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Generic AI produces plausible-sounding answers without provenance."},{"question":"What is THEUS security posture?","answer":"Aigora is SOC 2 compliant: we follow SOC 2 practices, and our infrastructure providers are SOC 2 certified. Our own independent Type II examination is in progress. The report has not yet been issued. Current deployments inherit controls from Vercel and Neon. Default workspaces are ephemeral (24-hour cache) unless durable storage is enabled by an organization admin. Avatars expire after 72 hours, and authentication is OAuth-only through Google and Microsoft. Client data is not used to train foundation models."},{"question":"What can teams do with THEUS?","answer":"THEUS enables cross-study synthesis, research gap analysis, hypothesis exploration through conversational dialogue, knowledge visualization, and institutional memory retrieval. Teams can query decades of research in seconds, identify what the organization knows and where new fieldwork has the highest value, and build stakeholder-ready evidence trails."},{"question":"What is a TKB file and how does knowledge ingestion work?","answer":"TKB (THEUS Knowledge Base) is a proprietary format created by a 4-stage multimodal pipeline: Normalize, Visual Understanding, Extract, and Validate. This pipeline processes uploaded research documents (up to 25MB) and generates 20–30 atomic facts per page with full statistical context, enabling grounded, traceable AI responses with page-level citations."},{"question":"Who built THEUS?","answer":"THEUS is built by Aigora, a company founded in 2019 by Dr. John Ennis, a mathematician with 30+ years in sensory and consumer science, 50+ peer-reviewed publications, and 4 published books. THEUS is used by consumer insights teams at several Fortune 500 companies. Aigora has also published or presented with leading CPG and ingredients organizations."},{"question":"How does THEUS relate to Forethought?","answer":"THEUS is the Memory layer; Forethought is the Foresight layer. THEUS turns your research into a cited, traceable evidence base. Forethought then builds synthetic consumer panels on that evidence and simulates a category appraisal before you commission fieldwork. 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","pricing":{"seatPriceUsd":1000,"unit":"per user","term":"one-year license"},"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-Powered Strategist","description":"10-week live program teaching AI-powered workflows for sensory and consumer science professionals. Covers prompt engineering, knowledge synthesis, data automation, R programming with AI, web app development, and AI strategy.","startDate":"September 22, 2026","duration":"10 weeks","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: Knowledge synthesis with RAG. Produce citation-backed insights.","content":"How models process information: tokens, embeddings, and context windows. Practice Model Empathy (showing the model where to look) and run the RAG flow with Deep Research, THEUS, and NotebookLM for traceable, citation-backed insights."},{"title":"Week 2: The AI taxonomy and prompting. Write dependable prompts.","content":"LLMs are probabilistic pattern recognizers, not search engines. Classify tools from models and wrappers to RAG systems, workflows, and agents, then apply the 10-Component Framework for prompt engineering, with data security and open formats (JSON, Markdown) along the way."},{"title":"Week 3: The parsing gap. Turn messy data into AI-ready inputs.","content":"Convert unstructured raw data into clean, machine-readable formats. Compare small-RAG tools (NotebookLM for private document synthesis, Copilot for web and workspace integration) and analyze SKU scorecards and research trends with the Aigora Master Datakit."},{"title":"Week 4: The visual toolkit. Generate report-ready images from data.","content":"Use the 4-step prompt-to-image workflow (Define, Contextualize, Synthesize, Refine) with tools like Nano Banana Pro, Aigora Image Maker, and Copilot to produce product mockups, enhanced charts, infographics, whiteboard sketches, and social assets."},{"title":"Week 5: Multimodal communication. Build video and audio narratives.","content":"Bridge dense data and executive reality with video and audio AI. Move from static prompting to conversational editing, with motion keyframing, sonic branding, and an exercise on priming emotion through visuals and sound."}]},"part2":{"title":"Part 2: AI-Assisted Data Science, Coding & Apps","subtitle":"Weeks 6-10","weeks":[{"title":"Week 6: The technical foundation. Stand up your AI-assisted stack.","content":"Set up R, Python, GitHub, and Posit/RStudio. Learn where AI excels (rapid boilerplate) and where humans lead (architecture and technical debt), plus the 5-Stage Segmented Workflow and the 99% Rule: 99% accuracy requires 100% proper context."},{"title":"Week 7: The data science workflow. Refine raw data into a golden dataset.","content":"Work across collection, preparation, analysis, and value delivery. Use AI to generate designs of experiments, script data imports, align panelist metrics, and produce PCA biplots and executive summaries from dense datasets."},{"title":"Week 8: Version control. Build reproducible analysis systems.","content":"Move from ad-hoc scripts to reproducible systems with a pair-programmer working style. Use RStudio, Git, GitHub, and GitKraken as a scientific safety net for code history, directory standards, and pull requests."},{"title":"Week 9: The app builder. Deploy an interactive Shiny app.","content":"Turn analytical scripts into stakeholder tools with Shiny (app.R or app.py). Separate the UI from the server logic, then deploy behind the company firewall (Posit Connect) or to the public cloud (shinyapps.io)."},{"title":"Week 10: API integration. Automate data pipelines end to end.","content":"The capstone week: call APIs and connect external data sources inside R, linking your analytical workflows to live services and automating pipelines end to end."}]}},"faqs":[{"question":"When does the next cohort start?","answer":"A new cohort is now enrolling. Spots are limited. Classes start September 22, 2026."},{"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. All sessions are recorded."},{"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. Every live session and office hour is recorded and uploaded to the private community within 24 hours, so your team can revisit the material on their schedule. If live timing does not work for you, course recordings and resource access are available separately. Contact us to discuss access."},{"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":"All sessions will be recorded and available on-demand. You can ask questions in the private community forum 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. Team enrollment of $5,000 or more can be invoiced net-30. 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 the Shared Seat License work?","answer":"One Full Course enrollment covers two people from the same organization. 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. One thing to know before you split a seat: the AI Toolkit license is a single seat and has to be assigned to one named person. If both people need their own Toolkit access, contact us before enrolling and we will quote a second license."},{"question":"Who gets a certificate?","answer":"Anyone who attends gets a certificate 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 split a Shared Seat License with a colleague, and the certificate names that part. Two people splitting one enrollment each get their own certificate for their own half."}]},"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 & AI Pioneer, 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)\""}