Benchmarks, readiness reviews and agent builds from a practicing biopharma bioinformatics leader. I help teams see where AI science agents are reliable, where they fail, and what humans still need to supply.
Runs code, documents it, returns reproducible output. Any capable model does this today.
Chains analysis steps, applies statistics consistently, adds independent agent review. Needs a good harness.
Context-specific insight, spotting analysis blind spots, iterating on expert feedback. Needs human ground truth, context and judgment.
Every engagement starts by placing your use case on this scale. That tells us what to automate now, what to verify, and where a scientist must stay in the loop.
Domain-expert tasks with grading rubrics and reference solutions that run end to end from raw public data, so your scores mean something.
An assessment of where AI agents fit your research workflows, where they need verified tools and context, and how to govern them.
One working agentic workflow: verified analysis tools, your program context, human review gates, and outputs your scientists can audit.
An independent read on whether an AI-for-biology product does what it claims, tested against tasks a working scientist would set.
Generic benchmarks reward fluent answers. Expert tasks check the method: the right statistical test, the confounders, the claim the evidence can support. This example reflects a failure seen in real co-scientist runs.
Verified, reproducible analysis code the agent calls instead of improvising. The agent's job shifts from computing answers to interpreting checked outputs.
Your program priorities, past decisions and thresholds. Grounding agents in institutional knowledge turns generic output into answers for your pipeline.
Expert judgment on what was not asked, and on which claims overreach. Every finding is labeled for what it is: an association, a hypothesis, or a validated result.
Alpine Bio is the independent practice of a bioinformatics and clinical-evidence leader at a global biopharma company, who builds real-world-data platforms and agentic co-scientist workflows for drug discovery. You work directly with the founder, not a team of generalists. Full credentials are shared on the intro call.
Tell me what your agent is supposed to do. I will tell you where it sits on the readiness scale and what it would take to trust it.