GatherGov.
Hardik Bansal's platform uses knowledge graphs and source-linked AI to turn fragmented municipal records into structured signals for real estate, finance, and newsrooms.
Every week, thousands of local governments in the United States hold public meetings where consequential decisions take shape, zoning changes, infrastructure approvals, development timelines, budget allocations, and tax assessments. Much of it is recorded and published. Almost none of it is usable at scale.
The reason is simple: the public record is scattered. Agendas live in one place, minutes in another, videos on a third-party player, attachments buried in PDFs, and transcripts, when they exist, are long, literal, and difficult to search. For an analyst trying to track more than a handful of jurisdictions, "monitoring municipal intelligence" becomes a manual job with no practical ceiling.
GatherGov is an AI company building software to remove that ceiling. The platform ingests local-government meeting materials, video, audio, transcripts, and supporting documents, and converts them into structured, queryable outputs designed for professional workflows. The company says it processes more than 3,200 hours of municipal proceedings per day.
"Municipal meetings are where infrastructure plans, financial signals, and community sentiment first become visible," said Hardik Bansal, GatherGov's chief technology officer. "For most institutions, the cost of monitoring that information across hundreds or thousands of jurisdictions has been prohibitive. We compress weeks of manual research into seconds, with traceability that institutional workflows demand."
GatherGav's initial core team (Hardik Bansal, second from the right)
Many enterprise AI systems rely on retrieval-augmented generation (RAG): fetch a handful of relevant documents and have a model summarize them. That approach can help with one-off Q&A, but it often breaks down when the job is longitudinal.
GatherGov's approach is different. Instead of treating each meeting as a standalone transcript to retrieve on demand, the platform builds and updates a knowledge graph that links what matters over time: entities (people, firms, agencies), projects, parcels, topics, jurisdictions, decisions, and the relationships between them.
"A traditional RAG system can tell you what a meeting said," Bansal explained. "A knowledge graph can tell you what it meant in the context of related meetings, named entities, and the approval pipeline that came before it. That difference is what makes the output usable for an investment committee."
In practical terms, that means a zoning amendment is not just summarized, it is connected to prior hearings, referenced parcels and developers, adjacent projects, and later votes. The meeting becomes part of a living record rather than a searchable transcript silo.
For GatherGov's customers, the promise is not "AI insights." It is source-backed intelligence that can survive professional scrutiny.
The platform is designed so that outputs are traceable to the underlying record, down to the meeting, speaker, and timestamp. So an analyst, editor, or committee member can verify a claim without replaying hours of video.
"We maintain a continuous chain of provenance, from the spoken word in a council chamber to the structured fact in a customer's report," Bansal said. "That chain is the product. Everything else is in service of keeping it intact."
That design has clear applications across multiple industries:
More municipal meetings are being recorded and published than at any time in recent history, creating a growing archive of public deliberation.
But the archive is only valuable if it can be structured reliably: names must resolve correctly, projects must be linked across meetings even when phrasing changes, and conclusions must remain tethered to primary sources. That is less like building a chatbot and more like building data infrastructure.
Bansal says that is where the company is investing: entity resolution, cross-document linking, and provenance tracking at scale, work the team plans to publish more about.
In the near term, GatherGov's pitch to institutional users is straightforward: replace weeks of manual monitoring with structured outputs delivered through reports and APIs. Longer term, Bansal frames the ambition as decision infrastructure, systems that can answer forward-looking questions about approvals, supply trends, and jurisdictional risk with evidence attached.
"We want to become the default decision infrastructure for development data," he said. "Users should be able to ask forward-looking questions about supply trends, approval likelihoods, and jurisdictional risk, and get structured intelligence back. That's the difference between giving someone a search box and giving them a decision."
