02 / BIOTECH R&D
The code behind an assay. The pipeline behind a result. The context behind a target. Put AI to work on research software while keeping unpublished science inside the environment your team controls.
Talk through your research ↗THE BOTTLENECK / SOFTWARE BETWEEN EXPERIMENTS
A run fails halfway through. A new export breaks the parser. Two notebooks produce different answers from the same samples. Useful assistance needs the code, run history, and experimental context that explain the discrepancy.
Target names, compound identifiers, assay conditions, and even negative results can reveal what you are pursuing. Research IP shows up in prompts, filenames, code comments, and logs as well as the primary dataset.
A changed reference, package version, or filtering rule can shift an analysis. Your team needs a proposed change, the exact inputs and configuration used, and tests that make the result inspectable.
CRO deliveries, licensed datasets, and participant information come with different access and use conditions. An agent’s tools and retrieved context need to follow those limits, including when engineers work across projects.
RESEARCH SYSTEM / EXPLORE THREE REAL PROBLEMS
Select a research scenario, then walk through the system. See which inputs the agent needs, what it can do, and what comes back for your team to review.
A successful exit code does not mean the analysis is unchanged. Reference versions, filtering defaults, container images, and sample manifests can all change the result. Investigating them takes time away from the science.
The next experiment may depend on this analysis. Your team needs to distinguish a software regression from a biological signal before committing more samples or lab time.
Sample identifiers, cohort metadata, unpublished targets, and file paths can reveal the program even when raw reads never enter a prompt. Scope what the agent can retrieve and what its traces retain.
Give the agent approved code, schemas, and selected research context. A task should not require access to the whole data lake.
Versioned workflow code, dependency locks, container identifiers, reference metadata, run logs, and an approved test dataset. Large sequencing files stay in the existing compute environment.
WHY ON PREMISES / KEEP THE CONTEXT CLOSE
Local inference removes an external model endpoint from the research workflow. It also lets your team choose which models run, when they change, and where the resulting traces live. Tools, storage, and network access still need explicit configuration.
We bring the hardware and inference stack, select open-source models for your compute and memory budget, and configure them for the coding and reasoning tasks your team needs.
Vault handles model inference. Your existing HPC, storage, and analysis tools continue to handle scientific workloads.
Explore Vault ↗Blitz brings an agent workspace, CueCode IDE, execution harness, and telemetry governance into your environment. We tailor the workflow around your repositories and approved internal tools.
Connections to pipelines, job schedulers, and research systems are scoped with your team. Agents get the access their task requires.
Explore Blitz ↗SECURITY / DESIGN AROUND THE RESEARCH
A model response can repeat a sample identifier. A tool trace can contain a proprietary sequence. A shared retrieval index can cross a project boundary. We work through these paths with your research, IT, and security owners before connecting the workflow.
Run inference on Vault and host the Blitz workflow internally. Map the full path through retrieval, tools, telemetry, backups, and support. Scope an air-gapped installation where required.
External calls, approved destinations, offline updates, support access, and tests for unintended outbound traffic.
Scope repositories, context sources, and tool credentials to the authorized project. Decide whether sensitive programs need separate deployments.
Identity mapping, denied-access tests, retrieval isolation, partner access, and visibility of traces and exports.
Start with the permitted use and authorized users. Minimize what enters the prompt, and use synthetic or de-identified fixtures when suitable for the task. Assess applicable agreements and obligations with the data owner.
Consent and data-use restrictions, re-identification risk, permitted users, retention, and any applicable HIPAA responsibilities.
Begin with staged copies, read-only sources, and reviewable code changes. Separate test credentials from production systems and keep approval with the research owner.
Write access, job submission limits, source preservation, review gates, and separation from instruments and systems of record.
Agree which model, code, environment, inputs, commands, and outputs to record. Host traces internally with access and retention appropriate to their contents.
Version capture, sensitive log content, retention, rerun behavior, and evidence needed by the scientist reviewing the change.
START WITH ONE RESEARCH WORKFLOW
Bring a bioinformatician or research software engineer, the scientific owner, and your IT or security lead. Start with approved data and a task with a known baseline. Clinical decisions and validated workflows need their own scope and acceptance process.
Plan your research pilot ↗Pick a pipeline regression, assay import, or partner handoff. Bring the current code, an approved fixture, and the expected result.
Agree which repositories, data, tools, and compute jobs the workflow can reach. Establish who can inspect traces and approve changes.
Measure correctness, reproducibility, review effort, and time to a usable change. Check permission enforcement alongside task quality.
