Pharma · R&D Excellence

Decision Ontology for R&D Excellence

Every R&D phase, reading from the same evidence.

Purpose-built to understand regulatory and scientific nuance, not generic analytics.

Unified Context Causal Self-Learning

One Enterprise Brain

One brain, not six systems.

An ELN, a CTMS, a safety system, and a RIM tool can each be excellent on their own, and still leave discovery, clinical, and regulatory teams re-deriving the same evidence in each one. The ontology is what replaces "six systems, six versions of the evidence" with one.

Point Solutions Today

Each system holds its own version of the evidence, tries to hand off to the next, and drops it at the edge.

vs

One Ontology

Same six layers, one shared state. Every phase, and every function, reads and writes the same record.

Same decision, two ways.

Three failure modes point solutions can't fix, and how a connected ontology fixes each one.

Causal Reasoning

Why did enrollment stall at three sites?

Point solution

The CTMS shows a lower enrollment number. Site history, protocol amendments, and competitive trial activity sit in different systems, so nobody can trace why.

Connected ontology

The ontology traces it: a protocol amendment (Trial & Site) tightened eligibility right as a competing trial (Competitive & IP) opened at the same sites. Full causal chain, not a status flag.

Grounded Applications

A go/no-go that wasn't complete

Point solution

A go/no-go dashboard pulls only from the safety database, blind to biomarker stratification or CMC readiness.

Connected ontology

Built on the ontology, the same dashboard inherits safety, biomarker, and CMC state automatically. The recommendation reflects the whole program, not one system's view of it.

Surfaced Blind Spots

A signal nobody connected

Point solution

A safety signal in one indication and a similar finding in a related compound's literature never get compared. Tox data and published evidence live apart.

Connected ontology

The ontology surfaces the link automatically, flagging that two independent signals share a mechanism. A blind spot becomes an early warning.

The Ontology

All R&D Relationships & Causality, Mapped.

Six functional layers, the full ontology stack behind every R&D decision, not a subset.

How It's Built

Built by agents, not a project.

The ontology isn't a one-time modeling project. Three kinds of agents build, maintain, and act on it continuously.

Always on, not a one-time build

1

Data Agents

Ingest & reconcile

Continuously ingest and reconcile data regardless of shape, LIMS tables and EDC exports as easily as tox study PDFs, adverse-event narratives, congress abstracts, and lab notebook entries. They resolve mismatched compound IDs, inconsistent site records, and unstructured evidence without a manual mapping project.

2

Ontology Agents

Model & maintain

Take what Data Agents reconcile and build the six layers themselves, inferring relationships, resolving the same target or asset across systems, and flagging drift as trial data and regulatory status change, so the ontology stays current without a re-modeling cycle.

3

Decision & Application Agents

Act & orchestrate

Orchestrate the systems that act on the ontology, a trial-design check, a submission-readiness alert, a safety-signal review. They can be schedule-triggered, event-triggered, or rule-triggered, and every trigger checks live ontology state first, so no action fires against stale or partial evidence.

Structured & unstructured evidence Data Agents reconcile Ontology Agents model Six living layers Decision & Application Agents orchestrate

Data Foundation

Any format. Any mess.

Format comes first: tables or free text, clean or messy, Data Agents handle it. The systems below are just examples, not a checklist.

Structured

Tables & feeds

Tables, feeds, and structured records.

Discovery databases UniProt / ChEMBL Structured abstract metadata LIMS results tables FAERS records Genomic sequencing outputs CTMS / EDC tables Process LIMS data eCTD metadata Claims / EHR datasets Trial registry records Patent filings

Unstructured

Documents & free text

Documents, notes, scans, and free text.

Internal research notebooks Preprint PDFs Full-text PDFs Congress posters KOL call notes Tox study report PDFs Adverse-event narratives Protocol appendices Lab assay reports Site monitoring visit reports Protocol amendment redlines Electronic lab notebook entries Analytical method PDFs Health authority letters Submission cover letters Legal opinions Conference abstracts Investor call transcripts

The Four Phases

Different phases. Same six layers.

Discovery, Preclinical, Clinical Trials, and Regulatory each draw a different combination from the same ontology, no phase re-derives evidence the one before it already established.

Decision 01

Discovery

Target prioritization grounded in biological evidence, the published literature, and where competitors already are, before resources commit to a program.

Reads from

Target & Indication Ontology

Safety & Tox Ontology

Competitive & IP Ontology

Decision 02

Preclinical

Go/no-go calls that connect toxicology signal to biomarker data and prior regulatory feedback, instead of reviewing each in isolation.

Reads from

Safety & Tox Ontology

Regulatory & Submission Ontology

Decision 03

Clinical Trials

Protocol design and site selection checked against enrollment history, biomarker-driven stratification, and formulation readiness together.

Reads from

Trial & Site Ontology

Safety & Tox Ontology

CMC & Process Ontology

Regulatory & Submission Ontology

Competitive & IP Ontology

Decision 04

Regulatory

Submission packages built with formulation data already attached, and checked against what has triggered delay in past filings.

Reads from

Regulatory & Submission Ontology

CMC & Process Ontology

Agent Library

Decision & Application Agents, purpose-built for R&D Excellence.

Pre-built agents that read directly from the six-layer ontology, ready to deploy, not built from scratch.

01Decision Agents

Reasoning primitives, reusable across every workflow.

02Application Agents

Purpose-built R&D workflows.

Architecture

From evidence to decision.

One flow, four tiers, agents run the first two so the ontology stays live without a manual pipeline.

Structured + Unstructured

ELN / LIMS tables

CTMS / EDC records

Tox & PV narratives

Literature & RWE

Data + Ontology Agents

Reconcile & resolve

Model six layers

Keep evidence current

Flag drift

Phase

Discovery

Preclinical

Clinical Trials

Regulatory

Outcomes

Target prioritization

Go / no-go

Protocol design

Submission readiness

Governance & Trust

Built for GxP evidence.

Every layer, every agent action, and every recommendation is traceable, access-governed, and explainable by default.

01

Full data lineage

Every value traces back to its source system and timestamp, auditable end to end.

02

Role-based access

Discovery, clinical, and regulatory teams see the layers relevant to them, governed at the ontology level.

03

21 CFR Part 11 aligned

Electronic records and signatures handled to the standard regulatory submissions require.

04

Explainable outputs

Every recommendation, and every Data Agent or Ontology Agent action, shows which layers and records it drew from.

Decision Ontology for R&D Excellence

See your R&D data here.

We'll walk through your current systems and show where the six layers slot in.

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