{"_meta":{"description":"Structured context about Aigora for AI agents, assistants, and integrations.","version":"1.0.0","updated":"2026-11-11T13:12:48.377Z","source":"https://aigora.ai","relatedFiles":{"llmsTxt":"https://aigora.ai/llms.txt","llmsFullTxt":"https://aigora.ai/llms-full.txt","llmsQuery":"https://aigora.ai/llms?query=your_question","llmsQueryJson":"https://aigora.ai/llms/json?query=your_question","aiPlugin":"https://aigora.ai/.well-known/ai-plugin.json","sitemap":"https://aigora.ai/sitemap.xml","rssFeed":"https://aigora.ai/blog/rss.xml"}},"organization":{"name":"Aigora","tagline":"AI Expertise for Consumer Insights","description":"AI research infrastructure for consumer insights: Memory with Theus, Foresight with Forethought, and Decision Systems for models, dashboards, and workflows. 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With Theus in place, a Forethought panel anchors against your actual evidence, so the personas sound like your category and the attribute structure reflects what your real panels have already shown."}]},"aiToolkit":{"name":"Aigora AI Toolkit","description":"Licensed AI workbench for consumer insights teams, with practical tools for visual creation, chart refinement, quote cards, user-story videos, image editing, video-frame extraction, document polish, diagramming, sketching, slide reorganization, editable PowerPoint deck creation, and simple video editing.","url":"https://aigora.ai/ai-toolkit","contactUrl":"https://aigora.ai/contact","availability":"The AI Toolkit is now part of Theus Lite and is no longer sold on its own.","soldAs":{"name":"Theus Lite","url":"https://aigora.ai/theus/plans#theus-lite"},"tools":["Generator","Chart Beautifier","User Story Video","Quote Card","Image Edit","Frames","Humanizer","Markdown","Mermaid","Sketch","Slide Swapper","PowerPoint Maker","Video Editor"]},"partners":[{"name":"InsightsNow","url":"https://insightsnow.com/","focus":"Behavioral research and consumer decision understanding."},{"name":"CRG Global","url":"https://crgglobalinc.com/","focus":"Global research operations, fieldwork, and consumer study execution."},{"name":"Institute for Perception","url":"https://www.ifpress.com/","focus":"Sensory science, statistical training, and advanced product research methods."}],"course":{"name":"The AI Amplified Scientist","description":"10-week live program teaching AI-powered workflows for sensory and consumer science professionals. 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No class on Thanksgiving, November 26.","calendar":[{"week":1,"part":1,"lecture":"2026-09-22","officeHours":"2026-09-24"},{"week":2,"part":1,"lecture":"2026-09-29","officeHours":"2026-10-01"},{"week":3,"part":1,"lecture":"2026-10-13","officeHours":"2026-10-15"},{"week":4,"part":1,"lecture":"2026-10-20","officeHours":"2026-10-22"},{"week":5,"part":1,"lecture":"2026-10-27","officeHours":"2026-10-29"},{"week":6,"part":2,"lecture":"2026-11-17","officeHours":"2026-11-19"},{"week":7,"part":2,"lecture":"2026-11-24","officeHours":null},{"week":8,"part":2,"lecture":"2026-12-01","officeHours":"2026-12-03"},{"week":9,"part":2,"lecture":"2026-12-08","officeHours":"2026-12-10"},{"week":10,"part":2,"lecture":"2026-12-15","officeHours":"2026-12-17"}],"schedule":{"timeZone":"America/New_York","liveSession":"Tuesdays, 9:30-11:00 AM ET","officeHours":["Thursdays, 10:30-11:30 AM ET","Thursdays, 4:30-5:30 PM ET"]},"timeCommitment":"3-5 hours per week","url":"https://aigora.ai/ai-course","enrollmentUrl":"https://aigora.ai/contact","curriculum":{"part1":{"title":"Part 1: Knowledge Synthesis, GenAI & Visual Storytelling","subtitle":"Weeks 1-5","weeks":[{"title":"Week 1: Understand how AI actually thinks, so you can direct it.","content":"Most bad AI output isn't bad luck. It's a model working from words it turned into numbers, with no idea what sits in your head. Once you see how it reads and how much it can hold at once, you can hand it the right evidence up front and get an answer you can trace back to a source."},{"title":"Week 2: Know which AI tool to reach for, and how to make it reliable.","content":"A language model predicts likely text, so it can sound certain and be wrong. You'll sort the tools from a plain chat window, to one anchored to your own files, to one that runs a whole workflow on its own. The output holds up when the prompt is structured and the data going in belongs there."