The AI copilot

The copilot is a large language model that plans gating strategies and drives the application through the same actions you would take by hand.

It is not AI-1. AI-1 is our own model, runs on-device, and identifies a fixed population list. The copilot is a general reasoning model that works in plain language and can attempt anything — with correspondingly different trust properties.

Using it requires a license.

Propose, then approve

The copilot never changes your analysis on its own.

You describe what you want. It lays out a strategy — one step per gate, each with a stated rationale. You execute the steps you agree with and skip the ones you don’t. Gates and statistics update as you approve each step.

Approved steps are recorded, and the record can be exported — which is what makes an AI-assisted analysis defensible rather than merely fast. If you cannot say how a figure was produced, it does not matter how quickly you produced it.

What it can do

It has access to the operations you do by hand, among them:

• create and modify gates
• read statistics for any population
• run clustering-based auto-gating
• inspect metadata, channels and keywords
• read the AI-1 competence report

Because it can read AI-1’s competence report, it can also tell you when our own model declined a population and reason about what to do instead.

Choosing a provider

Provider	Where your data goes
Claude (Anthropic)	Anthropic’s API
Amazon Bedrock	Your AWS account
OpenAI	OpenAI’s API
Local model (llama.cpp / Ollama)	Nowhere — runs on your machine

Configure this in the AI settings panel. API keys are stored locally.

If your institution does not permit cloud AI

Use the local model option, or don’t enable the copilot at all. AI-1 is entirely on-device and unaffected by this choice — the on-device population identification is available to you either way.

A local model needs a model file downloaded and enough RAM to run it, and will be slower and less capable than a frontier cloud model. For strategy planning on a familiar panel that is usually an acceptable trade; for open-ended reasoning about an unusual experiment it is a real step down.

What gets sent to a cloud provider

When you use a cloud provider, the copilot sends what it needs to reason about your analysis — channel names, population statistics, gate definitions and your messages. Check this against your institution’s policy before enabling it. If in doubt, use a local model or stay with AI-1.

The other AI panels

Several panels share the copilot’s model but are aimed at specific jobs:

Panel	What it does
Gate review	A second opinion on gates you have already drawn
Data check	Acquisition and quality problems worth knowing before interpreting anything
Report	A written summary of the analysis
Batch insights	Patterns across a batch rather than within one file
Tutor and Quiz	Flow cytometry teaching. These do not analyse your data.

Limits worth knowing

It can be wrong, confidently. That is a property of language models, not a bug we are close to removing, and it is the reason nothing is applied without your click. Read each proposed step; the rationale is there so you can disagree with it.

It is not a substitute for knowing your panel. It reasons from what it is told. A mislabelled channel produces a well-argued wrong strategy.

AI-1’s abstention is more trustworthy than the copilot’s confidence. When AI-1 declines a population, that is a measured statement about its own competence. When the copilot offers an opinion, it is a plausible one. Weight them differently.