Built for Data Science and Engineering
The Oncology Data Layer Your Team
Would Build, Already Built
Your bottleneck is not compute. It is the quality and structure of the underlying data. Building oncology ontologies in-house is years of taxonomy work before the first model ships. Kognitic gives you production-grade, machine-ready evidence: consistent schemas, living taxonomies, continuous updates.
Built for VP, Senior Director, and Director of Data Science, Enterprise AI, Data Infrastructure, MLOps, and RAG and agent platforms.
Uptime
99.9%
Latency p99
47ms
Schema
stable
Taxonomy
living, v2026.06
Access modes
SDK · REST · Webhook · Bulk
Illustrative view. Programs, indications, and figures are representative.
Build It, or License It
The Model Is Only as Good as the Structure You Feed It
The decision on the table is whether to build the oncology data layer in-house or license a validated one. Building it means years of taxonomy work and a pipeline that breaks every time a source changes format.
Building the ontology in-house is years of taxonomy work before the first model ships
Your team is expert at modeling, not at normalizing messy clinical data sources.
Extraction pipelines break every time a source changes its format
Maintenance overhead compounds. Every upstream change is your team's problem.
Your RAG models inherit the noise and gaps of whatever you feed them
Garbage in, confident garbage out. Provenance gaps surface at the worst moment.
Why Data and Engineering Teams Choose Kognitic
Skip the Years of Taxonomy Work
Technical evaluation, not sales theater. Kognitic is the validated data layer your team would otherwise spend years building, documented and ready to integrate against.
Production-Grade Endpoints
Trial arms, patient cohorts, and efficacy outcomes mapped to a rigorous ontology. The messy landscape structured into an apples-to-apples standard.
Glass Box, Not Black Box
See the parameters, the look-alike studies, and the historical accuracy behind every projection. Defensible in a leadership review, not a magic button.
Ontology Depth Across Nine Dimensions
The normalization your team would spend years building. Audit-grade schemas across indication, line of therapy, biomarker, arms, endpoints, and more.
The Decision View
Show Me the Schema,
Not a Screenshot
A normalized trial record, structured and provenance-traced. This is what your models
integrate against, not a decision view built for a meeting.
{
"trial_id": "NCT04812249", // fictional identifier
"indication": "NSCLC",
"line_of_therapy": "1L",
"biomarker": "PD-L1 >= 50%",
"arms": [
{ "role": "experimental", "regimen_class": "IO + chemo" },
{ "role": "comparator", "regimen_class": "chemo" }
],
"endpoints": {
"mPFS_months": 10.4,
"ORR_pct": 61
}, // illustrative
"ontology_version": "2026.06",
"provenance": "source-traced",
"confidence": 0.92
}
Illustrative response. Identifier is fictional and figures are representative.
The Legacy Playbook
The Same Evidence
Delivered as a Layer
Data and Engineering leads with the API. The same normalized evidence powers Core and Outcomes for the teams building on top of it.
Data Access Layer
Production-grade endpoints, consistent schemas, living taxonomies. Machine-ready oncology evidence for your models and agents.
Explore the API →Clinical Trial Intelligence
The turnkey competitive layer, built on the same normalized evidence your API delivers.
Explore Core →Clinical Evidence Intelligence
Comparative benchmarking on the same backbone. Proof the layer holds up in production.
Explore Outcomes →The Kognitic Difference
Build It Yourself, or Feed It Raw.
Both Cost You Years or Accuracy.
Sponsor Materials
Years Before the First Model
Standing up oncology ontologies and maintaining a living taxonomy is a multi-year detour from your actual product.
Legacy Databases
Volume, No Structure
Unnormalized dumps that break your pipeline and inherit every gap. The structuring work still lands on your team.
Then there's Kognitic
The Layer, Already Built
Production-grade endpoints. Audit-grade schemas. Nine ontology dimensions. Machine-ready for RAG and agents, continuously updated.
Skip the Detour
Integrate Against the Layer,
Not the Noise
Review the schemas, test a sample endpoint, and see how the layer fits your stack. Request API access.