Fully-automated outbound engine: 4× faster sales cycles

The problem:

BaseClaims sells home services across the US. Their main growth channel was referrals — high quality, but:

• •unpredictable
• •hard to scale
• •hard to control by job type / asset profile

They tried to buy volume from lead providers, but that introduced a different set of issues.

Their current approach:

• •Pay per lead
• •Run a first discovery call to figure out fit
• •Compete with multiple vendors who received the exact same lead

The consequences:

• •Low-quality, irrelevant leads (wrong damage types, wrong asset types)
• •Everyone has access to the same leads, so it becomes a race + price pressure
• •Discovery calls became a filter step — time-consuming and margin-killing
• •Too much time spent on leads outside their focus (e.g., specific asset types like low-rise buildings, and specific damages like water damage)
• •No trust built upfront: the first interaction felt like a pitch

Net: BaseClaims was paying twice — with cash and with time.

The goal wasn't "send more outreach." The goal was: build a predictable channel that finds the right properties, earns attention with real value, and only hands off qualified prospects to humans.

Here's the path we took:

1. Monitor property-level signals to identify intent early

We built a monitoring layer that watches key signals at the property level — the triggers that correlate with real demand.

Instead of buying commoditized leads that every competitor gets, BaseClaims now sources demand from signals they can own operationally.

2. Find + validate owner contact details

Once a property is flagged, the system:

• •identifies the likely owner / decision maker
• •finds email + phone
• •validates contact info before any outreach

This eliminates "spray and pray" and reduces wasted sequences.

3. Lead with value to build trust (not a pitch)

Rather than starting with "Want a quote?", the system offers practical tools that help the homeowner immediately:

• •damage reporting tool
• •inventory logger (contents + losses)
• •image analysis to estimate damages
• •form analysis extractor to prepopulate claim data

So the first touch feels like help, not sales — and trust builds before a call.

4. Qualify using real engagement + fit filters

The system tracks engagement (opens/clicks/tool usage/form completion) and filters for:

• •intent signals
• •fit with BaseClaims' focus areas (damage type + asset type)

Only engaged, relevant prospects get escalated.

5. Route qualified opportunities to vetted specialists

Finally, we added the "last mile" layer:

• •vetted specialists call with context from the homeowner's tool outputs
• •only high-quality opportunities move forward

Humans become closers — not filters.