Purpose-built AI agents that live in your Slack channels and hold context so your whole team can build on each other's work.
The problem
AI adoption is happening. Team value is not.
Most teams have API keys or active subscriptions. But most AI usage is still individual. One person gets value, and the team doesn't see it. Giving everyone their own bot just makes it worse: more experiments, more silos, no shared expertise.
When work spans days, you pay the context tax over and over: re-explaining decisions, re-sharing links, rebuilding context before anything can move forward.
The problem isn't access. It's structure.
Why Tiny Lab
Not a generic chat window
The hard part isn't the model. It's everything around it.
General chat tools help individuals. Tiny Lab helps teams, by making work visible, shared, and easy to build on.
We've run extensive deployments across real teams in Slack and watched what actually works. We know what breaks adoption, what builds trust, and how to make an agent legible enough that a team can calibrate to it and act on it.
For example
A team orchestrator tracks open decisions, pings owners when things need attention, and posts a daily recap. Nothing falls through the cracks between meetings.
How work changes
When the agent holds the context, three things shift.
Context compounds.
Decisions persist. Work picks up where it left off. You stop re-explaining and start building.
Knowledge travels.
What gets figured out in one channel becomes usable in another. Fewer duplicated decisions. Less drift.
Async by default.
Work moves forward even when the team is offline. No "remind me what we decided" tax.
AI stops being a private tool and becomes shared team infrastructure.
What you get
A purpose-built agent.
Each Tiny Lab agent has a defined domain, a characteristic way of approaching problems, and a predictable scope. Your team learns what to ask, when to trust the output, and when to push back. The behavior stays consistent as the work gets harder and the team gets bigger.
Team Orchestrator first
Coordinates work in a shared channel. Holds the thread, tracks decisions, keeps work moving even when the team isn't. This is the starting point. Once a team has one and sees how it works, they start imagining everything else.
Specialists coming next
Research synthesis, release coordination, support triage, ops. Each one purpose-built for a specific domain.
How it works
Clear scope. Shared context. Consistent behavior.
When you request early access, we work with you to identify the right agent for your team's first deployment. We handle setup, hosting, and lifecycle management. You bring your LLM API key and we take it from there.
The agent joins a dedicated Slack sandbox we set up for your team, introduces itself, and starts working alongside you. Context accumulates. The team builds a mental model of what to ask and what to expect. Over time, work gets faster — not because the agent got smarter, but because the team stopped paying the context tax.
On setup: We start in a dedicated Slack sandbox, a separate workspace where your team and the agent work together, build trust, and prove value before moving into your existing Slack.
Most teams start with one, and one is enough to see what's possible.
FAQ
What is a design partner?
A team that gets early access and works closely with us, sharing feedback so we can shape the product around real workflows.
Do we need to change how we work?
No. The agent works in Slack, where your team already works.
Do we bring our own LLM API key?
Yes. Tiny Lab runs on your LLM account. Model inputs and outputs stay with your provider, and you control model choice and costs.
Which models do you support?
We start with a supported set of providers and models and expand based on demand. If you have a preference, tell us when you request access.
Does this run in our existing Slack workspace?
We start in a dedicated Slack sandbox, a separate workspace where your team and the agent can work together, build trust, and prove value. Production deployments in your existing Slack are available on a case-by-case basis.
What does "managed" mean?
Tiny Lab handles agent provisioning, monitoring, lifecycle management, and updates. Runtime updates are gated: we test them on internal agents before rollout, so you're not surprised by behavior changes. After any update that affects how the agent behaves, the agent posts a change note directly in your channel. If something goes wrong, we can roll forward to the last known good state quickly.
What data do you store?
We store the bot's identity and configuration files, and the minimal metadata needed to run the service. We do not store your Slack messages or conversation content. If you set up a document repo, artifacts live in your GitHub repo, not ours. You can request deletion of your configuration data at any time.
What if we want to add more agents later?
That's the idea. The first agent is the gateway. Once your team sees how it works, you'll have a much better sense of what else you want. We'll be right here for that conversation.
Request Early Access
We're opening design partner deployments now. Tell us what you want your first agent to do.
Optional
You're on the list. We'll reach out as early access opens.