Signal House Labs builds software for fire investigators, tenants, homeowners, and law students — tools that structure reasoning, surface risks, and enforce standards before a decision becomes final. Woman-founded. Black-led. Based in Indianapolis.
AI-powered security deposit recovery letters grounded in state-specific statute logic. Tenants enter their situation and receive a professionally structured demand letter citing the relevant law, the violation, and the penalty provisions.
Live in Texas and Washington. Next states: Indiana, Missouri, Pennsylvania, Michigan.
Quality assurance for fire investigation reports, checked against NFPA 921 and 1033 standards for evidentiary completeness, internal consistency, and hypothesis elimination. Produces a structured Decision Record before the report leaves the investigator's desk.
Active pilot with Indianapolis Fire Department. Expansion targets identified in four additional metro regions.
Property tax assessment monitoring with comparable sales data and jurisdiction-specific filing guidance so homeowners can build and file a credible protest without hiring a consultant.
Tier 1 launch states: Texas, Florida, Indiana. Data layer: RentCast API. Four modules defined.
LSAT reasoning diagnostics that identify specific skill gaps rather than counting wrong answers. Targets study effort where it will compound.
Concept validated with LSAT students. Kernel architecture applied to test performance data.
Across fire investigation, property taxation, tenant law, and test preparation, consequential decisions are made on unstructured, unchallenged, or AI-generated information — with no quality control layer between the output and the action.
The people who bear the cost of these failures are almost never the institutions making the decisions. They are tenants who lose deposits they are legally owed. Homeowners who overpay taxes they could challenge. Investigators whose reports are never reviewed against the standards they are trained to meet.
Signal House Labs builds the infrastructure that closes these gaps — one domain at a time, on a single reusable architecture.
Three converging shifts make domain-specific judgment systems practical for the first time. Inference costs have dropped over 90% in two years, making it economically viable to run structured reasoning at scale. Context windows now accommodate full-length fire investigation reports, property tax assessments, and legal statutes in a single pass. And advances in multimodal reasoning allow systems to process documents, data, and domain logic together — not just summarize text. The regulatory and institutional demand for auditable, deterministic AI outputs is accelerating simultaneously. Signal House Labs is built for this moment.
Every Signal House product runs on the same engine: a five-stage pipeline that ingests information, identifies what matters, applies domain-specific quality standards, and produces a structured Decision Record — an auditable artifact showing what was evaluated, what rules applied, and what was recommended.
The quality control layer uses deterministic logic, not generative AI, because in high-stakes domains, repeatable and defensible matters more than creative. Seven API endpoints. Domain-configurable rule sets. One architecture, deployed across every vertical.
Each new product deployment reduces marginal cost and compounds technical defensibility. The fourth product costs less to build than the first. The tenth costs less than the fourth.
The gate layer uses rule-based logic for the quality control step. This produces auditable, repeatable, defensible outputs in environments where probabilistic answers are insufficient.
Signal House Labs was founded in 2025 by two enterprise technology veterans who spent a combined 40 years watching organizations make consequential decisions on incomplete, unchallenged information. We build the infrastructure that sits between the output and the decision — structured, auditable, domain-specific tools that strengthen professional judgment in the moments that matter most.
We are based in Indianapolis, Indiana. We are 100% women-owned and Black-led.
20+ years in enterprise IT strategy, healthcare technology, and digital transformation. Former Director of Customer Success and Senior IT Advisor at Genesys Telecommunications, serving UnitedHealthcare, Blue Shield of California, Molina Healthcare, and FEMA. Senior Technical Program Manager at UnitedHealthcare. Additionally operates a 20-year independent interior design practice.
Built and led North America’s first verticalized public-sector professional services organization at Genesys, supporting multi-state healthcare modernization initiatives.
Nearly 20 years in enterprise technology, professional services operations, and client success leadership. Former VP of Customer Success and VP of Global Professional Services Operations at Genesys, where she led global operations teams across portfolio management, service delivery, sales operations, and employee enablement. Prior to her VP roles, she served as Senior Director of Global Professional Services Operations and Director of Delivery Operations.
Earlier career includes Director of Account Management at Six Feet Up and Systems Integration and Technology Consultant at Accenture, specializing in communications and high-tech corporations.
Thesis. Signal House Labs is building judgment layer infrastructure — the quality control layer between AI-generated outputs and consequential human decisions. We believe decision integrity is a horizontal infrastructure problem, not a vertical feature, and we are proving it by deploying a single architecture across four domains simultaneously.
The markets we serve are large, fragmented, and underserved by technology. Security deposit disputes affect millions of renters annually. Property tax overassessment costs homeowners billions collectively. Fire investigation quality assurance has no existing software category. Each of these domains has clear statutory or standards frameworks that make structured, deterministic tooling possible — and each is newly addressable due to advances in AI context windows, inference economics, and multimodal reasoning.
Our founding team brings 40+ years of combined experience in enterprise technology, healthcare IT, and public-sector modernization — including Director-level and VP-level roles at Genesys Telecommunications, UnitedHealthcare, and Accenture. We have served organizations including Blue Shield of California, Molina Healthcare, FEMA, and the State of Wisconsin.
Each product proves the Signal House Kernel works in a new domain with a new user class. The fourth deployment costs less than the first. This creates compounding defensibility and declining marginal cost — a platform dynamic expressed through domain-specific products.
Whether you are a tenant who needs help, a fire department exploring quality assurance tools, an investor evaluating the judgment layer thesis, or a partner who sees an opportunity — we want to hear from you.
Tell us who you are and what you are looking for. We will route your message to the right person and respond within one business day.
Effective March 29, 2026
Signal House Labs (“we,” “our,” or “the Company”) operates signalhouselabs.com and related product websites including tenantwin.org. This policy describes what information we collect, how we use it, and your rights regarding that information.
We collect information you voluntarily provide when you contact us via email, submit a form, or use one of our products. This may include your name, email address, mailing address, and information related to your use of our services such as property details, lease information, or report data you submit for analysis.
We do not collect information from visitors who simply browse this website. We do not use tracking cookies, behavioral analytics, or third-party advertising pixels on signalhouselabs.com.
Information you provide is used solely to deliver the service you requested, respond to your inquiry, process a transaction, or improve our products. We do not sell, rent, or share your personal information with third parties for marketing purposes.
Our products process information you submit (such as lease details in TenantWin or investigation reports in FireScribe) to generate structured outputs. This information is processed to deliver your requested result and is not used to train AI models or shared with unrelated third parties.
Payments are processed through Stripe, Inc. We do not store credit card numbers or payment credentials on our servers. Stripe’s privacy policy governs the handling of your payment information.
We use a limited number of third-party services to operate our business, including Stripe for payment processing, Google Workspace for email and business operations, and Squarespace for domain management. Each of these services has its own privacy policy governing data they process on our behalf.
We retain your information only as long as necessary to fulfill the purpose for which it was collected or as required by law. You may request deletion of your personal information at any time by contacting us.
You have the right to access, correct, or delete your personal information. You have the right to know what information we hold about you. You have the right to opt out of any communications from us. To exercise any of these rights, contact us at admin@signalhouselabs.com.
Our services are not directed to individuals under the age of 18. We do not knowingly collect personal information from children.
We may update this policy from time to time. Changes will be posted on this page with an updated effective date. Continued use of our services after changes constitutes acceptance of the revised policy.
For questions about this privacy policy or your personal information, contact us at admin@signalhouselabs.com.
Signal House Labs
Indianapolis, Indiana