Lead processing · Production

Beside AI receptionist helps Matthew Fernandez capture every inquiry and reclaim hours weekly in commercial real estate

The problem

Matthew Fernandez's commercial real estate firm handled 300–500 calls per week through a largely manual, error-prone process — hand-written notes, inconsistent CRM logging in HubSpot, and no reliable fallback for missed calls — resulting in lost leads, missing context, and time wasted reconstructing conversations.

First attempt

HubSpot was the existing CRM but without consistent and complete logging it often failed to reflect reality, and voicemail rarely substituted for missed calls since many callers simply don't leave one.

Workflow diagram · grounded in source
1
Inbound call or text arrives
trigger
“A call comes in while Matthew is juggling multiple threads—clients, agents, listings, negotiations, and internal coordination”
2
AI answers and captures details
ai_action
“Beside acts as an intake layer. It answers when Matthew can't, captures key details even if a caller drops quickly, and preserves enough context to make the next step efficient”
3
Text agent responds to listing inquiries
ai_action
“When a prospect texts a number listed on a property, Beside can respond automatically, confirm a showing window, and in some cases add it directly to the calendar—capturing leads without slowing everything else down”
4
Summaries and transcripts generated
output
“summaries that preserve intent and tone, full transcripts that eliminate ambiguity, voice recordings for extra clarity”
5
Searchable memory cross-references calls
ai_action
“using Beside as an internal memory layer to connect opportunities across conversations. If someone calls about selling an office building, he can quickly ask Beside who he has previously spoken to about buying similar properties”
Reported outcome

Beside changed the system from manual memory to automatic capture and searchable accountability, delivering summaries, transcripts, voice recordings, and automated text responses — resulting in fewer lost opportunities, less manual logging, faster turnaround, and hours reclaimed weekly.

Reported metrics
Weekly call volume handled300 to 500 calls a week
Lost opportunities from missed calls (prior state)all the time
Fewer lost opportunitiesFewer lost opportunities
Manual logging reducedLess manual logging
Reported stack
BesideHubSpot
Source
https://www.beside.com/customers/ai-receptionist-for-commercial-real-estate-how-beside-helps-matthew-fernandez-capture-every-inquiry-stay-accountable-and-reclaim-hours-each-week
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Frequently asked questions

What did this team achieve with this AI workflow?

Beside changed the system from manual memory to automatic capture and searchable accountability, delivering summaries, transcripts, voice recordings, and automated text responses — resulting in fewer lost opportunitie…

What tools did this team use?

Beside, HubSpot.

What results were reported?

Weekly call volume handled: 300 to 500 calls a week; Lost opportunities from missed calls (prior state): all the time; Fewer lost opportunities: Fewer lost opportunities; Manual logging reduced: Less manual logging (source-reported, not independently verified).

What failed first in this deployment?

HubSpot was the existing CRM but without consistent and complete logging it often failed to reflect reality, and voicemail rarely substituted for missed calls since many callers simply don't leave one.

How is this lead processing AI workflow structured?

Inbound call or text arrives → AI answers and captures details → Text agent responds to listing inquiries → Summaries and transcripts generated → Searchable memory cross-references calls.

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