Build mission-critical AI that does real work
Parsed is an Al interpretability lab focused on supercharging model performance and robustness through the lens of evaluations and mechanistic interpretability.
The one-line API replacement for state of the art task-specific performance, complete interpretability, and continuous optimisation.
The one-line API replacement for state of the art task-specific performance, complete interpretability, and continuous optimisation.
The one-line API replacement for state of the art task-specific performance, complete interpretability, and continuous optimisation.
Optimize models with bespoke and precise customization, delivering state-of-the-art LLM performance for real-world clinical workflows.
01
Optimize models with bespoke and precise customization, delivering state-of-the-art LLM performance for real-world clinical workflows.
01
Optimize models with bespoke and precise customization, delivering state-of-the-art LLM performance for real-world clinical workflows.
01
Decode LLMs with mechanistic interpretability, discovering the underlying circuits, computations and activations driving model decisions. Gain transparent insights into model reasoning, enabling trust and clear attribution of inputs to clinical outputs.
02
Decode LLMs with mechanistic interpretability, discovering the underlying circuits, computations and activations driving model decisions. Gain transparent insights into model reasoning, enabling trust and clear attribution of inputs to clinical outputs.
02
Decode LLMs with mechanistic interpretability, discovering the underlying circuits, computations and activations driving model decisions. Gain transparent insights into model reasoning, enabling trust and clear attribution of inputs to clinical outputs.
02
Continuously refine and fortify deployed models by productising frontier LLM research to guarantee ongoing state-of-the-art performance, enterprise-grade reliability and minimal latency.
03
Continuously refine and fortify deployed models by productising frontier LLM research to guarantee ongoing state-of-the-art performance, enterprise-grade reliability and minimal latency.
03
Continuously refine and fortify deployed models by productising frontier LLM research to guarantee ongoing state-of-the-art performance, enterprise-grade reliability and minimal latency.
03
Optimize models with bespoke and precise customization, delivering state-of-the-art LLM performance for real-world clinical workflows.
04
Optimize models with bespoke and precise customization, delivering state-of-the-art LLM performance for real-world clinical workflows.
04
Optimize models with bespoke and precise customization, delivering state-of-the-art LLM performance for real-world clinical workflows.
04
Decode LLMs with mechanistic interpretability, discovering the underlying circuits, computations and activations driving model decisions. Gain transparent insights into model reasoning, enabling trust and clear attribution of inputs to clinical outputs.
05
Decode LLMs with mechanistic interpretability, discovering the underlying circuits, computations and activations driving model decisions. Gain transparent insights into model reasoning, enabling trust and clear attribution of inputs to clinical outputs.
05
Decode LLMs with mechanistic interpretability, discovering the underlying circuits, computations and activations driving model decisions. Gain transparent insights into model reasoning, enabling trust and clear attribution of inputs to clinical outputs.
05
We're backed by the best
Backed by LocalGlobe and a standout group of angel investors, including the co-founder and CSO of Hugging Face, a former director at DeepMind, a current director at Meta AI Research, the head of startups at OpenAI, and the former chair of the NHS.
Clinician led with technical interpretability roots.
CEO
Mudith Jayasekara
Medical doctor. Engineering PhD candidate (Oxford). Rhodes Scholar. Ex-elite pole vaulter for Australia.
CTO
Charles O'Neill
Mech interp researcher (MATS, Stanford, Johns Hopkins). Previously ML engineer (NASA, Macuject, quant trading). CS PhD candidate (Oxford).
CSO
Max Kirkby
Rhodes Scholar. PhD candidate in computational neuroscience (Oxford) studying reasoning in natural intelligence.
Clinician led with technical interpretability roots.
CEO
Mudith Jayasekara
Medical doctor. Engineering PhD candidate (Oxford). Rhodes Scholar. Ex-elite pole vaulter for Australia.
CTO
Charles O'Neill
Mech interp researcher (MATS, Stanford, Johns Hopkins). Previously ML engineer (NASA, Macuject, quant trading). CS PhD candidate (Oxford).
CSO
Max Kirkby
Rhodes Scholar. PhD candidate in computational neuroscience (Oxford) studying reasoning in natural intelligence.
Careers
We believe that Parsed is the most scalable way to actually improve patient lives. It applies horizontally across healthcare, is at the frontier of AI research, and has immediate impact for our customers.
Deep dual expertise in both medicine and interpretability is essential for this mission and is the DNA of our founding team. We’re growing a lean, all-star team.
Build clinically impactful AI
Want to outperform frontier closed source models for your task? Want complete interpretability for every output? Want zero-effort, ongoing, model improvement?
Build clinically impactful AI
Want to outperform frontier closed source models for your task? Want complete interpretability for every output? Want zero-effort, ongoing, model improvement?