The answer was already in your data. We built the way to reach it.
Assessor cards. Permits. Zoning codes. Water meter readings. Meeting minutes. Utility filings. Regulatory dockets across five states. Inspection records going back decades. It all exists. It sits in filing cabinets, siloed databases, and scanned PDFs that no one reads systematically.
The information was always there. The intelligence was not.
ArtifexAI ingests it all — links records to parcels, links parcels to geography, links geography to policy. Then it runs mathematical models across the whole and lets anyone ask questions in plain English. Not guesses — verified computation on your own data.
Every parcel classified by resident, absentee, LLC, trust, or seasonal owner.
Heating fuel, building materials, and assessed value combined into an energy risk score for every parcel.
Hedonic pricing models predict what each property should be worth. Divergences flagged by inspector, neighborhood, type.
ADU feasibility scored by physical capacity, financial viability, infrastructure, and ownership.
Integrity engine audits every card for math errors, phantom renovations, ghost verifications.
Digitized bylaws computationally applied to every parcel.
Teardown tracker identifies completed and pipeline demolitions.
Cross-reference bedroom counts, septic permits, ownership, and listing activity.
Meeting transcripts become structured intelligence: who spoke, what they said, how sentiment breaks down, and what property they own. Ask "what are residents saying about affordable housing?" and get 62 mentions across 4 topic clusters with named speakers, their property stakes, and the trajectory of the debate. Look up any speaker and get their address, assessment data, meeting history, and how their claims hold up against the numbers.
Planning board transcripts, zoning board decisions, select board minutes, committee reports — ingested, indexed, and cross-referenced with property data. Not just searchable. Structured into topic clusters with temporal patterns, sentiment analysis, and speaker identification.
For New York State, we analyzed 19,000+ utility commission docket filings across five states to benchmark LMI electrification policy. Stakeholder sentiment, funding mechanisms, cost-shifting evidence — extracted from regulatory proceedings no human team could read in full.
Cross-reference real-time meter data with property records, septic permits, and irrigation registrations. Bayesian models score the probability of non-compliance. We identified hundreds of unregistered irrigation systems from water usage patterns alone.
Model residential exemption scenarios and see the tax impact on every parcel. Simulate ADU adoption rates and project revenue. Run a geopolitical energy scenario and identify which neighborhoods lose heat first. The digital twin runs the counterfactual before you make the decision.
The system searched 595 indexed speakers across 202 topic clusters. It found 62 mentions across 4 major debate threads, identified the key contested projects, mapped sentiment patterns, and surfaced the core finding: near-universal rhetorical support paired with fierce implementation opposition on every specific site.
The system identified the most vocal housing critic — 40 recorded topics across 22 issues — found her property, mapped all 52 parcels on her street, located the Town-owned development site she opposes, and profiled the neighborhood: modest ranches and capes, median $626K, 91% absentee-owned.
Cross-referencing her meeting statements against actual property data, the system found that every home on her street — including hers — violates the density requirements she invokes. She defends "neighborhood character" on a road where 91% of homes sit empty most of the year. The system flagged where she is right (scale, pedestrian safety) and where the data contradicts her claims.
The system analyzed every lot size, basement square footage, building footprint, and ownership status. It found 19 homes with convertible basements over 600 sf, 20 lots large enough for detached cottages, and 29 absentee-owned properties sitting empty most of the year.
The system ranked every property by conversion feasibility, modeled cost per unit ($45K for basements vs. $200K+ for new construction), identified the 5 best candidates, designed a demonstration cottage, and allocated $40K for replicable program infrastructure.
No consultant. No RFP. No six-month study. Community sentiment connected to property data connected to policy design — in real time, from the town's own records.
The system found 37 mentions on water and sewer infrastructure (the dominant issue), 12 on budget and debt, 7 on grinder pump equity, and 9 on energy and solar. It identified the most active voices, surfaced the core tension: a sewer project ballooning from $300M to $650M with only 17% of homes connected, and mapped it against actual energy vulnerability by fuel type and neighborhood.
The system identified 1,056 oil-heated single-family homes facing dual infrastructure costs. It ranked neighborhoods by squeeze intensity: the town center had 431 oil homes (41% of the total) with the lowest median values. It then isolated 321 modest homes under $800K with vulnerability scores above 60 — the households where costs genuinely hurt.
The system mapped all 57 parcels on a single road and produced a house-by-house energy portrait: 8 oil-heated homes (avg. built 1941, vulnerability score 78) alongside 28 gas-heated homes (avg. built 1968, vulnerability 50). It identified the starkest contrast: a 1924 home with Fair condition throughout, oil heat, zero permits in 45 years of ownership — three doors from a 2014 gas-heated home worth twice as much.
The system designed a four-tier program: $195K for full oil-to-heat-pump conversions on 4 long-term resident homes (prioritized by vulnerability and tenure), $100K for weatherization on 3 gas homes with ancient building envelopes, $60K for 2 subsidized STR conversions tied to permit compliance, and $145K for monitoring, electrical panel upgrades, and a replication toolkit.
The longest-tenured resident — 46 years, zero renovations, Fair condition throughout, oil boiler likely original — goes first. Not the LLC. Not the STR operator. The person who has been there longest and has the least. Energy cost drops from $2,855 to $1,400. A street becomes the story. When town leadership debates scaling town-wide, they drive down one road and see the future.
The system found 83 mentions across 6 active topic clusters spanning 25+ meetings. It surfaced the headline: a sewer project originally estimated at $300M has ballooned to $650M+ with only 900 of 5,300 planned connections complete — 17% of the 2030 target. It identified grinder pump equity fights, nitrogen loading regulation battles, regional capacity diversions, and a PFAS-contaminated drinking water crisis running in parallel.