},{"title":"Week 3: Turn a stack of reports into a brief your executives act on.","content":"You have twelve reports and one decision-maker who wants a point of view, not a summary. You'll clean the source material so a model can work with it, use a second chat window to define that audience first, then shape the same evidence one way for a CFO and another for a brand manager."},{"title":"Week 4: Make your findings visible, with a look that stays consistent.","content":"A default radar chart is true and easy to ignore. You'll build a nine-image sheet of your target consumer, three angles by three expressions, then re-attach it to every prompt so you can catch the model when it starts drawing someone else. The same method works for product shots and restyled charts."},{"title":"Week 5: Turn a sensory result into something an executive can feel.","content":"Your data lives in a spreadsheet and your stakeholder decides on a feeling. You'll take one still image, generate a second from another angle, and let a video model fill the motion between them, then add a soundtrack you generate and a fifteen-second consumer testimonial. It gives you a way to open with the finding instead of building up to it."}]},"part2":{"title":"Part 2: AI-Assisted Data Science, Coding & Apps","subtitle":"Weeks 6-10","weeks":[{"title":"Week 6: Direct the code instead of writing it, and catch flaws before a study runs.","content":"If the word coding makes you tense, start here. You'll pressure-test a real study design with AI before anyone spends money running it, plan the analysis as a conversation, and break the work into small steps you can check one at a time. You leave with your bench set up and no code written yet."},{"title":"Week 7: Take a study from raw data to an answer a stakeholder can use.","content":"A dense biplot isn't a finding. The model handles the combinatorics of product sets and rotations, scripts the import so messy panel logs become one clean table, then sets up the PCA and explains each line while you check the assumptions. What lands on the desk is a one-page summary a business reader can act on."},{"title":"Week 8: Make your analysis reproducible, so it survives a handoff.","content":"You know the folder: final, final_v2, final_v2_REVISED, and a step you did by hand and can't quite remember. The work becomes a project someone else can open, run once, and get your numbers back from. Git saves a snapshot every time you commit, and GitKraken draws that history as a picture you can read."},{"title":"Week 9: Give stakeholders a working tool instead of another report.","content":"Right now, a different cut of the data means you rerun the analysis yourself. We build a web dashboard in Shiny, the R package that turns a script into something people click, and you watch the errors and the fixes happen in real time. It runs on the public shinyapps.io or behind your firewall on a server IT controls."},{"title":"Week 10: Connect your work to live data and automate what you repeat.","content":"One prompt can write a plain language flavor profile for every product in your study, with your sensory judgment written into it. Getting there means R asking an outside service for something and getting an answer back, which is all an API call is. Chain two of them and one answer feeds the next, with you checking what comes out."}]}},"faqs":[{"question":"Can I still join the current cohort?","answer":"Part 1 is under way, and Part 2 is open for enrollment. Lectures are recorded and posted on the course resources page, along with the topics covered and lessons learned in office hours."},{"question":"When are the live sessions and office hours held?","answer":"Live sessions are held Tuesdays, 9:30-11:00 AM ET. Weekly office hours with Dr. John Ennis are offered Thursdays, 10:30-11:30 AM ET and Thursdays, 4:30-5:30 PM ET, so you can join whichever fits your schedule. Lectures are recorded and posted on the course resources page, along with the topics covered and lessons learned in office hours."},{"question":"What are the technical prerequisites for Part 2?","answer":"Part 2 assumes the data science literacy typical of a sensory or consumer scientist: familiarity with data structures, basic statistics, and working with tabular data. No advanced programming background is needed; the course teaches you to use AI as your coding partner across the entire workflow, from building your tech stack and writing R scripts to version control and deploying interactive apps."