LO GPT-mortgage broker AI
AI-powered mortgage guideline assistant for brokers

An assistant for understanding mortgage guidelines and loan products.
Explain the differences between FHA and VA loans.
Produce a chart comparing interested party contributions for each loan type.
How do Fannie Mae guidelines differ from Freddie Mac?
Can you identify the conventional loan limit by county?
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LO GPT — Mortgage-focused Assistive GPT for Loan Originators
LO GPT is a specialized assistant designed to support mortgage professionals by providing accurate, guideline-driven answers and practical tools for FHA, VA, USDA, and conventional loan products. It is intentionally scoped to use a curated set of reference documents and resources supplied by the system/creator (e.g., lender guides, program matrices, and PDF policy manuals). LO GPT's design purpose is to increase productivity and reduce compliance risk by translating complex underwriting rules into clear, actionable guidance and reusable artifacts (checklists, sample calculations, submission notes). LO GPT is not a general web-search agent — it does not accept outside resources from other users — and it prioritizes consistency with the curated materials it was provisioned with. Key design elements: 1) Guideline-driven reasoning: LO GPT maps user questions to the appropriate sections of mortgage program guides (e.g., income calculation rules, allowable compensating factors, occupancy definitions) and synthesizes concise, stepwise guidance. Example: When asked how to treat non-taxable income for qualifying under FHA, LO GPT cites the relevant validation steps, required documentation, and how to annualizeLO GPT introduction and functions irregular pay. 2) Scenario-based support: LO GPT converts lender rules into practical, scenario-specific workflows — for originators, processors, and underwriters. Example: For a borrower with multiple rental units, LO GPT shows how to treat rental income (including leases, tax schedules, vacancy factors) under conventional guidelines and how to document it for AUS acceptance. 3) Reusable artifacts and templates: LO GPT produces checklists, sample AUS submission narratives, verification request templates, and calculation worksheets (DTI, LTV, residual income) to reduce back-and-forth and speed file delivery to underwriting. Example: Given a refinance with a non-occupant co-borrower, LO GPT can draft a submission memo that highlights occupancy statements, seasoning requirements, and asset verification items. Operational constraints and safety: LO GPT provides guidance consistent with its curated materials and flags when a precise answer depends on lender overlays or state-specific rules not present in the dataset. It offers conservative recommendations and suggests what documentation to collect when there’s any ambiguity.
Primary Functions and Practical Applications
Guideline lookup and interpretation
Example
An originator asks whether rental income from a new lease signed 15 days ago can be used for qualifying under Fannie Mae rules. LO GPT reviews the curated conventional guide language and returns the correct seasoning/lease requirements, documentation needed (executed lease, proof of deposit or canceled checks, tax returns if applicable), and how to treat partial months in income calculations.
Scenario
Loan officer preparing a submission for an investor who recently signed a lease. LO GPT provides a short checklist: lease document, proof of receipt of rent, calculation method for monthly rental income, and any AUS-specific entry that must be adjusted to reflect the new lease start date.
Loan scenario modeling and calculations
Example
A broker provides borrower income, assets, debts, and desired loan terms. LO GPT produces detailed calculations (monthly stable income, total monthly obligations, housing payment, DTI, LTV/CLTV) and explains if the file appears eligible for automated approval, manual underwrite, or likely denial based on the curated underwriting rules.
Scenario
A client provides: two W-2 wages (one hourly with variable overtime), one year of 1099 consulting income, and assets. LO GPT annualizes overtime using the correct averaging method per the guide, explains treatment of 1099 self-employment income (profit/ loss adjustment), calculates qualifying income and DTI, and lists what tax-docs and profit & loss statements to collect for AUS/underwriter review.
Document checklists, submission memos, and compliance support
Example
For a VA IRRRL with a borrower who has a recent late mortgage payment but otherwise meets seasoning, LO GPT drafts a submission memo that highlights the VA-specific seasoning requirements, the lender's overlay triggers to check, the exact documentation to include (rate/term comparison, evidence of current appraisal if required), and suggested compensating factors to emphasize in the file.
Scenario
A broker packages a VA streamline refinance where the AUS is refer/eligible only if compensating factors are documented. LO GPT produces a one-page submission memo for the underwriter: program citation, borrower credit snapshot, compensating factors (e.g., residual income, reserves), and an itemized document checklist to reduce request for conditions.
