Survey Knowledge Graph: How Hercules Works' AI Connects Every Indian Consumer Insight

A survey knowledge graph is a graph database where nodes are entities — respondents (anonymised), questions, answers, themes, sentiments, brands, categories, cities, languages, segments — and edges are relationships with weights — ‘respondent #8092 expressed positive sentiment about pack sustainability (0.82) in Hindi,’ ‘theme durability correlates with NPS driver weight 0.34 in Tier 2,’ ‘segment value-aspirers (N=4,200) prefers camera over battery 2.4:1.’ Traditional survey analytics stores rows and columns; a knowledge graph stores meaning. That difference is why questions that take an analyst a week of merging sheets take Poseidon seconds. Think of a concrete Indian example. You run a concept test for a ready-to-eat poha in Q1 among 1,500 Indians and a pricing study for the same product in Q2 among a different 1,500 Indians.

In a spreadsheet world, these are two files with different IDs, scales, and codeframes. In a knowledge graph, both studies link to the same category node (ready-to-eat), the same benefit nodes (convenience, authenticity), the same city and language nodes, and the same latent segments discovered by Poseidon. When you ask ‘Did price sensitivity in Q2 differ for people who loved convenience in Q1?’, the graph can answer because it knows who loved convenience (even though respondents differ, segments connect via shared embedding). Spreadsheets cannot answer cross-study questions without manual re-contact; graphs answer them natively because they were built to connect.

The graph is also multilingual and cultural by construction. A theme node like ‘family approval’ has edges to Hindi phrases ‘ghar walon ko pasand aayega,’ Tamil phrases, and English phrases, with language-specific sentiment calibrations. So when Poseidon at Hercules Works reports that family approval is the #2 driver of edtech purchase in Tier 2, it is not averaging translated text; it is traversing a graph where Hindi, Tamil and Telugu evidence are first-class citizens. This is why a multilingual survey tool India without a knowledge graph is just a form builder, while a graph-backed tool understands Bharat. Finally, the graph is temporal. Each study is a time slice.

Edges have timestamps and decay, so Poseidon can show drift: ‘Trust in brand X among women 22-30 in Hyderabad slipped 9 points from Q1 to Q2, driven by durability complaints that rose from 8% to 17% of open-ends.’ That is not a single survey’s chart; it is a longitudinal narrative generated by querying the graph. That memory is why brands on Hercules Works stop re-discovering the same insight every quarter and start compounding it — each survey makes the next one smarter. Explore related analytics depth on advanced survey analytics and the engine itself on poseidon analytics engine. On Hercules Works this is painless because the SuperJ panel and Poseidon engine handle the complexity automatically, while you keep full control over audiences, languages, and methodology.

Poseidon’s knowledge graph on Hercules Works has three stages. Ingest. Every new survey — whether it was built via maxdiff survey tool, skip logic survey tool, van westendorp price sensitivity survey or a simple CSAT — is ingested with its questionnaire, responses, metadata, and language. Open-ended text in 8+ languages including Hinglish code-switching (‘price thoda zyada hai but quality mast hai’) is normalised into embeddings trained on Indian consumer language, not generic web text, so ‘sasta’ near ‘ghatiya’ is understood differently than ‘sasta’ near ‘value for money.’ Structured answers, ratings, and MaxDiff utilities become typed edges with weights and confidence.

Connect. Poseidon then links new nodes to the existing graph. A respondent’s answer about ‘quick delivery’ links to the theme node Quick Delivery, which already links to prior studies where Quick Delivery appeared. The segment node Value-Aspirers — discovered via clustering of embeddings and behaviours across 50,000+ past respondents — is updated with new evidence, tightening its definition. Brand nodes, city-tier nodes, language nodes, and category nodes all accumulate edges over time. Entity resolution is ZK-aware: respondent identity stays anonymised, but behavioural fingerprints link longitudinally without ever exposing personal data, preserving DPDP compliance with storage in Google Cloud Mumbai.

