From a sentence, to experiments nobody’s run.
Type a scientific goal. Nine AI agents work through it with you — mapping what science knows, finding where it falls short of what your goal needs, and laying out fully-specified lab experiments that close the gap.
Designing experiments by intuition is expensive — and most of that cost teaches us nothing.
Most hypotheses don’t pan out — and a negative result usually leaves no clear signal about what to try next.
It’s not a shortage of ideas. It’s the wrong experiments early — before evidence can falsify the bad ones.
Every experiment has to count. Yet teams pick by intuition, not by which experiment maximally discriminates between competing hypotheses.
Most teams design experiments from what they already know. Omega-Point — an AI pipeline of nine reasoning agents — starts from what your goal requires, maps it against current scientific knowledge, then designs experiments aimed precisely at the unknown.
The pipeline mirrors the four steps above. Each phase’s output becomes the next phase’s context. The full chain is what makes each experiment non-obvious.
From “reverse aging in the human brain” to a dynamical-systems experiment asking whether aging itself is an attractor. Each level produced by a different agent. The leaf is only conceivable because of every level above it.
A chatbot rearranges what its training data already contains. Omega-Point derives experiments from your goal — not from text it has read. Four concrete differences.
Every Omega-Point Frontier Question is required to be unanswerable by literature search. If a review article already settles it, the pipeline rejects the question and generates a new one. The whole system targets the gap between what your goal demands and what published science currently delivers — that gap is where new science actually happens.
Every Omega-Point experiment carries a 9-link reasoning chain: Goal → Pillar → Requirement → Domain → Science → Frontier Question → Hypothesis → Tactic → Protocol. Remove any one link and the experiment collapses into something a postdoc could pull from a review. ChatGPT skips straight to the protocol — you get the “what” with none of the “because.”
Without adversarial design, an LLM proposes the experiments that match the dominant view — the ones that confirm what you already believe. Omega-Point structurally prevents this: every hypothesis set must include one heretical position and one cross-domain transfer, and at least half of all tactical questions must pit competing hypotheses head-to-head with a single distinguishing measurement.
Ask a chatbot for 100 experiments and #50 onward is paraphrase — the same handful of ideas with reagent names swapped. Omega-Point produces hundreds of structurally distinct experiments per goal because each one requires the full 9-link chain to conceive. Every leaf ships with complete S·I·M·T (System · Intervention · Meter · Threshold/Time) and is auto-ranked by ambition and feasibility — you keep the top picks, discard the rest.
A growing market of AI tools is being built for scientists. Omega-Point sits in the focused middle — sharper than search, more concrete than a general "AI scientist".
Search and synthesize the published literature. Strong at surfacing what’s already known — weak at designing new experiments end-to-end.
Structured falsification logic, MECE goal decomposition, hypothesis scoring, and protocol-oriented outputs you can hand straight to a bench.
Broad scientific assistance — the market signal is clear, but these systems aren’t optimised for the specific workflow of biotech R&D.
The reasoning chain is the same for everyone — what changes is which leaf of the output matters most to you. Four roles where Omega-Point earns its keep:
Designing the next round of experiments where “what the lab can actually do” meets “what would move the field.” Omega-Point returns hundreds of fully-specified, ranked candidate experiments — including ones a textbook wouldn’t suggest, and the rationale chain for each.
Hypothesis generation that doesn’t just repeat the literature. Frontier Questions must be unanswerable from training data — that’s where novel IP lives. Every hypothesis set is forced to include a heretical position and a cross-domain transfer.
Stress-test a scientific thesis before raising, committing, or signing off. The 9-level decomposition exposes exactly where a thesis is original versus derivative, and surfaces the foundational assumption that no one’s actually tested.
Argument-grade structure for a proposal. The chain — Goal → Failure Mode → Requirement → Domain → Pillar → Gap → FQ → Hypothesis → Tactical Question → Experiment — is already the spine of a defensible Aims page.
A 12-month plan with concrete milestones. Each phase is shaped by feedback from the previous one.
The questions a careful researcher or R&D lead wants answered before booking the call.
No. A plain LLM surfaces what is in its training data — Omega-Point’s Frontier Questions are constrained to be unanswerable from any literature an LLM has read. Every hypothesis set is forced to include a heretical position and a cross-domain transfer, and at least half of tactical questions must pit competing hypotheses against each other. The full breakdown is in section 04.
Your goal and the resulting experiments sit in your private workspace. Goals are not used to train any model. Sessions are stored with full JSON export, so you can pull everything out at any time. For sensitive R&D, Omega-Point runs in Docker — we can deploy it inside your environment so nothing leaves your network.
No. It’s a creativity multiplier. Omega-Point generates the experiment space — ranked, fully specified, and auditable. Choosing which experiment to run, adapting it to your equipment, and interpreting results stays with you. Every protocol carries its full 9-level reasoning chain, so you can sanity-check the logic before spending a dollar at the bench.
Any domain where mechanism matters. The architecture is fully domain-agnostic — nothing in the nine reasoning agents is hard-coded to a specific field. We’ve verified the pipeline end-to-end across pilots as different as brain aging, plant photosynthesis, and pathogen resistance — the same machinery produced fully-specified experiments in each. The best fit is hypothesis-rich problems in the life sciences, chemistry, materials, and adjacent fields. If you can write your goal as a sentence with measurable success criteria, the pipeline runs on it.
Every leaf experiment carries an explicit S·I·M·T spec: named System (cell line or organism, with source), named Intervention (compound, dose, schedule, controls), named Meter (assay, instrument, protocol) and a quantitative Threshold with statistical power. Each is scored on ambition and feasibility — avg 7.3 / 10 across all verified runs. The 9-level reasoning chain is auditable end-to-end.
We onboard testers personally. A short call to understand your research focus, then guided access to run your goal through the pipeline. Pricing is custom — per-lab arrangements for academic groups, project-based and on-prem deployment for pharma and biotech R&D teams. Start with the contact buttons in section 09.
We onboard testers personally. Drop us a line and we’ll set up a short call to walk you through your first run.