Infrastructure for Life Science Agents
Genomics databases, search indexes, and research environments, purpose built for the AI world.
Rafflesia brings you the fastest homology search.
AI agents are ushering a new era in biology, reasoning across billions of sequences to surface connections, accelerate discovery and invent new medicines.
Rafflesia is building the fastest and most scalable cloud infrastructure for the AI era.
We provide search across UniProt, PDB, and AlphaFold DB (200M+ structures), with a simple, plug-and-play, agent-ready API.
If you want to perform homology searches on your own internal databases, contact us.
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RUN- Read query64 aa · alpha-globin
- SearchAlphaFold DB · 214M structures
- Rank and alignsignificance calibrated
Scale
Our homology infrastructure separates storage and compute. Sequences are indexed on cheap object storage, an SSD cache is used for faster queries, and expensive Smith-Waterman dynamic programming alignment is performed only on the subset of data that's the most relevant.
This allows us to achieve tree-of-life scale protein alignment in seconds, with an accuracy far superior to BLAST, MMseqs2 and DIAMOND.
The approach was pioneered in the 2010s by the engineers behind Google's Dremel and BigQuery, to run SQL queries over petabytes of data. It now powers the most scalable databases on the web: Snowflake, Databricks, turbopuffer, ClickHouse, and more.
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Security
Rafflesia is trusted by bioinformatics researchers, fast-growing biotech startups, pharma companies, and frontier AI labs. Our core infrastructure was built to comply with high standards of security, compliance, and privacy.
Learn more about our security and compliance practices in the privacy policy.
- SOC 2 Type 2
- Zero data retention available
- HIPAA compliant
- GDPR compliance
- Single sign-on (SSO/SAML)
- Single-tenant or BYOC
- Available on every cloud
- Microsoft AzurePending
Team
Agentic research is the new frontier in the life sciences. Given the right tools, autonomous agents can run experiments, test hypotheses, and reason over data at a pace no human could sustain by hand, compressing centuries of discovery into years.
We're a small and mighty team of infrastructure engineers, statistical physicists, and bioinformaticians. We're building the foundational cloud infrastructure that these research agents run on, and we'd love for you to help us build it. Join us.
- Get in touch
Homology Search
Search sequences and structures at tree-of-life scale with fast, serverless, reproducible indexes.
- Get in touch
Databases
Curated biological databases with typed relations, built for AI agents.
- Get in touch
Foundry
Run open biological foundation models through one typed, usage-based inference API.
- rafflesia-ai/requel
Ontology
An open-source, opinionated framework for Agentic Research
- Get in touch
Benchmark & Evals
Measure agents on real structural biology work with task-faithful benchmarks and evals.
FAQs
Rafflesia runs homology search over the protein universe: give it a sequence or a structure and it returns the related ones, aligned, with coordinates, coverage, and a significance estimate. It is infrastructure, not an assistant. You get deterministic biological primitives and the evidence behind them, never an interpretation of what the hit means for your project.
Those tools match sequences letter by letter. Once two proteins share less than about a quarter of their residues, the letters stop agreeing and those tools go quiet, even though the proteins are still related. Rafflesia finds those pairs. Each one comes back as a full alignment: where the match starts and ends, how much of each sequence it covers, and how likely it is to be chance.
The other difference is scale. Their index has to fit on the machine running the query. Ours does not, so a bigger database does not mean bigger hardware.
UniProt for sequences, and the PDB plus AlphaFold DB for structures, which is over 200 million of them. Every search names the release it ran against, so a result stays reproducible after the underlying database moves on.
Yes. Private collections are indexed the same way the public ones are and searched with the same primitives. Get in touch and we will size it with you.
The aligned pair with query and target coordinates, the alignment itself, coverage, the score, and a significance estimate calibrated against the space that was actually searched. Everything is machine-readable and carries the release it came from, because a hit you cannot trace is not evidence.
Yes. The same query against the same release returns the same answer, and no language model sits anywhere in the path. Nothing is sampled, nothing is guessed, and nothing summarises the result for you.
An HTTP API, the rafflesia CLI, and generated SDKs that all speak the same contract, because they are all generated from it. Searches run asynchronously and batch cleanly, so ten thousand queries are one job rather than ten thousand of them.
Tree-of-life scale searches finish in seconds. A much larger corpus does not mean a larger deployment, so growth in the database is not growth in what you have to run.
Install the CLI, or open the dashboard and run a search against a public release. Neither needs infrastructure from you.