Everything you need to read an electorate.
One platform spanning field operations, graph intelligence, analytical depth, and scenario planning — each module wired to the same governed data layer.
Command Dashboard
Constituency-wide KPIs, political lean distribution, issue intensity, and live digital signals — your full AC at a glance.
Booth Intelligence
Per-booth voter composition, turnout, pulse scores and confidence — all 247 polling stations, searchable & sortable.
Booth Map
Interactive geospatial layers for lean, turnout, demographics and issue hotspots across every booth in the AC.
Knowledge Graph
Parties, candidates, booths, schemes and narratives resolved into one queryable Neo4j ontology with 40+ relationships.
AI Search
Ask questions in plain language — the engine compiles Cypher, runs it on the knowledge graph, and explains the answer.
Intelligence Hub
Narratives, contradiction flags, scheme gaps, and geo-attributed digital signals surfaced through a multi-source NLP pipeline.
Voter Demographics
Gender ratio, age cohorts, locality segments and booth-level voter composition derived from the 2026 electoral roll.
Candidate Profiles
Affidavit-backed dossiers: assets, liabilities, ITR history, criminal cases, education — sourced directly from MyNeta.
Scheme Delivery
Beneficiary counts, coverage gaps and scheme-to-booth attribution across welfare programmes in the constituency.
Scenario Planner
Model four electoral scenarios — water, roads, scheme delivery, narrative risk — with twin-scenario comparison at booth level.
Swing Analysis
Vote-swing projections by booth, party, and demographic cohort — identify which booths are persuadable and why.
Development Works
MLA works from eGramSwaraj linked to booths, surfacing delivery gaps and completed projects at ward level.
From raw PDFs to structured, auditable insight.
Form 20 results, 2026 SIR electoral rolls, MyNeta affidavits, scheme beneficiaries, and public digital signals are pulled into a staged data layer.
Entities are de-duplicated and linked into a Neo4j knowledge graph — candidates, parties, booths, households, issues and narratives.
Confidence scoring, contradiction flags and alias expansion run across sources, with an LLM layer that compiles natural language into Cypher.
Query, visualise and compare — from a single polling booth to the whole constituency — through nine purpose-built modules.