Multi-step scientific reasoning, formula discovery, drug interaction analysis, and research verification — with a complete proof chain on every result. Built for the environments where confident-but-wrong is the failure mode LLMs are hardwired for.
LLMs are extraordinarily good at producing text that sounds like scientific reasoning. They are structurally incapable of producing scientific reasoning. In drug discovery, clinical research, and regulatory submission, the difference between a fluent wrong answer and a correct one is not a style question — it is a patient safety question.
A confident fabrication about a study that doesn't exist, a mechanism that hasn't been demonstrated, or an interaction that hasn't been characterized is not a minor error in pharma research. It is a reproducibility failure, a regulatory risk, and potentially a patient safety event. Helixor cites what exists and refuses to produce what doesn't.
21 CFR Part 11 and ICH GCP require that electronic records and their underlying reasoning be auditable and reproducible. Post-hoc explanations generated by a language model do not satisfy this requirement. Helixor's structured proof chain is produced by the same execution that generated the result — it is the actual reasoning, not a description of it.
Sending proprietary compound structures, unpublished trial data, or genomic sequences to a cloud inference endpoint for every query is not a data governance issue to be managed with a BAA. It is a competitive and regulatory exposure that compounds with every query. Helixor runs on-premise, processes data locally, and makes no external calls.
The mechanism that explains a crop yield response may also explain a cellular receptor interaction. The statistical pattern in orbital mechanics may describe protein folding behavior. Helixor's cross-domain reasoning architecture finds these structural similarities — transferring verified knowledge patterns across domains rather than searching within them.
Evolutionary search through mathematical transformation space — discovering relationships between variables that aren't apparent from first principles. Every discovered formula is verified before it is returned. No candidate that cannot be proven is presented as a result.
Multi-step reasoning across pharmacological constraint chains — identifying interaction risks, contraindication patterns, and mechanism overlaps with a structured proof chain on every conclusion. Every output is traceable to its source evidence, not generated from statistical co-occurrence.
Inclusion/exclusion criteria, endpoint selection, site allocation, and statistical power requirements — optimized simultaneously as a unified constraint problem. Replanned in real time as enrollment data and interim results arrive, with full constraint enforcement on every protocol modification.
Verified mathematical relationships from physics, ecology, or materials science transferred to biological, pharmacological, or genomic contexts — where the underlying structure is similar even when the domain vocabulary is entirely different. Every transfer is validated before it becomes a hypothesis.
Every analytical decision, model output, and reasoning step structured and logged with the provenance required for FDA submission and ICH GCP compliance. The audit trail is an output of the process — not a documentation task performed after it.
Internal research documents, published literature, and proprietary experimental data — indexed and queried with source attribution and confidence scoring. Every answer includes the provenance of its evidence. "Unknown" when evidence is insufficient. Never a fabrication.
Helixor continuously evaluates incoming research queries, incoming data signals, and hypothesis candidates — returning verified results or explicit failures, with full reasoning chains. No fabricated citations. No hallucinated mechanisms.
Active conversations across pharma R&D, clinical operations, and life sciences research. Tell us where the wrong answer is the costliest.