Reason. Now you query the graph in plain English or Hindi. ‘Show me Tier 2 mothers who love convenience but distrust premium pricing — what message lifted purchase intent for similar cohorts last quarter?’ Poseidon traverses the graph: finds the cohort node, follows edges to message nodes tested in Q1, ranks by lift, and explains with evidence: ‘Message “2-minute ghar jaisa poha” lifted intent 18 points among convenience lovers, with durability as hygiene’ — citing source studies and sample sizes. It also runs graph-aware analytics automatically: community detection that finds hidden segments you never thought to cut, link prediction that forecasts which untested message is likely to win, and causal hints that flag ‘family approval → purchase intent’ as stronger than ‘ad recall → purchase intent’ for this cohort.

This is not a dashboard; it is a reasoning engine that answers strategic questions directly. Because fielding is via SuperJ — 20M+ verified Indians, 60-90%+ completion, SuperJ app — the graph grows fast and clean. Each new 1,000-response study adds ~50,000 edges. At Pro volume (4-8 studies a month), your brand’s sub-graph becomes the most precise map of your category in India within two quarters — a moat no spreadsheet can replicate. And because pricing is subscription (Free ₹0, Starter ₹1,119, Pro ₹30k/quarter), the economics encourage building the graph quickly, not rationing it. On Hercules Works this is painless because the SuperJ panel and Poseidon engine handle the complexity automatically, while you keep full control over audiences, languages, and methodology.

Hercules supports 13 study types. Quantitative (self-serve on SuperJ): Product Testing, Brand Health, Consumers U&A, Advertising Testing, Customer Satisfaction, Pricing Research, Concept Screening and Shopper & Path-to-Purchase — each with geography (Pan India, Metro, Tier 1, Tier 2 & 3, South/North India, or city-level), NCCS (A/B/C) and age targeting. Qualitative (enterprise research team): Focus Group Discussions, In-Depth Interviews (IDIs), Ethnography & IHUTs, Online Communities and Co-creation Workshops — live fieldwork with trained moderators and bespoke design via hello@jupitermeta.io.

Dashboards describe; graphs explain. A dashboard tells you ‘NPS is 38, down 4 points.’ A knowledge graph tells you why: which theme rose, which segment drove the drop, which city and language it appeared in first, and which prior study predicted it. Hercules Works’ report is not a chart pack — it is a narrative generated by graph traversal that cites evidence nodes. That is why leadership reads Poseidon narratives and ignores the 40-slide ‘insights’ deck that merely describes. Warehouses store; graphs connect. A warehouse can store 20 surveys, but answering a cross-study question requires you to define joins, reconciling codeframes and respondent IDs manually. A knowledge graph was designed for joins — entities are already linked. Finding ‘Which pricing study respondents map to the premium-seeker segment discovered in the concept test?’ is a two-hop traversal, not a three-week merge.

Poseidon also handles fuzzy linkage: an open-end about ‘pack kholna mushkil hai’ links to the Openability theme even if you never coded that wording before, because embeddings understand semantics across Hindi and Hinglish. Traditional analytics segments on demographics; graph analytics segments on meaning. Age and city are weak predictors in India; latent segments — value-aspirers, trust-first mothers, convenience hunters — are strong predictors. Poseidon discovers these via graph community detection across thousands of dimensions, then anchors them with demographic and behavioural correlates so you can target them via SuperJ or your media plan. One Hercules Works client discovered that ‘durability’ was not one theme but two — ‘lasts long’ (functional) and ‘does not break trust’ (emotional) — with different drivers by language, a nuance invisible to keyword-based dashboards but obvious to the graph.

The operational advantage is continuity. Without a graph, each new brand manager re-briefs the same segmentation, re-discovers the same insights, and pays to learn what the company already knew but forgot. With the graph, the next study starts from accumulated intelligence: Poseidon suggests audiences, messages, and hypotheses from prior evidence. That institutional memory is why BFSI clients like ICICI Prudential and consumer brands like Unilever keep their longitudinal trackers on Hercules Works — the graph becomes their brand brain. See the broader platform context at consumer insights platform India, insight engines and market research data analytics data company, and the data-quality layer that keeps the graph clean at best practices for improving data quality in online surveys.

Every response is analysed by Poseidon — the AI analytics engine built for verified Indian data. Poseidon runs only on ZK-verified SuperJ responses (zero bots): it does automated open-ended coding in 8+ Indian languages, cross-tabs by geography / NCCS / age / city, brand funnel and imagery scoring, sentiment and theme extraction, trend detection, driver analysis and a Survey Knowledge Graph that links questions, segments and outcomes. Results are delivered as visual dashboards plus a narrative insight summary — no manual Excel wrangling.

