Production system · High-volume environment
AI Revenue Agent
Increased lead-to-purchase conversion by 29% by removing timing and execution constraints.
This system replaced a human-dependent process operating across 200k+ leads/year.
Most of the lost revenue came from timing, not demand.
Tested on 20k+ leads in controlled split vs human process
Before
- ✕Leads contacted hours later or not at all
- ✕Dependent on agent availability
- ✕Inconsistent follow-up execution
After
- ✓<5 min contact time
- ✓Autonomous execution 24/7
- ✓Deterministic follow-up on every lead
Context
Online car buying platform processing 200k+ inbound leads/year with high marketing spend and time-sensitive conversion windows.
Problem
This created a consistent gap between demand and conversion.
A significant portion of demand never converted — despite existing intent.
- ▸High marketing spend generated demand, but conversion was constrained by operations.
- ▸Leads were contacted too late to convert — or not at all.
- ▸Calls depended on human availability, creating bottlenecks at peak hours.
- ▸A large share of leads arrived outside traditional working hours.
- ▸Follow-up execution was inconsistent across the team.
Constraint
This was not solvable with a simple automation layer.
- ▸No usable APIs on the client side.
- ▸Raw data was not clean for real-time conversations.
- ▸Multi-step workflows had failure risk at every integration point.
- ▸Latency mattered — live voice interactions required sub-second decisions.
What we built
A real-time revenue system that initiates contact within minutes, understands intent, handles objections, and books the next step — end-to-end, without human intervention.
Key insight — what actually made this work
The failure was not in calling leads. It was in execution under real-world constraints. Fragmented systems, unreliable data, and unpredictable latency. We built middleware, normalized conversation data, added invisible failure handling, and optimized latency through internal accelerators.
Results
Measured against the previous human-driven workflow:
- ✓+29% conversion vs previous human-driven process
- ✓20k+ leads tested in a controlled split test over 10 days
- ✓80% of leads contacted in under 5 minutes
- ✓System now handles 90% of total production volume
- ✓Fully loaded cost: $0.093/min vs $4–8/min for human equivalent
System runs in production handling the majority of lead volume.
Business impact
This removed the operational constraint limiting revenue capture:
- ▸Increased revenue per lead without increasing acquisition spend.
- ▸Removed dependency on human availability for conversion.
- ▸Captured demand that was previously lost due to timing.
What happens if this is not fixed
- ▸Revenue lost to delayed response on every shift.
- ▸Higher cost from human dependency that does not scale.
- ▸Growth ceiling on lead volume the team can convert.
Why this matters
- —Conversion is constrained by operations, not demand.
- —Speed of response directly determines revenue.
- —Manual follow-up does not scale with lead volume.
Why this approach works where others fail
- ✓Works without requiring clean APIs.
- ✓Handles messy, real-world data from legacy systems.
- ✓Designed for failure scenarios, not just ideal flows.
- ✓Built for real-time execution, not batch processing.
Where this pattern applies
If your operation depends on speed and follow-up, this pattern applies.
Applicable wherever lead speed, timing, and fragmented systems reduce conversion.
See how this would work in your operation
We map this directly to your current workflow and show what would change.
No long sales cycle. We start with your use case.
Most teams already have this problem. Few solve it correctly.
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