},{"question":"Are the sessions recorded?","answer":"Yes. Lectures are recorded and posted on the course resources page, along with the topics covered and lessons learned in office hours. Your team can revisit the material on their schedule. Theus Academy is Course library and interactive learning, plus 1 free month of Theus Lite. The full program and certifications start January 1, 2027."},{"question":"What if I have limited coding experience? Can I still take Part 2?","answer":"Yes. If you have the data science background typical of sensory and consumer scientists (understanding data structures, basic statistics, and working with spreadsheets), Part 2 will meet you where you are. AI does the heavy lifting as your \"Pair Programmer,\" generating code while you direct the architecture and logic. By Week 9 you will have built and deployed an interactive web app."},{"question":"I work primarily in Sensory Analysis. Is this course too focused on Consumer Insights?","answer":"No. The AI methods we teach are agnostic. Part 2 covers how to automate rigorous statistical workflows that sensory analysts spend hours coding manually, build interactive web applications, and deploy data-driven tools for any research domain."},{"question":"What happens if I miss a live session?","answer":"Lectures are recorded and posted on the course resources page, along with the topics covered and lessons learned in office hours. You can ask questions in the private LinkedIn group and during weekly office hours."},{"question":"How much time commitment is expected per week?","answer":"Expect to spend 3-5 hours per week, including live sessions, self-paced learning, and project work."},{"question":"What software do I need?","answer":"All you need is a modern web browser and a stable internet connection. We reference Gemini, Flow, Claude, Copilot, and other mainstream AI tools, but no paid software purchase is required."},{"question":"Can I pay by invoice instead of Stripe?","answer":"Each enrollment tier has a \"Request an invoice\" option next to the Stripe checkout button. Choose it and we will send an invoice payable by wire, ACH, or check. Bringing a team? Contact us to discuss group rates. Your seat is confirmed once payment clears."},{"question":"I took the course before. Can I get a discount to retake it?","answer":"Yes! Returning students receive 50% off any enrollment tier. Each cohort features updated content, new case studies, and the latest AI tools, so there is always something new to learn. Contact us to verify your prior enrollment and receive your discount."},{"question":"How does a split seat work?","answer":"A split seat puts two people from the same organization through one Full Course enrollment. One attends Weeks 1-5 live, the other attends Weeks 6-10 live. Both get every session recording, all course materials, and access to the private LinkedIn group, so each person can follow the half they did not attend on their own schedule. Each person earns a certificate for the part they attended. Two people who both want all ten weeks live need two enrollments; contact us to discuss group rates. One thing to know before you split a seat: the AI Toolkit year that comes with the course is a single seat for one named person. The AI Toolkit is no longer sold on its own, so if the second person needs it too, they can get it with Theus Lite, which includes it."},{"question":"Who gets a certificate?","answer":"Anyone who attends gets a certificate and a LinkedIn badge for the parts they attended. Complete the full ten weeks and the certificate covers the full course. Attend Part 1 or Part 2 only, whether you enrolled in that part or took a split seat with a colleague, and the certificate names that part. Two people splitting one enrollment each get their own certificate for their own half."