Magnifica

Challenge

The fastest path to extracting value from public civic data is also the most corrosive one: scrape county permit and agenda records, profile the named applicants, owners, and developers behind them, and sell the resulting scores, approval-odds, and lead lists to whoever pays. That pattern degrades the public square — it converts equal-access civic truth into a private targeting advantage, and it treats the AI as the sovereign rather than the servant. The harder question is whether the same data can be engineered to increase equal-access public understanding — helping a first-time founder navigate the path to opening a business — without ever crossing into surveillance of private parties.

Solution

Built Magnifica, a local-first decision-support layer for North Texas economic activity, under an explicit, binding alignment protocol (the “Magnifica Humanitas” human-centered AI standard) encoded directly into the system’s agent context and enforced in code.

• Binding mission order — serve individuals, first-time founders, SMBs, and local operators first; serve public stewards second with de-identified aggregate friction; serve investors and lenders only through the same equal-access public outputs
• Capture/release separation — public-record civic context may be normalized internally for coherence, but public release is a separately gated act: no raw packet dumps, private-party profiles, scores, rankings, flags, lead lists, vulnerability labels, approval odds, or opportunity feeds
• Deterministic, inspectable scoring that operates over process events and friction only — never over private actors — with LLMs restricted to summarizing already-structured sourced data, never acting as a source of truth
• Provenance-first outputs that always show sources, uncertainty, as_of dates, and data age, and never present stale records as current
• Multi-jurisdiction collectors spanning Gainesville, Sherman, Denton, Denison, Collin and Grayson counties, and more — issued permits, planning/zoning guidance, EDC profiles, and public agendas — normalized into a single SQLite canonical store
• Founder-facing navigators and runbooks that map the ordered path through public desks, hearings, and approval bodies, plus de-identified aggregate friction reports that hand process bottlenecks back to public stewards
• Correction path for any named subject, treating the right to correct public-record context as a first-class feature

Impact

• Demonstrates that AI-native civic tooling can expand equal-access public truth without becoming a surveillance or lead-generation product — a working reference for principled, human-centered engineering
• Alignment principles are not a policy memo bolted on after the fact — they live in the agent context, the schema, and the deterministic scoring boundaries, and are exercised by a broad test suite and source-confidence audits
• The capture/release model lets richer internal civic context improve coherence while keeping public outputs source-bound, context-bound, and free of targeting surfaces
• Local-first and fully inspectable — Python ingestion, a canonical SQLite store, and Markdown/JSON generated as exports only — so the entire pipeline remains auditable and reversible rather than a black box