GTM Automation Tool

Give it a name, an address, and a company. In under 60 seconds you get a scored lead, specific pain points, and a cold email grounded in real data — ready to review and send.

An SDR prospecting property management companies needs to answer five questions before writing a single email: Is this a real rental market? Is this building big enough to matter? Is the company growing or struggling? Do residents have complaints? And is now a good time to reach out?

Doing that manually takes 25–45 minutes per lead. At 20 leads a day, most of the workday is gone before anyone picks up the phone. And because it's expensive, SDRs end up cherry-picking by instinct — a mid-size apartment complex in a tight Boston rental market gets the same treatment as a single-family home in a rural suburb.

This tool replaces all of that lookup work.

flowchart TD
 subgraph row1[" "]
 direction LR
 A["Name + Company<br/>+ Address"] --> B["Geocode<br/>4-level fallback"] --> C["9 APIs<br/>in parallel"]
 end
 
 subgraph row2[" "]
 direction LR
 D["Building Type<br/>Classification"] --> E["Google Places<br/>(targeted search)"] --> F["Score<br/>Demand · Friction<br/>Scale · Opportunity"]
 end
 
 subgraph row3[" "]
 direction LR
 G["Pain Points<br/>rules + LLM"] --> H["Cold Email<br/>arc + LLM"]
 end
 
 row1 --> row2 --> row3

1. The address gets geocoded (Intellipins → Nominatim → US Census as fallbacks).
2. Nine APIs fire in parallel: US Census, FRED vacancy rates, WalkScore, OpenStreetMap, Intellipins parcel data, Open-Meteo climate, FBI crime, NewsAPI, and Wikipedia.
3. Building type is classified from OSM and Intellipins — then Google Places searches with the right strategy (apartment complexes get searched by address to find tenant reviews; commercial properties get searched by company name to find the management company's rating).
4. A deterministic scoring engine produces four sub-scores and a composite 0–100 Lead Score with grade A–F. No LLM touches the score.
5. A rule engine fires pain points. An LLM adds up to two nuanced insights. A second LLM writes a cold email in one of five story arcs — arc selection is also deterministic.

cp .env.example .env # fill in your API keys
pip install -r requirements.txt
source venv/bin/activate
uvicorn backend.main:app --reload
# open frontend/index.html in your browser

Backend runs at http://localhost:8000.

For re-scoring: body is {"steps": "scoring,pain_points,outreach", "llm_provider": "anthropic"}.

pie title Lead Score Composition
 "Demand — rental market pressure" : 30
 "Opportunity — timing & urgency signals" : 30
 "Scale — property footprint" : 20
 "Friction — operational difficulty" : 20

Demand asks: how overwhelmed is this property manager with inbound activity right now? Opportunity asks: is there a specific signal that makes this company likely to act — a bad Google rating, a news trigger, a Wikipedia-established brand? Scale asks: is this a 20-floor apartment complex or a single-family rental? Friction asks: how hard is this property to operate? High friction is good for us — it means the manager needs automation.

Scoring is fully deterministic and uses calibrated ceilings (e.g., Walk Score ceiling is 80, not 100 — "Very Walkable" should max the component, not get penalized against a ceiling no real lead ever hits) and shaped curves to push weak leads below 50 and strong leads above 80.

A 20-floor apartment complex at 40 E Oak St in the Gold Coast. Walk Score 99. Chicago winters (62.7 cm snow/yr, 178 rain days, -24°C lows). 54.4% renter share. Population 2.74M.

Arc selected: operational_friction · Subject: Midnight Maintenance Calls

Your team deals with around 2 feet of snow and ~178 rainy days per year, which means constant maintenance issues. At Inland American Real Estate, a burst pipe during a cold snap can lead to a flooded lobby and numerous resident calls. EliseAI keeps residents informed automatically so your team can focus on the fix, not the calls.

A single-family house at 6839 E Montecito Ave. Google rating 4.8★ (100 reviews). 0.3 cm snow/year. 33.4% renter share. Building type score: 0.20.

The 4.8-star rating is the tell. This operator is running their property well without automation. Opportunity scores 0 — the high rating means no low_rating signal, and there's no news or Wikipedia presence to fire on. There's no pain to sell into. The model correctly identifies this as a skip.

An interactive visual version of all docs is at frontend/docs.html — open it in a browser for animated pipeline diagrams, the scoring chart, and hover-detail API cards.

Why deterministic scoring, not an LLM judge? Reproducibility. The same address always produces the same score. You can tune weights, audit decisions, and A/B test arc selection without worrying about prompt drift.

Why partial-weight scoring? Missing an API response shouldn't penalize a lead. If OSM fails, the scale score runs on what it has. Every sub-score ships with an available_weight so you know how much data backed the number.

Why two LLM calls? Zero means generic emails. Two (nuanced pain points → arc-aware email) means every email is grounded in the actual enrichment data for that property. The deterministic scoring layer constrains the LLM to numbers the system already verified — it can't make things up.