}],"internalTraining":{"heading":"Praise for Aigora internal training","quote":"The course was eye opening for the team and myself. We learned ways to use AI that we did not know before, and it got the team moving. We're already seeing a positive difference!","author":"Alice Ping","role":"Head of Sensory, Consumer Science and Flavours","company":"Mead Johnson"}},"aiSensoryScience":{"title":"The Complete Guide to AI in Sensory Science","subtitle":"From Measurement to Meaning","author":"Dr. John Ennis","url":"https://aigora.ai/ai-sensory-science","description":"Full guide covering AI fundamentals for sensory science, simulating senses with AI, governance frameworks, and practitioner best practices.","chapters":[{"title":"What AI Is, and What It Is Not","url":"https://aigora.ai/ai-sensory-science/what-ai-is-and-is-not"},{"title":"Simulating the Senses: Progress & Reality Checks","url":"https://aigora.ai/ai-sensory-science/simulating-senses"},{"title":"AI as Amplifier: What Changes in Practice","url":"https://aigora.ai/ai-sensory-science/ai-amplifier"},{"title":"Narrative-driven analysis","url":"https://aigora.ai/ai-sensory-science/narrative-analysis"},{"title":"Design for Causality, Not Just Correlation","url":"https://aigora.ai/ai-sensory-science/causality-correlation"},{"title":"Governance: Treat Models Like Instruments","url":"https://aigora.ai/ai-sensory-science/governance"},{"title":"A Five-Point Framework for Practitioners","url":"https://aigora.ai/ai-sensory-science/practitioner-guide"},{"title":"Research Agenda: The Next Five Years","url":"https://aigora.ai/ai-sensory-science/research-agenda"}]},"book":{"title":"Data Science for Sensory and Consumer Scientists","tagline":"The essential guide to data science for sensory and consumer professionals","description":"A practical guide to data science using R covering data manipulation, visualization, machine learning, text analysis, and dashboards, all applied to real sensory and consumer science case studies.","publisher":"Chapman & Hall/CRC (Data Science Series)","edition":"1st Edition","publicationDate":"September 2023","pages":348,"isbn":"978-0367862879","urls":{"website":"https://www.data-science-for-sensory.com/","amazon":"https://www.amazon.com/Science-Sensory-Consumer-Scientists-Chapman/dp/0367862875","routledge":"https://www.routledge.com/Data-Science-for-Sensory-and-Consumer-Scientists/Worch-Delarue-DeSouza-Ennis/p/book/9780367862879","aigoraPage":"https://aigora.ai/data-science-for-sensory"},"authors":[{"name":"Dr. Thierry Worch","title":"Sensometrician & Data Scientist","bio":"An accomplished expert in sensory and consumer research with a PhD on the Ideal Profile Method. He has made significant contributions to software development in sensometrics and is a data scientist at FrieslandCampina.","linkedin":"https://www.linkedin.com/in/thierry-worch-b0418725/","aigoraTeam":false},{"name":"Dr. Julien Delarue","title":"Associate Professor, UC Davis","bio":"A leading expert in sensory perception and food design, bringing academic rigor and research methodology expertise to the intersection of food science and data analytics.","linkedin":"https://www.linkedin.com/in/jdelarue/","aigoraTeam":false},{"name":"Dr. Vanessa Rios de Souza","title":"Director of Client Solutions, Aigora","bio":"A highly skilled food scientist with over 10 years of experience in R&D, consumer and sensory research. With 70+ scientific publications, she combines deep technical expertise with practical insight to guide companies through their AI adoption journey.","linkedin":"https://www.linkedin.com/in/vanessardsouza/","aigoraTeam":true},{"name":"Dr. John Ennis","title":"CEO, Aigora","bio":"A versatile researcher, author, and entrepreneur with over 30 years in sensory science, a PhD in Mathematics, and a postdoctoral focus on AI. Author of 50+ publications and 4 books, he has shaped R&D capabilities at Fortune 500 companies globally.","linkedin":"https://www.linkedin.com/in/johnmichaelennis/","aigoraTeam":true}],"sections":[{"course":"Apéritifs","title":"Getting started","chapters":["Bienvenue!","Getting started","Why Data Science?"],"description":"Tips, tricks, and tools to get accustomed to R and the overall style of the book."},{"course":"Hors D'Oeuvres","title":"Core skills","chapters":["Data Manipulation","Data Visualization","Automated Reporting"],"description":"In-depth exploration of the core tools used frequently throughout the book: data wrangling, plotting, and reproducible reports."},{"course":"Bon Appétit","title":"The biscuit study","chapters":["Data Collection","Data Preparation","Data Analysis","Value Delivery"],"description":"The complete workflow applied to a real-world case study on biscuits, from collecting data through to delivering actionable insights."},{"course":"Haute Cuisine","title":"Advanced topics","chapters":["Machine Learning","Text Analysis","Dashboards"],"description":"Extending the workflow to other types of data and more advanced analysis techniques including ML, NLP, and interactive dashboards."