HYFT Builds Breakout Momentum Higher

“ReefIQ™, MindWalk's biological context layer for life sciences, supplies what a horizontal ontology cannot: a governed, model-agnostic representation of what biology does, available from the first day of a deployment rather than modeled in over years, enriching a pharmaceutical company's drug discovery data as it lands, and compounding its intellectual property with every program run on it. For investors, the distinction matters because the context layer, not the model, is where durable platform value accrues, and it is the layer MindWalk sells.”

"The enterprise has discovered that an agent without an ontology guesses, and a guessing agent is a liability. That is exactly right, and it is exactly half of pharma's problem," said Dr. Jennifer Bath, President and Chief Executive Officer of MindWalk. "An enterprise ontology tells an agent what a purchase order, a patient or a clinical site is. It cannot tell an agent what a protein does, which regions of an antibody will tolerate modification, or why a program that failed three years ago matters to the one running today. The competitive question is no longer which model or agent you run; every model becomes a commodity. The question is what the model or agent runs on, because that is where the value ends up. In the enterprise, that is an ontology of the business. In biology, it has to be a representation of biology itself, built from what evolution preserved. The client's own programs, including the ones that failed, have to keep compounding inside it. That is what we built, and we are not aware of anyone else delivering it as a governed layer any model or agent can reason over."

Four years into the generative AI cycle, the enterprise has learned that large language models are becoming interchangeable ¹ and that deploying them inside a real organization disappoints far more often than the demonstrations suggest. MIT's 2025 State of AI in Business study put the share of enterprise generative AI pilots delivering no measurable return at 95 percent and located the cause not in model quality but in systems that do not retain context or integrate into workflows, ² and Gartner predicts that through 2026, 60 percent of AI projects unsupported by AI-ready data will be abandoned. ³ Few environments carry a higher cost for a confident wrong answer than drug discovery.

The market's answer is a new layer between an organization's data and the models and agents that use it, called an ontology layer, a semantic layer or a context layer. Whatever the label, it defines the entities an organization cares about, how they relate, what actions can be taken on them, and who may take them. Leading enterprise data platforms have built this layer for business data, the accounts, orders, sites and products their agents act on, ⁴ and Gartner now predicts that by 2030 universal semantic layers will be treated as critical infrastructure alongside data platforms and cybersecurity. ⁵ The market no longer debates whether the layer matters. It debates who owns it, in each domain.

Every version of this layer describes the organization. The vendor supplies the machinery for objects, links, actions and governance, and the client models the content, deployment by deployment. That is the right design for an enterprise, where the entities are business entities. It is the wrong design for biology, where the meaning that matters is what a sequence or a structure does, and that meaning is not something a client can model in.

A protein sequence stored in an enterprise ontology is a string in a property. A structure is a file. Nothing in the layer knows that two sequences with no surface similarity perform the same function, or that a particular region of an antibody will not tolerate modification. That knowledge is not in the enterprise's data to be modeled; it is in biology, and it has to be brought to the data. Biomedical literature networks and vocabulary services annotate what is already known about a catalogued molecule; they cannot infer function for a sequence no one has characterized. Lab informatics systems register molecules and assays but do not encode function. Protein foundation models carry learned representations of function, but they are sold as models, not as a governed layer that any model or agent can reason over.

ReefIQ occupies the intersection none of these reach. HYFT® Technology encodes biology as function-aware pattern-objects spanning sequence and structural biology. Refined over 20 years of curation, they form a continuously evolving biological representation of 660 million patterns and 25 billion relationships. When a client's discovery data lands in ReefIQ it is enriched at ingestion against that representation, so sequences, structures, assays, omics and program history become governed, queryable biological context. LensAI™, and any model or agent a client chooses to run, reasons over that same context, embedded in the client's own workflow, governance and human review. The client's own intellectual property compounds there. Client data remains the client's, inside their governance. Every program a client runs enriches every prior program, because the biological representation refines continuously and that refinement reaches data already on the platform. Programs that failed become queryable context for the programs that follow, and historical and ongoing work sit in one frame. The value a client builds compounds in the HYFT representation layer, not in any individual model or agent that runs on top of it.

MindWalk is a Bio-Native AI company built on more than 40 years of biology heritage, founded in 1983. It occupies the data structure and orchestration layer of biological AI, in the one domain where a confident wrong answer costs the most. ReefIQ has been commercially available since June 2026, and LensAI is in contracted, recurring arrangements with life sciences clients today.

Because ReefIQ is model-agnostic and governed, it is complementary to the enterprise data estate pharma already owns. Horizontal ontology platforms, cloud data platforms and lab informatics systems hold operational and instrument data; ReefIQ supplies the biological context those systems cannot generate. MindWalk expects the two layers to be deployed together as pharma moves agentic AI from pilots into discovery workflows.

In pharma, the work of building this layer is usually procured under an unglamorous name: data management. Unifying sequence, structure, assay and program data into a governed, AI-ready foundation is the first phase of every context-layer deployment, and it is the same work the enterprise now recognizes as building the ontology layer. It is also where the position is won. Once a client's discovery data is enriched and governed in ReefIQ, each subsequent program builds on that context instead of recreating it, and the models and agents running on it can be changed without disturbing it. A ReefIQ deployment is therefore structured differently from the fee-for-service projects and seat-based subscriptions that have made up most of MindWalk's historical revenue. Licensing is metered on the data a client holds in the platform and the compute it runs against that data, so revenue scales with discovery activity rather than headcount. The context a client builds there cannot be exported to another vendor without starting over, which is why these relationships are multi-year by design. A deployment typically begins with data management and widens into analytical tools and custom models running on the same context. ReefIQ agreements should not be benchmarked against MindWalk's historical LensAI agreements for any of these reasons.”