Natural Language Survey Analytics India: Ask in English, Get Verified Charts

Natural language survey analytics India starts with a simple promise: you should not need to learn SQL to learn from Indians. A brand manager in Chennai should type 'show brand funnel by city tier' and get a funnel, not a syntax error. A founder in Pune should ask 'what drives repeat purchase among urban millennials?' and get a driver analysis, not a ticket. Hercules Works delivers this because Poseidon does not feed raw rows to an LLM and hope for arithmetic. It builds a Survey Knowledge Graph at ingestion — Question, Column, Formula and Demographic Axis nodes with EXTRACTED/INFERRED/AMBIGUOUS confidence — and every English question traverses that graph to find the right columns and the right formula before any code is generated. The LLM never invents math; it assembles verified SQL templates that are then checked.

The payoff is visible in everyday questions. 'What is average satisfaction for delivery speed in Tier 2?' maps to a likert_5pt Column Node, selects the mean/top-2-box Formula Node valid for that type, adds a GROUP BY on city_tier Demographic Axis, and executes via DuckDB on Parquet. 'Which feature ranked first most often?' maps to a ranking column stored as 'Feature A;Feature B' and computes Borda count Σ(n − rank_position), not a naive average. 'How many respondents mentioned price in open text?' routes to Open-Ended Intelligence for theme clustering across Hindi, Hinglish and English. In each case, natural language survey analytics India is not keyword matching; it is intent understanding plus formula selection, with the Survey Knowledge Graph as the lens. Built by Jupiter Meta Labs in Bangalore, Hercules Works handles this automatically while you keep control of audiences, quotas and languages, from Free ₹0/month with 10 AI chats to Pro at ₹30,000/quarter.

Crucially, Poseidon handles Indian English as Indians speak it. 'Kya NPS hai Tier 2 mein?' and 'What is NPS in Tier 2?' are embeddings, not exact strings, so the semantic cache at 78% hit rate recognises them as the same intent and returns the verified result in under 200 ms. For a Lucknow team running surveys in Hindi and Hinglish, that means the same verified answer serves both languages without re-computation. For a Hyderabad FMCG brand testing price too low/cheap/expensive/too high in Van Westendorp, the English question 'show Van Westendorp curves by income' correctly triggers the Price Sensitivity Meter formula, not a generic frequency. That is why natural language survey analytics India on Hercules Works feels like talking to a senior analyst who already knows your questionnaire, your columns, and the correct formula for each — because Poseidon does, via the graph built once and traversed many times.

Every question you type goes through Poseidon's 5-phase LangGraph pipeline, the single most important reason natural language survey analytics India on Hercules is accurate. Phase 1 — Initialization — loads the Parquet schema, checks if the question is relevant to the loaded survey (if you ask about pricing but the survey is about shampoo, Poseidon refuses rather than hallucinates), rewrites ambiguous queries for precision, and checks the semantic cache at 0.88 threshold. If a similar prior query exists and the schema hash has not changed, the cached verified result returns instantly. This is why 78% of repeat queries return in under 200 ms at zero LLM cost — natural language survey analytics India that learns your habits and rewards them with speed, essential when a Delhi CMO asks the same NPS cut every Monday morning.

Phase 2 — Routing — classifies along two axes: intent (analytics, clarification/greeting) and complexity (SIMPLE/MODERATE/COMPLEX). SIMPLE triggers on single column, single formula, no cross-question join, caught by regex like 'how many respondents' or 'NPS score' — it skips the LLM, uses a rule-based SQL template and executes directly. MODERATE is one or two columns, one formula, no routing — single code-generation call, execute, verify. COMPLEX is multiple questions, demographic cuts, conditional logic, joins — Query Decomposer splits it into sub-tasks, Multi-Task Orchestrator runs them in parallel and aggregates. Internal distribution is roughly 78% cache, 11% SIMPLE, 7% MODERATE, 4% COMPLEX. Routing is how Hercules Works keeps natural language survey analytics India both cheap (lite mode for SIMPLE) and powerful (parallel sub-agents for COMPLEX) without asking you to choose a mode; you just ask in English and Poseidon routes correctly.

