Anima Chat
Ask Your Data Anything.
Natural language access to your research library. Type a question. Get an answer grounded in your actual data -- across countries, categories, and waves. No SQL. No pivot tables. No waiting for the analyst to come back from lunch.
Your Data Knows the Answer. You Just Cannot Reach It.
Research teams sit on years of accumulated data -- brand trackers, usage and attitude studies, concept tests, segmentation studies across dozens of markets. The intelligence is there. But getting to it means filing a request with the analytics team, waiting days for a custom cut, and hoping the question was framed precisely enough to get a useful answer back.
Chat removes that bottleneck. It turns your research library into a live conversation. Ask a question in plain language and get an answer drawn directly from your data, with citations showing exactly which study, which wave, and which market the answer came from. The analyst is not replaced. The waiting is.
What Chat Can Do
Chat is built on a retrieval-based architecture designed specifically for research data. It does not generate answers from general knowledge or language patterns. Every response is traced back to specific data points in your research library. Here is what that architecture enables.
Cross-Country Comparison
Ask "How does brand awareness for our lead product in Kenya compare to Nigeria?" and Chat surfaces the answer from your data in seconds -- pulling from the relevant tracker waves, normalizing for methodology differences, and presenting the comparison with source citations. No need to open separate reports, align timeframes manually, or reconcile different data formats. Ask the cross-market question and get the cross-market answer. Works across as many markets as your data covers.
Rolling Data Windows
Chat does not query static snapshots. It tracks trends through rolling data windows with delta processing, so you see not just what the data says right now, but how it has changed. Ask "How has purchase intent shifted over the last four waves?" and Chat returns the trajectory, highlights inflection points, and flags statistically significant movements. Your data becomes a living timeline instead of a stack of disconnected reports.
Session Memory
Chat remembers your conversation. Start with a broad question -- "What are the top three brands in the beverage category in East Africa?" -- then drill down without repeating context. "How about among 18-24 year olds?" "And in urban areas specifically?" Each follow-up builds on the previous question. No re-explaining, no starting over. The session holds context across the full thread, the way a conversation with a knowledgeable analyst would.
Retrieval-Based Architecture
Chat does not hallucinate. This is not a general-purpose language model guessing at answers. Every response is retrieved from your data. If the answer is not in the data, Chat tells you -- it will say "this question cannot be answered from the available data" rather than fabricate a plausible-sounding response. Every answer includes data citations: the study name, wave, market, and question reference that produced it. Verifiable, auditable, trustworthy.
Talk to Your Research
Chat Interface Demo
The demo is pre-loaded with a sample multi-market brand tracking study spanning six markets and eight quarterly waves. Type natural language questions -- "What is the top brand in the beverage category in Kenya?", "Show me the trend for purchase intent over the last four waves", "Compare aided awareness between urban and rural segments in Nigeria" -- and watch Chat return answers with data citations, inline chart visualizations, and confidence indicators for each data point.
Watch session memory in action. Ask a follow-up question that builds on the last answer without restating context. See how Chat carries the thread forward, narrowing the analysis with each turn. Export the full conversation as a shareable report with all data references and visualizations intact -- ready for a stakeholder review without any reformatting.
Interactive Demo Coming SoonRetrieval, Not Generation
The distinction matters. Generative AI tools produce text that sounds correct. Retrieval-based systems return information that is correct, because it comes directly from your source data. Chat is built on retrieval-augmented architecture specifically designed for structured research datasets.
When you ask a question, Chat does not predict what the answer should be. It identifies which data points in your library are relevant, retrieves them, structures the response, and cites the sources. If the relevant data does not exist, the system says so. There is no gap-filling, no interpolation from training data, no plausible fiction. What you get back is what your data actually says.
For research teams that have spent years building data integrity, this is not a feature. It is the entire point.
Who Uses Chat and How
Chat is designed for anyone on a research team who needs answers from data but does not want to wait for custom analysis. The use cases range from quick lookups during a client call to deep exploratory sessions that would normally require a dedicated analyst.
Client Calls and Presentations
A client asks an unexpected question during a debrief. Instead of promising to follow up next week, pull up Chat on a second screen and surface the answer in real time. Grounded in data, cited, and ready to share. The follow-up email writes itself.
Pre-Study Exploration
Before designing a new study, explore what your existing data already covers. "Do we have data on brand switching behavior in the premium segment in South Africa?" Chat searches across your full library and tells you what exists, which waves covered it, and where the gaps are.
Trend Monitoring
Set up regular queries against rolling data windows to track key metrics. "How has our brand health index moved in the last three waves across all markets?" Get the trajectory, spot the movements, and flag anomalies -- all without waiting for the next scheduled reporting cycle.
Cross-Functional Access
Marketing, product, and strategy teams that are not data specialists can self-serve from the research library. Chat handles the query complexity. The user just asks a question in plain language and gets a grounded, sourced answer they can act on.
The Briefing Room
Chat is the interface layer for the entire Anima platform. It gives your team instant, conversational access to the intelligence that Calibra validates, Essentia generates, and Praesaga predicts. One interface for everything your research library knows -- past, present, and projected.
As your Anima deployment grows, Chat grows with it. Connect new data sources, add Essentia panel outputs, layer in Praesaga predictions -- everything becomes queryable through the same conversational interface. The complexity stays under the hood. Your team just asks questions and gets answers.