Company profile · 3 min read
Enjamb Labs: an AI workforce for biopharma
Enjamb connects agents to biopharma systems and channels so research, clinical, regulatory and quality work stays attributable across the drug program.
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
Published · Updated
What it does
Enjamb Labs is an AI workforce for biopharma. Its agents work inside the systems a drug program already uses—Benchling, Veeva, Medidata, LIMS, Slack, Teams and internal tools—across research, clinical operations, biometrics, regulatory and quality. The YC profile describes a connection layer for closed life-science systems; the current homepage shows attributable work inside the company’s permissions.
The fit is a biopharma team that wants agents to carry evidence, analysis and work product across a long drug program without moving data into a generic AI workspace. Enjamb is strongest where provenance, review and system access matter as much as model capability.
Why I’d look closer
The platform is specific about the handoff: agents act in Slack, Teams or Google Chat, connect to scientific systems, keep company memory, and attach every result to the documents, queries, code and person that produced it. The homepage reports 130+ companies, 34 countries and 10,000+ scientific workflows; those are company-reported usage claims. It also lists SOC 2 Type II, ISO 27001 and HIPAA controls and says the agent holds no credentials of its own, acting with the requester’s permissions. These status claims should still be verified during enterprise diligence.
The founders’ backgrounds fit the intersection. The YC biographies describe Rayan Mubarak with ML/biology research and Maadhav Deekshitha with Dell AI Lab, Broadcom and systems/AI research experience. The launch reports company-specific claims about FDA submission packages and dataset checks; I would treat those as vendor claims, not regulatory or clinical proof.
What I’d ask
Which connectors and regulated workflows are live, how are source permissions enforced, and where does human approval stop the agent? I’d run one evidence-to-document workflow with a controlled dataset, inspect attribution and audit logs, and verify security certifications, data residency and no-training terms.
My editorial take
Enjamb is a serious enterprise fit for biopharma teams whose bottleneck is the distance between systems and accountable work. The permission and provenance model is the differentiator. The buyer should validate one workflow end to end rather than accept the breadth of the platform as proof.
Quick facts
| Field | Sourced detail |
|---|---|
| Buyer fit | Biopharma research, clinical, regulatory, quality and operations teams |
| Integrations | Benchling, Veeva, Medidata, LIMS, Slack, Teams, Google Chat and internal tools |
| Usage signal | Company reports 130+ companies and 10,000+ workflows |
| Security posture | Homepage states SOC 2 Type II, ISO 27001 and HIPAA controls; verify scope |
Sources checked
Checked 2026-09-20.
| Source | Used for |
|---|---|
| YC company profile | Product, founders and launch claims |
| Enjamb homepage | Current platform, integrations, usage and security claims |
| Enjamb About | Company context and enterprise surface |
Cohort context
Enjamb Labs is listed in Spring 2026. In our 2026-09-18 directory snapshot, 112 of 193 listed companies in that cohort have YC’s primary industry label B2B (58.0%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.
Public website snapshot
Observed 2026-09-19T16:16:12.192Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.
| Signal | Homepage observation |
|---|---|
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| Docs/developer link | Not observed in this response |
| Pricing link | Not observed in this response |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |
Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.
About the author
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.
First1000 ↗ · X ↗