Tandem Labs is the platform for persistent memory and trust in AI teammates. It is an agentic staffing platform for engineering teams: companies add software-based AI teammates by role and level, starting with Dave for SRE, sold per agent rather than per seat.
Tandem connects to the systems where work already happens, turns scattered signals into durable operational memory, and keeps every action inside clear approval boundaries. This is agentic staffing for software teammates, not recruiting or human staffing.
Dave plugs into code, docs, incidents, tickets, runbooks, alerts, and team conversations. No toolchain replacement is required.
Turns scattered context into durable memory: current task state, historical decisions, failure patterns, ownership, and system relationships.
Agents explain what they used, what they found, and what they recommend. Read-only and human-in-the-loop operation stay on by default.
Each approval, rejection, incident, and handoff adds to the same team memory. Future AI roles inherit the context instead of starting over, with the same per-agent commercial model.
Dave is the first staffed AI role on the Tandem platform. He starts with read-only SRE triage and grows through a shift-left operating model: more context before escalation, stronger recommendations before action, and explicit approval before production change.
Junior Dave starts with low-risk, high-value SRE work: reading alerts, gathering the surrounding context, and preparing a clear summary so the team can move from signal to understanding faster. He does not take uncontrolled production actions.
Explore Dave concept
The philosophy is not to make Dave autonomous overnight. Tandem moves the earliest, most repetitive parts of SRE work closer to the signal: read the alert, gather context, explain likely causes, recommend next steps, and only expand action rights when the team has earned confidence through approvals and outcomes.
Junior Dave is live today. He reads alerts, gathers surrounding context, and summarises what is happening so humans start with a clearer picture instead of a blank investigation.
The next step is deeper investigation before escalation: correlate logs, tickets, deploys, runbooks, and ownership so the team can reach root cause faster with evidence attached.
As trust grows, Dave can propose and prepare safer remediation paths, then execute only the actions the team explicitly approves inside defined production boundaries.
The same memory, trust, and approval layer can later support QA, development, architecture, UX, and product workflows. That is the staffing model: add AI teammates by role and level while keeping the near-term wedge in production operations, where context loss is most expensive.
Tandem is for engineering teams where incidents, access, and operational context matter. Too much knowledge lives across dashboards, docs, tickets, runbooks, incidents, and people's heads. Tandem gives software-based AI teammates shared memory, clear permissions, and approval boundaries so teams can add capacity without weakening control.
Dave starts with the minimum access needed to read context, explain what it found, and recommend next steps. Permissions expand deliberately, not automatically.
Any write path or production-impacting step requires explicit human approval. Trust is inspectable, not assumed.
Tandem layers onto existing systems with controlled connections, clear boundaries, and no requirement to replace the tools teams already trust.
Tandem extends engineering teams with AI teammates, but operational judgment and ownership remain with the people responsible for the system.
We are looking for a small cohort of SRE, platform, and engineering teams with real on-call load, incident volume, observability complexity, or context-handoff pain. Partners help shape the current product, the trust boundary, and where Dave should widen next.
For investors and strategic partners, Tandem is building persistent memory and trust for AI teammates. The long-term platform is agentic staffing for engineering teams: multiple software-based AI roles over time, sold per agent rather than per seat. The near-term wedge is Dave for SRE.
If there is a fit, we will reach out to discuss a small working cohort. No commitment required.
We respect your privacy. Your email will not be shared with third parties or used for spam.
