The civic ground-truth and verification layer for AI
GovData helps AI systems prove what a government actually decided, not merely generate a plausible summary.
Local-government decisions involving zoning, housing, infrastructure, energy, procurement, public safety, and regulation are fragmented across thousands of incompatible portals and millions of documents.
AI systems can retrieve and summarize individual records, but they cannot reliably determine what was proposed, amended, approved, rejected, postponed, or superseded unless the complete decision chain and chronology are preserved.
GovData normalizes these records into a source-linked government decision graph, creating responsible-AI infrastructure for citation-grade retrieval, temporal verification, interpretability, runtime safeguards, evaluation, compliance monitoring, and independent auditing.
Built by the original architect of Legistar.
Designed from the underlying legislative model outward, not from document summarization inward.
Government information is public, but not AI-ready
Public records were designed for human access within individual jurisdictions, not for machine reasoning across thousands of governments and decades of changing decisions.
No consistent source model
Meetings, staff reports, attachments, motions, votes, and final records are spread across disconnected vendor systems, archives, APIs, and custom portals.
The latest document may not be the final truth
A proposal can be amended, postponed, substituted, vetoed, reconsidered, or superseded. Correct interpretation requires chronology, not just semantic similarity.
Summaries lose the evidence chain
When AI systems collapse multiple records into one answer, users often cannot see which motion, vote, attachment, or source record supports the conclusion.
A source-linked decision graph for civic reasoning
GovData reconstructs the legislative process as linked data so that models can reason across the full history of a decision and return the primary evidence behind the answer.
Place → Government body → Meeting → Agenda item → Legislative matter → Attachment → Motion → Amendment → Vote → Outcome → Source
Identity
Resolves jurisdictions, governing bodies, officials, meetings, and matters across changing portals and vendor-specific identifiers.
Chronology
Connects repeated consideration, amendments, continuances, substitutions, votes, and later actions into a time-aware legislative history.
Provenance
Preserves links to agendas, minutes, staff reports, ordinances, exhibits, packets, and other authoritative public records.
Outcomes
Distinguishes what was discussed from what was adopted, rejected, postponed, amended, or superseded.
Already operating at national scale
GovData is pre-revenue, but not pre-product. The collection, normalization, search, and update infrastructure is live across the United States and Canada.
The dataset contains over 20 years of history, 189,000+ officials, and millions of relationships connecting meetings, matters, attachments, sponsors, motions, votes, and outcomes. It is refreshed continuously as public portals change and new records are published.
Why GovData belongs in the responsible-AI stack
The goal is not to make AI sound more authoritative. It is to make important civic answers more verifiable, reproducible, and resistant to plausible error.
Reduce unsupported conclusions
Retrieve the complete decision history and require answers to cite the source records, motions, votes, and outcomes that support them.
Know what was true when
Determine whether a statement refers to a proposal, an intermediate action, a final adoption, or a decision that was later changed or superseded.
Make answers inspectable
Allow regulators, businesses, journalists, researchers, governments, and the public to follow the evidence chain without trusting the model alone.
Not just better RAG
Generic retrieval can locate documents containing similar language. It does not inherently understand that an agenda item, amendment, substitute motion, roll-call vote, and later ordinance are different stages of the same decision.
GovData contributes the identity resolution, chronology, relationships, and outcome labels that allow retrieval to become evidence-based civic reasoning.
Designed for consequential use
Incorrect interpretations of local-government actions can affect development, compliance, investment, procurement, public reporting, legal analysis, and public trust.
The more consequential the use case, the more important it becomes to distinguish a fluent answer from a verified one.
Help AI know when to answer, qualify, or abstain
GovData does more than retrieve relevant records. It provides controls that help an AI system determine whether the evidence is current, consistent, complete, and sufficient for a reliable conclusion.
Find what replaced the earlier record
Identify when a proposal, ordinance, motion, approval, or policy was later amended, substituted, repealed, reconsidered, reversed, or superseded.
Surface inconsistent evidence
Flag conflicts among agendas, minutes, attachments, vote records, later actions, and other authoritative sources instead of silently selecting the most convenient document.
Check whether the decision chain is complete
Determine whether the available record includes the motion, vote, outcome, chronology, and supporting source material needed to support a reliable answer.
Test the conclusion against the evidence
Evaluate whether an AI-generated answer is actually supported by the cited records and whether the cited records support the specific claim being made.
Separate proposal from final action
Require answers to distinguish what was true at a particular point in time from what was ultimately adopted, rejected, delayed, or later changed.
Do not manufacture certainty
Allow the system to state that the public record is incomplete, contradictory, or inconclusive rather than produce a confident but unsupported answer.
These safeguards turn GovData from a retrieval source into a runtime verification and accountability layer for AI systems operating in consequential civic, regulatory, legal, commercial, and public-sector settings.
Three responsible-AI products from one maintained foundation
Each product uses the same source-linked decision graph, strengthening the underlying dataset while serving a distinct safety and governance need.
GovData Verify
APIs and agent tools that return source-supported answers about government actions, including the relevant chronology, motions, votes, outcomes, and citations.
- Citation-grade civic retrieval
- Temporal and outcome verification
- Structured evidence packages for agents
- Conflict and supersession detection
- Evidence completeness and qualified abstention
The core responsible-AI product.
GovData Benchmarks
Evaluation datasets for measuring whether models and civic AI applications can interpret multi-stage government decisions accurately.
- Hallucination and citation testing
- Temporal reasoning benchmarks
- Final-outcome classification
- Provenance and evidence completeness
A measurable standard for civic-AI reliability.
