How does AI remember my research context?

The short answer: you don't configure anything. AI picks up your industry, role, target audience, and past research from your conversations and remembers them across sessions. You can view and edit what it has stored under Settings → Memory at any time.

What gets remembered automatically

From your normal conversations, AI extracts and remembers:

Your profile — industry, role, company type, what you work on
Your preferences — research methods you favor, persona types you trust, report depth
Your research history — past studies, key findings, decisions made
Your brand context — product positioning, target audience, competitive framing

You don't fill out forms, don't set "preferences," don't say "remember this." It happens as you talk.

What it looks like in practice

First conversation. You say "I want to understand healthy snack trends. I'm a brand manager at a consumer goods company, our target is 25-35 year-old urban women." AI files this away.

Tenth conversation. You say "I'm considering an emotional-value snack line." AI responds with: "Based on your focus on 25-35 year-old urban women and your past research on healthy snacks, here's what I'd suggest..." — no need to re-explain.

Where Memory shows up

In three places:

AI responses — context is already woven in
Settings → Memory — view, edit, or delete what's stored
Brief template suggestions — when starting a new study, AI pre-fills based on memory

Can I edit what's remembered?

Yes. Go to Settings → Memory. You'll see structured entries for:

User profile (industry, role, etc.)
Brand positioning
Core target audience
Research preferences
Past research summary

Edit anything that's wrong. Delete anything that's outdated. AI uses the edited version going forward.

You can also tell AI in conversation: "Forget that I work in fintech" or "Update my target audience to include Gen Z." It adjusts.

How does the auto-organization work?

As you use the system longer, memory accumulates. To prevent it from getting unwieldy:

Deduplication. When "target is 25-35 year-old women" appears in 5 different conversations, AI collapses it to one stored fact.

Compression. When 10 studies all touch "emotional value," AI stores "core theme: emotional value" instead of 10 separate notes.

Prioritization. Recent and frequently-mentioned facts are kept; stale one-offs are dropped.

Versioning. Past versions are kept in the change history so you can roll back if needed.

You'll see memory organization run automatically when stored entries cross 100 or 30 days have passed since the last organization. You can also trigger it manually.

Memory and objectivity

Memory only provides context. It doesn't change how AI evaluates evidence.

If your memory says "we think product A is great" and the new research shows users dislike product A, AI will tell you the research findings honestly. The memory helped frame the question; it doesn't bias the answer.

If your memory says "target audience is 25-35 year-old women" and a new study is on 40-50 year-olds, AI will note the demographic difference when reporting findings. The memory informs what to compare against; it doesn't replace critical thinking.

Memory in team settings

If you're on a team plan, there's a separate team memory layer:

Personal memory — only you see this. Follows your account.
Team memory — shared across team members. Captures things like "our company positioning" and "what we've researched as a team."

When you leave a team, you lose access to that team's memory but keep your personal memory. Your personal memory doesn't migrate to the team — they stay separate.

What to put in memory proactively

While memory extracts automatically, you can speed it up:

In your first 5 conversations, mention:

Your role and industry
Your product or service
Your target audience with specifics (age, region, behavior)
Your research style preference (quick validation vs deep dive)
Your brand positioning or differentiation

When starting major projects, state the context: "We're testing a new packaging design. It needs to feel premium and align with our sustainability positioning."

Each of these gives memory concrete material to extract. Without it, AI has to infer — usually well, sometimes not.

What memory won't do

Update itself in real-time from new market shifts. Memory is what you told the AI; market reality can drift. Re-validate periodically.
Replace independent thinking. Memory is a starting point, not an oracle. Question it when stakes are high.
Cross-contaminate between companies. Personal memory for your old job stays with your account. If you change industries, the memory doesn't bleed into new contexts you haven't told it about.

Common patterns that work

Set the scene early. When you start a new project with atypica, spend 5 minutes in the conversation telling AI about your company, your product, your audience. Memory stores it; future conversations save you from re-explaining.

Re-state what matters. If a key decision was made ("we're going premium, not value"), tell AI explicitly. Important context gets prioritized in memory.

Review quarterly. Every few months, scan Settings → Memory. Delete outdated info, add anything missing, fix errors.

Don't over-stuff. Memory works best when it's the essential 10–20 facts about you, not a transcript of everything you've said. AI is good at pruning, but if you tell it the same thing 30 times, that's still noise.

Limits to know about

Memory is per-account, not per-project. A casual conversation in a side project adds to the same memory as your main work.
Memory doesn't export as part of reports. If you want your research context to travel with a report, write it into the brief.
Memory isn't shared across users. Even on a team, you only see your own personal memory. Team memory is separate and curated differently.

Related: Settings → Memory, Team Memory, Plan Mode