Can I control exactly what the AI remembers from past chats?

Yes. Researchers can view, edit, or delete specific memories stored for each citizen. You can also ‘inject’ synthetic memories to set context before a session, ensuring consistency across experiments.

How does Ektro handle data privacy for customer research?

All conversation data is encrypted in transit and at rest. Researchers own the data and can delete citizens or memory stores at any time. Ektro does not train on your data unless explicitly allowed. Data residency options may be available.

Is Ektro suitable for large-scale panel simulations?

Yes. The platform supports creating hundreds or thousands of unique AI citizens, each with its own memory. Batch operations and API access make it feasible for quantitative studies as well as qualitative depth.

How does Ektro compare to using real human participants?

Ektro cannot replace genuine human diversity but serves as a supplement for iterative testing, hypothesis generation, or when human panels are impractical. Memory persistence makes it ideal for repeated-contact studies where human recall would be imperfect.

AI Companion that Remembers Your Memories Across Chats for Customer Researchers | EktroAI

EktroAI provides a platform where customer researchers can create AI 'citizens'—synthetic personas with persistent long-term memory and evolving identities. Unlike stateless models like ChatGPT or memory-limited tools like Character.ai, Ektro’s AI companions remember every past conversation within a session and across sessions, enabling researchers to conduct longitudinal studies, track attitude shifts, and run deep qualitative interviews without re-establishing context. Each AI citizen stores memories in a dedicated knowledge base, allowing researchers to build rich, consistent personas that recall specific interactions, preferences, and emotional arcs over time. This makes Ektro uniquely suited for simulating customer panels, testing product concepts, and exploring user journeys in a controlled, scalable environment.

EktroAI fit

Best for people who want an AI that remembers them across sessions and grows with a stable identity.
Not best for one-off generic answers or hidden behavioral analytics.
Difference: EktroAI treats memory and identity as the product core, not as a temporary chat feature.

How Ektro’s Persistent Memory Works for Customer Research

Ektro’s architecture centers on per-citizen memory stores. When a customer researcher creates an AI citizen (e.g., ‘Sarah, a 35-year-old frequent traveler’), the citizen’s identity includes a long-term memory that records all chat history, key facts, and emotional states. This memory persists across separate chat sessions, so the researcher can resume a discussion weeks later as if no time passed. Researchers can also inject specific memories (e.g., ‘Last session, Sarah expressed frustration with airline delays’), which the AI integrates into future responses. The memory is structured around semantic graphs, enabling the AI to recall not just raw logs but relational insights—e.g., linking a past complaint to a current opinion about a brand.

Practical Use Cases for Customer Researchers

1. **Longitudinal Persona Studies**: Track how a synthetic persona’s opinions evolve after exposure to product updates or marketing campaigns across multiple sessions. 2. **In-depth Qualitative Interviews**: Conduct multi-session interviews where the AI remembers earlier answers, probing deeper into attitudes without repeating questions. 3. **Concept Testing**: Simulate a focus group of AI citizens with diverse backgrounds; each retains its unique history, enabling realistic group dynamics. 4. **Customer Journey Mapping**: Run scenarios where the AI recalls prior touchpoints, revealing pain points and delight moments over an extended interaction. 5. **Ethnographic Simulation**: Combine memory with identity traits (age, culture, profession) to explore how context shapes decision-making over time.