GovData Monitor
Continuous monitoring for organizations that need to know when local rules, approvals, obligations, or government positions change.
- Regulatory and compliance alerts
- Decision-change detection
- Cross-jurisdictional monitoring
- Primary-source audit trails
Governance infrastructure for ongoing operations.
Safety infrastructure with immediate commercial value
Local-government decisions shape where projects can be built, how businesses may operate, what governments purchase, and which rules apply across thousands of markets.
Ground models in authoritative civic records
Model developers and AI applications need reliable government evidence, evaluation datasets, and audit trails for systems used in legal, regulatory, journalistic, commercial, and public-sector contexts.
Know when local rules and approvals change
Multi-location operators, infrastructure developers, energy companies, real estate firms, and regulated businesses need verifiable cross-jurisdictional monitoring.
Make civic AI inspectable
Governments, newsrooms, researchers, watchdogs, and residents need AI-generated explanations that can be traced back to the public record.
Local-government decisions influence trillions of dollars in public spending and private economic activity. GovData can become the trusted intelligence and verification layer for these decisions, first across North America and eventually across government systems worldwide.
The defensible asset is the maintained decision graph
Monitoring and summarization products validate demand. GovData is differentiated by the historical, cross-vendor, source-linked structure needed to verify outcomes and reconstruct decision history.
- Thousands of public portals must be discovered and monitored
- Vendor systems differ in APIs, schemas, archives, and attachment handling
- Legislative identities must be resolved across meetings and changing source systems
- Chronology and outcomes require domain modeling, not document matching alone
- The pipeline must adapt continuously as portals, vendors, and records change
Why generic AI does not erase the moat
AI can accelerate extraction, classification, and summarization. It does not eliminate the need for a maintained national corpus, durable source identities, historical relationships, quality controls, and domain-specific outcome logic.
The models will change. The need for reliable evidence, provenance, and continuously maintained government data will remain.
Built from more than thirty years of domain experience
GovData is led by the person who originally modeled the legislative relationships the platform now reconstructs at national scale.
Darius Tajanko is a technology founder and municipal-data specialist with more than thirty years of enterprise software experience. He was the original architect of Legistar, beginning in the 1990s, and spent many years designing the legislative workflows and data structures used to manage government meetings, agendas, motions, votes, and public records.
He founded GovData to apply that experience to the fragmented world of local-government information. Darius designed and built the platform's ingestion systems, normalization framework, legislative decision graph, public portal, and AI-ready data infrastructure.
His advantage goes beyond collecting public documents. He understands how government decisions are introduced, amended, debated, voted upon, published, implemented, and preserved over time.
Generic scraping can collect pages. Generic AI can summarize text. GovData reconstructs the decision itself.
Why GovData fits SAIF
GovData turns responsible-AI principles into deployable infrastructure for a consequential, fragmented, and globally important information domain.
Operationalize responsible deployment
GovData provides the evidence, chronology, monitoring, and controls required when AI systems interpret government actions for regulated or high-impact use cases.
Make civic answers verifiable
Every conclusion can be traced through the decision chain to authoritative public records rather than accepted solely because a model sounds confident.
Show why the AI reached its conclusion
GovData preserves the records, relationships, chronology, outcome logic, and conflicting evidence behind an answer so users can inspect how the conclusion was reached.
Give consequential users better evidence
Businesses, governments, regulators, researchers, journalists, and the public can make decisions using verified outcomes and primary-source audit trails.
Domain-specific model evaluation
GovData can test whether AI systems correctly reconstruct multi-stage government decisions, identify final outcomes, cite authoritative evidence, recognize superseded records, and abstain when the public record is incomplete or contradictory.
GovData can also serve as enabling infrastructure for civic-intelligence applications, including other companies in the broader responsible-AI ecosystem. The objective is not to replace every application, but to provide the normalized evidence and verification layer those applications need to produce more reliable results.
From national dataset to trusted AI infrastructure
GovData is seeking SAIF's early-stage investment and strategic guidance as part of the company's broader seed financing.
Launch GovData Verify
- Production verification API
- Agent and MCP-compatible interfaces
- Evidence packages and audit trails
- Conflict, supersession, and completeness checks
- Qualified abstention and outcome confidence scoring
Release civic-AI benchmarks
- Hallucination and provenance tests
- Multi-document temporal reasoning
- Final-outcome verification
- Public and private evaluation datasets
Recruit design partners
- AI and agent developers
- Compliance and regulatory platforms
- Newsrooms and research organizations
- Government and civic-technology teams
SAIF's value extends beyond capital. Geoff Ralston's experience helping deeply technical founders find product-market fit, sharpen their story, build high-value networks, and prepare for the next financing stage would be particularly valuable as GovData turns a difficult national data asset into a focused responsible-AI company.
AI should not guess what a government decided
GovData is building the evidence and safeguard layer that lets AI systems retrieve the right records, reconstruct the full decision, explain the evidence chain, cite authoritative sources, and abstain when the record does not support a reliable answer.
The Opportunity
GovData already has the national-scale data foundation, historical depth, legislative model, and operating pipeline.
The next step is to package that foundation into verification, evaluation, and monitoring infrastructure for responsible AI.
We are seeking an investment partner who recognizes that trustworthy AI depends on trustworthy underlying evidence.
Thank you for your consideration.
I built the legislative model that preserves the history of government decisions. Now I am building the infrastructure that allows AI to interpret those decisions accurately, transparently, and at global scale.