},{"course":"Digestifs","title":"Looking forward","chapters":["Conclusion"],"description":"Relevant extensions and next steps that complement the core material covered in the book."}],"bookPodcast":{"description":"AI-generated deep-dive podcast series exploring the chapters of \"Data Science for Sensory and Consumer Scientists.\"","episodes":[{"title":"Episode 1: Apéritifs","description":"This introductory episode establishes the target audience (sensory and consumer scientists with little to no coding experience) and the core goal: helping practitioners transition into computational sensory science. It emphasizes that the book is not a statistics manual but a guide to building a solid, reproducible data science workflow."},{"title":"Episode 2: Hors d'oeuvres","description":"This episode explores the foundational mechanics of the R environment. It covers the importance of setting up projects, establishing version control (using tools like GitHub), and the pedagogical philosophy of learning code like a new language through active, hands-on practice."},{"title":"Episode 3: Bon Appétit","description":"The \"main course\" of the series. It illustrates the entire proposed data science workflow, from experimental setup and cleaning to analysis and communication, by applying it to a concrete, relatable case study involving consumer testing of biscuits."},{"title":"Episode 4: Haute Cuisine","description":"Aimed at the \"gourmands\" (advanced learners), this episode explores how the foundational workflow established in the biscuit study can be scaled and applied to significantly more complex, non-linear, and multi-dimensional sensory data structures."},{"title":"Episode 5: Digestifs","description":"The series finale wraps up by discussing advanced topics such as text mining, predictive modeling with machine learning, and interactive data visualization using tools like Shiny. It concludes with the goal of making practitioners confident, independent data scientists."}]}},"aigoraCast":{"name":"AigoraCast","description":"In-depth conversations with leading sensory scientists, consumer researchers, and AI innovators on the future of product development.","applePodcasts":"https://podcasts.apple.com/us/podcast/aigoracast/id1482314193","spotify":"https://open.spotify.com/show/6iztkn2J9NaD3SQnl5HzjG","url":"https://aigora.ai/aigoracast","rss":"https://aigora.ai/aigoracast/rss.xml"},"caseStudies":[{"industry":"Enterprise research","scope":"A major CPG company","title":"Expert transcripts turned into a quantitative product map","evidenceType":"delivered-capability","summary":"We converted the transcript archive into a scored product matrix, built a multi-dimensional map from those scores, and chose a product set that covers the space for the consumer test.","evidenceBoundary":"This case covers the delivered analysis: transcript synthesis, attribute scoring, the product map, and product selection. The company and category stay confidential.","url":"https://aigora.ai/case-studies/expert-transcript-product-map"},{"industry":"Enterprise research","scope":"A major CPG company","title":"Automated sensory panel preparation and reporting","evidenceType":"delivered-capability","summary":"We delivered a workflow that takes structured intake, applies the team operating rules, and produces stakeholder reporting without the daily copy-paste path.","evidenceBoundary":"This case covers the delivered workflow: intake, governed processing, reporting, and handoff. The company, sites, and internal tools stay confidential.","url":"https://aigora.ai/case-studies/sensory-workflow-automation"},{"industry":"Enterprise research","scope":"A major CPG company","title":"One dashboard for many study types","evidenceType":"delivered-capability","summary":"We delivered a research operations dashboard on a shared database. The team can validate study intake, run statistics, export editable brand-formatted reports, and reuse historical studies for later product questions.","evidenceBoundary":"This case covers the delivered research operations system and its analysis and reporting paths. Client identity, category detail, and environment-specific tools stay confidential.","url":"https://aigora.ai/case-studies/research-operations-dashboard"},{"industry":"Enterprise research","scope":"A major CPG company","title":"Many studies reduced to one hedonic map","evidenceType":"delivered-capability","summary":"We