FOR INVESTORS
BioQuery™ lets researchers ask questions about cancer genomics in plain English and get publication-ready answers in seconds.
Cancer researchers waste 3-4 hours answering simple questions that should take seconds.
3-4 hours
30 seconds
15.2% CAGR
16.3% CAGR
14.5% CAGR
Sources: Mordor Intelligence, TowardsHealthcare, Grand View Research (2024)
Claude and GPT-4 can reliably parse natural language to structured queries
TCGA, GTEx, and other datasets are mature and accessible via BigQuery
Serverless compute makes this economically viable at scale
62% of healthcare executives have implemented GenAI use cases
| Feature | cBioPortal | GEPIA | BioQuery |
|---|---|---|---|
| Natural language queries | ✗ | ✗ | ✓ |
| Answer in < 30 seconds | ✗ | ◐ | ✓ |
| Publication-ready figures | ◐ | ◐ | ✓ |
| Editable exports (PDF/SVG) | ✗ | ✗ | ✓ |
| Auto-generated methods text | ✗ | ✗ | ✓ |
| Shareable permalinks | ✗ | ✗ | ✓ |
| Transparent SQL | ✗ | ✗ | ✓ |
No existing tool offers natural language → publication-ready output.
Two-phase parsing system combines LLM flexibility with deterministic execution. Natural language in, reproducible results out — no hallucinations.
Patent-pending approach
Seamless access to 11 major genomics databases representing 185,000+ patient samples. Smart routing selects optimal data sources automatically.
4x larger cohorts for specialized cancers
Every analysis produces a reproducible, citable unit with interactive figures, statistical methods, and transparent SQL queries.
Trademark pending
Proprietary batch processing architecture reduces LLM inference costs by ~50% while maintaining sub-second response times.
Sustainable unit economics
Trade Secrets
Prompt engineering & routing logic
First Mover
NL-to-genomics pioneer
Network Effects
Shared Query Cards & studies
Data Expertise
Years to replicate integrations
A common concern: "What if Anthropic or OpenAI just releases this themselves?"
If BioQuery were just "ask questions about data in plain English," this would be a real threat. But general-purpose AI tools can't replicate what we've built:
We've integrated TCGA, TARGET, GTEx, CCLE, CPTAC, and GENIE through ISB-CGC BigQuery. Anthropic builds general tools, not cancer genomics data pipelines.
Our analysis modules encode actual biostatistics: survival analysis, differential expression with multiple testing correction, 33K+ MSigDB signatures. Domain expertise in software, not prompt engineering.
Reproducible, shareable analysis units with methods text for grants is a specific product format, not a general capability.
Cancer researchers don't want SQL or Python. They want answers with figures for papers. That's a specific product, not a general AI feature.
Would a cancer researcher get the same value from Claude + BigQuery directly?
Today
No — they'd need to know schemas, write statistical code, generate proper visualizations.
In 2 Years
Maybe the gap narrows, but domain-specific data curation and scientific workflow design aren't what Anthropic optimizes for.
Our moat: The biology, the data relationships, the researcher workflow, and the community — not the AI wrapper.
$0
10/month
Try before buy
$99/year
100/month
Individual researchers
$49/month
Unlimited
Power users
$199/month
Unlimited + API
Labs & companies
80%
Gross Margin
<$50
CAC (organic)
4.8x
LTV:CAC Ratio
We're building the future of cancer genomics research. Get in touch to discuss the opportunity.
Contact Us