Who Benefits Most from LO GPT
Mortgage Brokers and Loan Officers
Primary users who originate loans and need quick, accurate interpretations of program rules, eligibility checks, and pre-submission packaging. They benefit because LO GPT reduces time spent hunting through lengthy guides, helps prepare clean submissions to lenders, increases first-pass approval rates, and creates standardized documentation (disclosures checklists, AUS input notes). Example day-to-day benefits: fast pre-qualification conversations with accurate DTI/LTV scenarios, tailored checklist generation for each loan type, and immediate guidance on acceptable documentation for unusual income sources.
Processors, Underwriters, and Compliance/Operations Staff
Operational teams that enforce guideline conformity and need consistent rule application for quality control and audits. Processors use LO GPT to generate documentation checklists and pre-underwrite checklists, reducing missing-doc cycles. Underwriters use it to prepare decision memos and ensure consistent interpretation of overlays and AUS findings. Compliance teams use LO GPT-generated summaries to help train staff on policy changes and to produce audit-ready narratives showing how a file meets specific guideline citations. Example: a processor receives a file with multiple non-traditional income sources; LO GPT provides a document list, stepwise validation flow, and specific guide citations so the processor can assemble a compliant package for underwriting.
How to use LO GPT
Visit aichatonline.org for a free trial without login, also no need for ChatGPT Plus.
Open the site to try LO GPT immediately — no account or ChatGPT Plus subscription required. This gives you instant access to the interface and a hands-on feel for the tool.
Prepare required materials
Gather your loan files, borrower data, and any internal guideline PDFs you’re authorized to use. LO GPT works best when you can reference specific documents or clearly state product/agency details (FHA, VA, USDA, Conventional).
Ask targeted, document-backed questions
Frame queries that reference guideline sections, loan types, or scenarios (example: 'FHA manual: income from partner on rental property — allowable?'). Provide facts (credit score, AUS findings, asset types) for precise, actionable answers.
Use outputs as advisory support — validate before relying
LO GPT provides guideline interpretation, checklist items, and example workflows. Treat responses as informed guidance: cross-check with originating guideline PDFs, AUS findings, and your internal compliance team before final loan decisions.
Optimize workflow with iterative prompts
Refine questions (add missing facts, attach guideline excerpts if available) and request output formats (checklist, underwriting rationale, email draft). Save useful promptUsing LO GPT patterns and standardize them across your team for faster repeatable results.
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Common questions about LO GPT
What primary functions does LO GPT perform?
LO GPT interprets mortgage guidelines, summarizes policy excerpts, generates underwriting checklists, drafts borrower and broker communications, and produces comparison summaries of FHA/VA/USDA/Conventional rules. It’s designed to speed guideline lookup, reduce manual parsing of PDFs, and produce standardized outputs (e.g., condition lists, eligibility checks) tailored to mortgage broker workflows.
Which sources does LO GPT use and how current are they?
LO GPT relies on a curated set of reference documents and links provided by its creator (approved guideline PDFs and internal resources). It does not ingest or trust arbitrary external web pages. Currency depends on the document versions uploaded by the owner — always verify by checking the original guideline PDF dates or the agency website for the definitive, most recent rule.
How should I treat LO GPT answers in a compliance context?
Use LO GPT outputs as advisory, explanatory, and time-saving tools — not as final legal or compliance determinations. Validate any underwriting decisions against the original agency guideline PDFs, AUS findings, and your internal compliance policies. LO GPT can produce clear citations and checklist-style items to simplify that verification process.
Can LO GPT integrate with my systems or ingest my files?
Integration capabilities depend on the deployment environment. In typical setups LO GPT can accept pasted excerpts, uploaded PDFs (if the platform supports file input), or structured prompts containing loan facts. For deep integrations (LOS, CRM), consult the platform owner for API or connector options — LO GPT itself is optimized for guideline interpretation rather than full LOS automation.
What are common use cases and best-practice prompts?
Common use cases: underwriting checklist generation, eligibility screening, compensating factor analysis, pre-underwrite summaries, and email templates to conditions-clear. Best-practice prompts are specific: include loan product, credit score, DTI, AUS findings, property type, and the exact guideline excerpt you want interpreted. Ask for output format (bullet checklist, short memo, or condition list) to get usable results.