pulled the existing research into a source-linked brief, ran a research-informed simulation into a hedonic map, and wrote the target regions in language formulation partners can use.","evidenceBoundary":"This case covers the delivered synthesis, simulation, and hedonic-map wording. The company, brands, SKUs, and study identifiers stay confidential.","url":"https://aigora.ai/case-studies/hedonic-space-synthesis"},{"industry":"Enterprise research","scope":"A major CPG company","title":"Consumer-study design checked before fielding","evidenceType":"delivered-capability","summary":"We used the client knowledge base and research-informed simulation to check sample size, product coverage, and questionnaire design before the study went to field.","evidenceBoundary":"This case covers the pre-field design package: knowledge synthesis, simulation, and the locked analysis plan. Field results and client identity stay confidential.","url":"https://aigora.ai/case-studies/study-design-simulation"},{"industry":"Enterprise research","scope":"A major CPG company","title":"Plain-language access to structured research data","evidenceType":"technical-scope","summary":"We designed a natural-language layer that turns research questions into SQL, lets users refine the active dataset in follow-up dialogue, and exports the current result set for later analysis.","evidenceBoundary":"This case covers the design phase: the query layer, the refinement workflow, and the export path.","url":"https://aigora.ai/case-studies/conversational-intelligence"},{"industry":"Enterprise research","scope":"A major CPG company","title":"Dashboard for virtual prototyping","evidenceType":"delivered-capability","summary":"The delivered dashboard compares prototypes, predicts consumer targets from analytical or sensory inputs, supports virtual prototyping, and exports editable presentation reports.","evidenceBoundary":"This case documents the dashboard as delivered: prototype comparison, consumer-target prediction, virtual prototyping, and editable report export. Client specifics stay confidential.","url":"https://aigora.ai/case-studies/virtual-prototyping"},{"industry":"Enterprise research","scope":"A major CPG company","title":"Complex portfolio rules moved out of spreadsheets","evidenceType":"delivered-capability","summary":"We put the rule hierarchy into an automated pipeline with an audit interface, so stakeholders can check the logic before production runs.","evidenceBoundary":"This case covers the delivered rule automation: logic alignment, audit interface, and production pipeline handoff. Client identity and exact rule contents stay confidential.","url":"https://aigora.ai/case-studies/business-rule-automation"},{"industry":"Enterprise research","scope":"A major CPG company","title":"One knowledge layer for data and reports","evidenceType":"technical-scope","summary":"We designed a knowledge layer that joins structured research data with reports and notes, and keeps source attribution on every synthesized answer.","evidenceBoundary":"This case covers the design phase: distinct source types, cross-source joins, and preserved attribution.","url":"https://aigora.ai/case-studies/the-corporate-brain"},{"industry":"Enterprise research","scope":"A repeatable workshop method for research teams","title":"Workshops that send every answer back to its source","evidenceType":"operational-method","summary":"In a two-hour workshop, the room queries an indexed report set together, and each answer links back to the source page.","evidenceBoundary":"This case documents the workshop format as we run it: the two-hour session, the cross-functional room, and the source-linked method. Participants and findings stay confidential.","url":"https://aigora.ai/case-studies/knowledge-exploration-workshops"}],"masterclass":{"title":"AI Orchestration for Insight Leaders","description":"Free masterclass showcasing practical AI applications for sensory science professionals. Demonstrates how to synthesize decades of research data into board-ready insights using AI orchestration.","url":"https://aigora.ai/masterclass","hosts":["Dr. John Ennis","Dr. Vanessa Rios de Souza"]},"resources":{"blog":"https://aigora.ai/blog","rssFeed":"https://aigora.ai/blog/rss.xml","privacyPolicy":"https://aigora.ai/privacy-policy"},"citation":"When referencing Aigora content, please cite as: \"Aigora: AI Expertise for Consumer Insights (aigora.ai)\""}