Mito AI is the scientific decision layer for asset strategy — turning fast-moving clinical evidence and expert discourse into governed, decision-grade outputs for the teams steering oncology innovation and care.
The bottleneck is no longer access to data. It is turning fragmented, noisy evidence into trusted, decision-ready outputs at speed.
Helyx maps clinical reality in real time and runs decision-grade analytics. DiffuseScan optimises the full option space. Together, they give development leaders the confidence to act.
Custom generative models and deterministic biophysics map the full decision space for asset design — ruling out unviable configurations and finding the balance between efficacy and toxicity before capital is committed.
A unified oncology ontology, integrating inference-driven and deterministic reasoning with decades of domain logic. A fully visible corpus where every output is traceable to source.
Systems of record manage workflows. BI shows metrics. Databases store facts. Mito AI delivers the scientific decision layer for asset strategy.
Six structural problems in oncology decision-making that general-purpose AI cannot address — and how Mito AI answers each one.
The clinical inference architecture behind Mito AI rests on a proprietary knowledge mesh built for oncology — connecting entities, settings and evidence that general-purpose AI cannot resolve.
Genes, biomarkers, MoAs, tumour settings, lines of therapy, trial phases, endpoints and drugs — normalised across every source so each analysis starts from a shared clinical lexicon.
Entity-based retrieval combined with semantic search and relevance ranking — the precision and recall that keyword search and general LLMs cannot match.
Proprietary relevance logic that evaluates evidence against the specific decision context — setting, population, line of therapy and biomarker.
Every claim traceable to its source — built for outputs that face R&D, regulatory, HCP or deal-committee scrutiny.
We build at the intersection of generative, agentic and deterministic reasoning — and we are deliberate about where each belongs. Models accelerate synthesis and orchestrate the work; deterministic rules and decades of domain logic hold them to clinical and biological reality.
We don't believe AI should replace scientific expertise in drug development. The complexity of oncology and the stakes of each decision demand a human who understands the science and owns the process as much as the outcome. With a fully visible corpus and every output traceable to source, the expert stays in control throughout.
That means striking the right balance between control and delegation at every layer of the platform. We'd love to share how we think about it.
Curious how we do it? Let's talk →Mutual discovery, early user feedback and a platform "tune-up" before roll-out — so Mito AI fits your workflows, not the other way round.
Mito AI combines biopharma domain expertise, product design, data engineering and a pragmatic understanding of how pharma teams make decisions.
Request a Helyx pilot, sign up or partner on DiffuseScan, or simply ask how Mito AI fits your development decisions.