The system filtered 7,733 parcels down to 888 homes on interior R40 lots that are over-assessed, non-conforming, and still on septic. Total assessed value: $1.52 billion. Median over-assessment: 176% above model-predicted value. More than half have never had a verified interior inspection.
The system ranked all 888 homes by combined pain — tax distortion, construction age, vulnerability score, ownership tenure — and identified the town center as ground zero: 54 resident-owned homes in 1960s subdivisions, median assessed $558K, over-assessed by 136%, on 10,000 sf lots with 60-year-old septic systems. Not one resident from these streets has ever spoken at a board meeting.
The system designed a three-phase construction plan: 12 connections on two parallel streets sharing a trunk line corridor ($780K), 8 connections completing an adjacent street ($520K), and 10 connections on the longest street's resident core ($700K). It sequenced by tenure — the first shovel goes at a home owned by the same person since 1962. Sixty-three years waiting.
The most vocal infrastructure critic has made 209 public appearances. The homeowners on these four streets — the most over-taxed, most under-served, most modestly-housed year-round residents in town — have made zero. This plan speaks for them with data. Not one person from these streets has ever attended a board meeting. The system gave them a voice anyway.
The system scraped active listings from Airbnb, VRBO, and other platforms, then used computer vision and address matching to link each listing to its actual parcel in the assessor database. It matched listing photos to property card images, cross-referenced bedroom counts against septic permits, and flagged ownership type — resident, LLC, trust, absentee. Out of hundreds of listings, it identified 43 operating with no valid permit or with expired, rejected, or stopped registrations.
The system surfaced a waterfront property whose permit application had been rejected by the Health Agent months earlier — record status: Stopped. The owner signed an affidavit acknowledging $200/day fines for violations. The Airbnb listing was still active. Guest reviews proved continued operation for 10 months after rejection, with names, dates, and trip details documenting paying guests in an uninspected property.
The system found a second category: properties operating under pre-existing bed-and-breakfast licenses that predate the STR regulation. One had a 2017 B&B license, an active food establishment permit, 32 reviews, and zero STR registration. Operating through a trust. The owner knew they were running a commercial hospitality business — they just never registered under the new rules. Two different violation types, both invisible without cross-referencing permits, listings, and property records.
The system calculated exposure across all 43 confirmed violations: permit fees, room excise tax, inspection charges, and statutory fines. It cross-referenced bedroom counts against septic capacity — 14 of 21 LLC-owned properties had bedroom/septic mismatches, meaning the listed occupancy exceeded what the property was designed to handle. That is both a revenue problem and a public health problem.
A health agent rejects a permit. The applicant keeps operating. Without automated monitoring, nobody follows up — for months, sometimes years. The system watches continuously: matching new listings to parcels, checking permit status, flagging guest reviews that prove occupancy. One click on the dashboard gives the enforcement officer the rejected application, the active listing, the reviews, the assessor card, the AI photo match, and the fine calculation. The entire case, ready to send.
Housing analysis, assessment integrity, STR compliance, energy planning, and policy simulation — from the data you already collect.
Target LMI investments at individual homes instead of census tracts. Measure reform outcomes with real data at the resolution where policy happens.
Military housing requirements, BAH market modeling, infrastructure condition assessment across privatized housing portfolios.
Anything that touches your data. "Where can we add housing?" "Are our assessments fair?" "Who spoke against this project and what property do they own?" "If we adopt a 25% residential exemption, what happens to every parcel?" "Which homes should get heat pumps first?" "Where should we dig sewer next?" The system cross-references parcels, meeting transcripts, permits, zoning, ownership, and utility data — so the question can span any combination of those.
That is the core use case. The system runs policy simulations: model exemption scenarios and see the tax impact on every parcel, project ADU adoption rates and revenue, sequence infrastructure investments by equity impact, and design grant programs optimized to a specific budget. It connects community sentiment to property data to policy design in one conversation.
Every answer runs through mathematical models underneath: hedonic pricing, assessment ratio analysis, feasibility scoring, displacement tracking, clustering algorithms. The conversational interface makes it easy to ask questions in plain English. But the answers come from verified computation on your actual data — not generated text.
Yes. You ask questions in plain English. "Show me all properties on Elm Street" works. "Which neighborhoods have the worst assessment quality" works. The system handles the data joins, the spatial queries, and the model runs. If your staff can type a question, they can use it.
We start with publicly available records: assessor databases, GIS parcels, zoning bylaws, and permit records. Most municipalities already publish this. For deeper analysis we can integrate CAMA exports, water meter data, meeting minutes, and inspection records — all data you already collect. No new data gathering needed.
A consultant takes months and delivers a static report. This is a living system you can query any time. The housing question you ask today becomes the STR compliance question tomorrow and the energy targeting question next month — same data, same platform, no new engagement. And when your data updates, the answers update with it.
We ingest data from standard municipal formats — CAMA systems like Vision, ProVal, and AssessPro, GIS exports, OpenGov records, utility billing systems, and PDF meeting minutes. We do not replace your existing systems. We unify what they already contain.
Two to three weeks for a town under 10,000 parcels. The first week is data ingestion and parcel linking. The second is model calibration and quality assurance. By week three, you are asking questions.
Yes. We run a live demonstration on real municipal data — real parcels, real assessments, real meeting transcripts. Bring your hardest question. Book a 30-minute demo.
15 minutes. Live data. No slides.
See it live