Phases 3 through 5 complete the promise. Phase 3 — Analysis — runs three parallel sub-agents: Data Profiler (per-column stats via DuckDB, LLM sees only summary dict, never raw rows), Column Selector (walks the Survey Knowledge Graph to find exact columns matching concepts with confidence tags), and Quality Checker (flags completeness, outlier risk, sample-size concerns). The Code Generator then assembles SQL from column names plus formula template — the LLM does not invent arithmetic. Phase 4 — Verification — re-derives every number via fresh DuckDB SQL (Numerical Claim Verifier at 0.5 tolerance), structural checks (Output Validator lite or full), and narrative audit (Self-Critique 0-10). Phase 5 — Reporting — caches the verified result, generates the answer plus chart data plus verification metadata, assigns confidence (low/medium/high) and suggests two to three quick follow-ups. That is how 'what is NPS by city tier?' becomes a verified, charted narrative in 1.5 to 3 seconds, streamed.

If the 5-phase pipeline is the engine, streaming SSE with persistence is the dashboard that lets you watch it work — and a key reason natural language survey analytics India on Hercules feels trustworthy. When you hit enter, the browser opens a Server-Sent Events stream to POST /api/v1/analysis/query/stream/{chat_id} and you see progress messages: 'Profiling columns...', 'Selecting relevant columns...', 'Generating SQL...', 'Verifying numbers...', 'Writing narrative...' with a 0-100% bar. This is not theatre; each message corresponds to a LangGraph node completing, so you know whether the system is in Analysis or Verification. For a 1.5 to 3 second simple query, the bar flicks; for a 60 to 90 second COMPLEX query like 'NPS by city tier split by top-ranked feature with post-stratification weighting,' you watch the decomposer fan out, sub-agents run in parallel, and numbers get verified before aggregation.

The differentiator is persistence. If you refresh the browser or switch from chat to report tab mid-query, the backend job continues and result is saved to chat_turns.stream_logs. When you reopen, Hercules recovers by chat_turn_id and shows the final verified answer — no lost work, no re-run, no second charge. Most 'ask your CSV' tools lose state on refresh; Hercules Works does not. That persistence is built for Indian reality: flaky connections in Tier 2 offices, shared devices, and back-to-back meetings where you ask a question, lock the phone, and return later. Built by Jupiter Meta Labs in Bangalore, Poseidon treats streaming as part of reliability, not polish. Combined with DuckDB-on-Parquet at 10-20× faster than Pandas and ephemeral DuckDB connections that ensure no cross-survey leakage, natural language survey analytics India on Hercules is both live and safe.

Streaming also surfaces verification live. When Numerical Claim Verifier re-computes 47.3% and finds it correct, you see 'Verifying numbers...' succeed; if it finds a mismatch, you see a retry message before the final narrative appears. That transparency is why brand managers in Hyderabad and Delhi trust the answer without a manual audit — they watched the audit happen. And because the semantic cache at 78% hit rate means repeats return under 200 ms, streaming rarely even appears for frequent questions; it shines for complex explorations where you want to know progress, not just result. Whether you are on Free ₹0/month testing your first pricing survey or Pro at ₹30,000/quarter running daily trackers, streaming SSE with persistence makes natural language survey analytics India feel instantaneous, observable, and resilient — hours, not weeks, with zero anxiety about lost work.

Intent and complexity routing is where natural language survey analytics India either feels magical or breaks — and Hercules Works was built to make it magical. Consider intent: 'what is NPS by city tier?' is analytics; 'can you clarify the question?' is clarification; 'hi, what can you do?' is greeting. Poseidon classifies before any SQL, so it never tries to compute NPS for a hi. Complexity is equally important. SIMPLE is regex-detectable: 'how many respondents,' 'what is average,' 'NPS score,' 'top 3 answers' — single column, single formula, no join — handled by rule-based SQL template with no LLM, sub-second. MODERATE is one or two columns, one formula, no skip logic — single generation, single verify. COMPLEX is the hard one: 'NPS by city tier, split by the top-ranked feature' touches three Question Nodes, two formulas (NPS plus Borda), and a GROUP BY plus join — so Query Decomposer breaks it into sub-tasks and orchestrates them in parallel. You never label complexity; you just ask, and Poseidon routes.

