Reliable, fast inference for biology data workloads.
For bioinformatics teams running agents across pipelines, cohorts and papers. One API, 2x faster on long runs.
5M+
Effective context
Every sample, log and paper in one run.
Biology is a data problem.
One analysis can mean thousands of samples, gigabytes of logs and hundreds of papers. Agents that run pipelines and read the results burn tokens at a scale few workloads match.
Pipeline runs and debugging
Run Nextflow and Snakemake workflows and fix them when a step fails.
Variant analysis
QC, calling and annotation across a whole cohort, with every filter recorded.
Single-cell analysis
Cluster, annotate and iterate on parameters until the cell types hold up.
Literature and database review
Read papers and query public databases with the hypothesis still in view.
A fix at batch 9 depends on a choice at step 3.
Pipelines carry decisions forward: the reference build, the filters, the excluded samples. Lose them from context and the agent reruns the cohort with the wrong settings.
More analysis per day, with results you can trust.
Research leads care how fast results come back, whether they hold up, and where the data lives.
Same-day turnaround
Long analyses return sooner, so the next experiment starts while the question is fresh.
Accuracy on long runs
Less stale context gives the model a cleaner view across thousands of samples.
Nothing stored
The API keeps token counts for billing and nothing else. Your data is never stored.
Fast at every stage of the pipeline.
Each stage produces output the next one depends on, and all of it lands in context.
- 01
QC
Read reports across every sample and flag the outliers.
- 02
Align
Run alignment, catch config errors and resume without starting over.
- 03
Call
Genotype the cohort with every earlier exclusion still applied.
- 04
Annotate
Attach annotations and write up results with sources.
Who builds this on Subconscious.
Biotech and pharma R&D
Running analysis agents on their own sequencing data.
Genomics cores and labs
Processing cohorts for many research groups at once.
Bioinformatics platforms
Adding agents that run pipelines inside their products.
Get started in minutes.
Every model is served in both the OpenAI and the Anthropic format. Point the agent you already have at Subconscious.
API format
Language
Endpoint
https://api.subconscious.dev/v1/chat/completions
Model
subconscious/glm-5.3-marathon
Works with
OpenAI SDK
Intact
Tool calls, streaming, structured output
from openai import OpenAI
client = OpenAI(
base_url="https://api.subconscious.dev/v1",
api_key="YOUR_API_KEY",
)
response = client.chat.completions.create(
model="subconscious/glm-5.3-marathon",
messages=[
{
"role": "user",
"content": "Refactor the billing module and update every affected test.",
}
],
)
print(response.choices[0].message.content)Questions
- We do not store your prompts or completions. The API keeps only token counts for billing. Teams with stricter rules can also deploy on their own infrastructure.
- The API serves GLM-5.3 Marathon and DeepSeek V4.1 Flash Marathon in both the OpenAI and Anthropic formats. Dedicated deployments can serve virtually any open model, including your own fine-tunes.
- Yes. Start on the Subconscious API, then move to a dedicated deployment in our cloud or on your own GPUs. The runtime is a drop-in replacement for vLLM or SGLang, and our engineers can install and tune it inside your cloud.
Need it on your own GPUs?
The same runtime runs as a dedicated deployment in our cloud or on your own hardware, as a drop-in replacement for vLLM or SGLang.