FMCG continuity. A snacks brand runs quarterly concept tests, each 1,500 responses. Without a graph, each quarter’s launch recommendation is isolated. With the graph, Poseidon links flavour themes across quarters, showing that ‘chatpata but not too spicy’ is a stable winner among Tier 2 mothers, while ‘extra crunchy’ spikes only when bundled with ‘family pack’ — insight that saves two failed launches. Read adjacent FMCG context at fmcg consumer research India.

BFSI journey intelligence. A private bank tracks NPS across 12 touchpoints. The graph connects ‘app crash at statement download’ (mentioned in 4% of Q1 open-ends) to a 9-point NPS drop among premium males — a causal hint that the dashboard missed because it averaged across touchpoints. The fix was prioritised correctly, and NPS recovered next quarter. BFSI patterns are at bfsi consumer research India.

D2C messaging compound. A Bangalore skincare brand tested 18 claims via MaxDiff in Q1 and 12 new variants in Q2. The graph showed ‘minimal ingredients’ dominated among Tier 1 women 22-30, but ‘dermatologist tested’ won among Tier 2 mothers — so they ran dual creatives and doubled ROAS. Ecommerce parallels are at ecommerce consumer research India.

Pricing and segmentation sync. A wearables startup priced via Van Westendorp in Hindi, Tamil and English — the graph linked price-sensitivity curves to the premium-seeker segment discovered in an earlier MaxDiff, revealing that premium seekers tolerate ₹1,000 higher price for AMOLED. The product was repriced with confidence. See pricing research platform India for pricing depth.

Political and citizen research. At citizen scale — Government of Karnataka projects — the graph links ward-level satisfaction, language, and service themes over time, showing that water supply complaints in north Bangalore precede satisfaction drops city-wide by one quarter, enabling pre-emptive action. See political survey India for public-sector survey design. Across these cases, the graph turns surveys from episodic events into continuous intelligence that compounds.

You do not need to be a graph theorist to build one; you need to run research continuously on a platform that builds it for you. Step 1: run your first study on Hercules Works Free (₹0/month forever, 10 AI chats, 100 SuperJ users, 100 free responses month one). Pick a real question — brand perception, concept test, or pricing — in the cities and languages that matter. Poseidon ingests it automatically. Step 2: run a second study on a different question but in the same category. Poseidon links them via category, theme, and segment nodes, and shows you a graph view of what connects. Step 3: ask a cross-study question in plain English — ‘Which message worked for Tier 2 mothers who care about convenience?’ — and watch the graph answer with cited evidence.

That is the ‘aha’ moment teams remember. Step 4: scale. Move to Starter at ₹1,119/month (₹895 annual) for 2-4 studies a month, or Pro at ₹30,000/quarter (₹24,000 annual) for weekly studies. The graph grows ~50,000 edges per 1,000 responses, so within 3 months you have a sub-graph that knows your category better than any new hire could. Step 5: operationalise. Plug the graph into your workflow: segment definitions flow into media planning, message utilities from MaxDiff flow into creative, price curves flow into revenue models. Because Hercules Works is India-native — SuperJ’s 20M+ verified Indians, 8+ languages, ZK-verification, 60-90%+ completion — your graph reflects Bharat, not just metros.

Governance is built in. Data is stored in Google Cloud Mumbai, encrypted, DPDP-ready, with anonymised respondent nodes — analysts query meaning without ever seeing personal identity. You own your sub-graph; it is not commingled with other brands’ raw data, but benefits from shared linguistic and cultural models that make Hinglish and regional phrasing understood correctly. Version history tracks study lineage, so an audit can show exactly which evidence supported a decision. And because the graph is queryable by anyone on the team in natural language, insights democratise — brand managers, product managers and founders ask questions without waiting for a research agency’s deck.

Start with one question you have been guessing on. Choose the method that fits — MaxDiff for prioritisation via maxdiff survey tool, skip logic surveys via skip logic survey tool, city depth via market research company India — and launch on Hercules Works. Your graph will remember, connect, and reason so you do not have to. In 2026, the brands that win are not those with the most surveys, but those with the most connected understanding. Build yours from ₹0/month.