Why does routing matter for Indian data? Because Indian surveys are rich with multi-select and routing. A naive router would treat 'most preferred payment mode' as a single categorical and count respondents, missing that the column is multi_select stored as 'UPI;Cards;COD' and must be UNNEST(string_split(col, ';')) to count selections. Poseidon's Column Selector, walking the Survey Knowledge Graph, resolves that correctly with EXTRACTED confidence when the question type is multi_select, or INFERRED when fuzzy matched, or AMBIGUOUS when multiple formulas fit — in which case it asks for clarification rather than guessing. That is natural language survey analytics India that respects data shape, not just words. For a Mumbai fintech asking 'preferred wallet features by age,' the router correctly fans to multi-frequency plus top-2-box per feature, rather than collapsing to a single frequency table.

The business impact is that you can finally ask hard questions in English and trust the answer. A Kochi D2C beauty brand asked 'what drives repeat purchase among urban millennials with rating above 4?' — a COMPLEX query with a filter plus driver analysis. Poseidon decomposed it, ran correlation plus Shapley decomposition per city tier, verified each coefficient via fresh DuckDB SQL, and returned a narrative with confidence medium and suggestion 'split by income?' That analysis would have taken a Delhi agency three days and a junior analyst plus a senior reviewer. On Hercules Works (hercules.works/ai), it took 41 seconds streamed, verified at 99.1% accuracy, cached for next time, and cost tokens only once thanks to 78% cache hit. From Free ₹0/month through Starter ₹1,119/month (₹895 annual) to Pro ₹30,000/quarter, routing makes natural language survey analytics India both accessible to a founder like Vikram in Hyderabad and deep enough for Unilever's consumer insights team.

Verification and follow-ups are where natural language survey analytics India stops being a chat trick and becomes a decision system. Every English answer on Hercules Works passes three-layer verification before you see it: Numerical Claim Verifier re-computes every number via fresh DuckDB SQL within 0.5 tolerance (deterministic, no LLM), Output Validator checks that percentages sum to ~100, NPS is minus 100 to plus 100, and scale bounds are respected (with lite mode for SIMPLE to save latency), and Self-Critique scores the narrative 0-10 for unsupported claims and causal overreach, rewriting up to two cycles and enforcing the 'So What' rule — business implication before evidence. You see confidence low/medium/high and a verification badge in the chat. For 'what is NPS by city tier?' you do not just get numbers; you get numbers that have already been re-derived, structural-checked, and rhetorically audited. That is why Kantar and ICICI Prudential co-sign Hercules outputs.

Then come quick suggestions — the two to three clickable follow-ups under each answer that make exploration effortless. After NPS by city tier, Poseidon might suggest 'split NPS by top-ranked feature,' 'show top-box drivers for Tier 2,' or 'what is Van Westendorp price curve for premium seekers?' These are not generic prompts; they are generated from the Survey Knowledge Graph's Formula to Demographic Axis edges, so they are always valid next analyses for your questionnaire. Clicking one traverses the same 5-phase pipeline, reuses cached column profiles, and often hits semantic cache, returning under 200 ms. That is natural language survey analytics India that teaches you what to ask next, compounding insight without compounding effort. For a Jaipur founder who knows what she wants but not the statistical term for it, quick suggestions are the bridge from curiosity to method.

Together, verification and suggestions close the loop on trust and habit. You ask in English, you get a verified charted answer in 1.5 to 3 seconds (or under 200 ms cached) with live SSE progress and persistence, you see confidence and verification metadata, and you click a suggestion to go deeper — all on SuperJ's 20M plus verified Indians at 60-90%+ rates, with pricing from Free ₹0/month permanent (10 AI chats, 100 users) to Pro ₹30,000/quarter. No manual Excel wrangling, no agency wait of 6-8 weeks, no lingua-franca barrier. Whether you are a research agency in Ahmedabad serving BFSI or a growth lead in Chennai testing ad recall, natural language survey analytics India on Hercules Works via Poseidon — FastAPI, LangGraph, Gemini, DuckDB, Parquet, Survey Knowledge Graph — turns English into evidence, and evidence into decisions, in seconds rather than weeks.