direction.
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Sponsored by Chitor.