We perform fixed-scope research, feasibility studies, and artifact production for grant committees, commercial R&D groups, and independent researchers. Ontology maps what we study; epistemic rules govern what we may claim; AI accelerates inside that map — human verification holds the gate.
Grant reviewers · industry research teams · PIs and small labs
Research infrastructure map · drag to rotate
Every engagement ships named deliverables with trust cues you can cite in proposals, board decks, or audit trails.
Scoped assessment of data, baselines, and whether the claim you want to support is reachable — with explicit limits.
Insert-ready methods, data-governance, and risk language for SBIR, NIH, NSF, or internal R&D review.
Structured results, human-readable report, and checksum — immutable experiment ID; prior runs never overwritten.
Proposal sections, supplementary artifacts, and claim ceilings aligned to funder integrity expectations.
Partner-facing or public copy that passed explicit verification gates — AI may draft; the research group owns what ships.
We remove integrity risk: governed AI use, reproducible workflow, and deliverables that survive post-award audit. You see what was run, what was claimed, and what evidence supports it.
Fixed-scope feasibility and interpretable analysis without fire-sale IP. Your data stays bounded; our core methods stay separated. Auditable reports and methods packets for internal review.
Turn exploratory work into grant-ready structure — typed hypotheses, bounded claims, and artifacts that drop into methods sections without model hype.
In high-noise domains, models generate faster than judgment. We separate the map of study from the machinery of inference — so justified belief stays traceable.
Domains, hypotheses, experiments, and claims are named entities with explicit relations — not ad hoc folders or one-off prompts. Gaps and overreach show up before they reach clients or journals.
Every outward claim has a ceiling and a receipt path. Upgrades require versioned results — structured output, human report, checksum — not model confidence.
AI assists literature, drafting, and formalization. Public statements, filings, and partner deliverables pass human-verified gates aligned to your governance requirements.
Ontology is the map. Epistemic technology is the rulebook for justified belief on that map. AI is acceleration inside the map — not a substitute for verification.
One public benchmark example — metric, dataset, claim ceiling, and experiment ID. Not slide-deck superlatives.
Aligned with funder concerns about GenAI integrity, reproducibility, and proposal readiness.
Inference runs inside the epistemic map. The failure mode we refuse is narrative without artifacts.
ML outputs carry claim ceilings, baseline comparisons, and abstention — especially where cohorts are mixed or N is small.
Serious results ship as structured JSON, human report, and checksum — not slide-deck superlatives.
Tools accelerate formalization and literature triage; the research group verifies and owns what goes public.
Each domain keeps its own claim ceiling. The mother company tells the story; artifacts show the evidence.
Feasibility reports, methods packets, and grant-ready artifacts — without selling core IP or overclaiming science.