A booking agent designed around the real conversation, not just the calendar
Intake, qualification logic, scheduling paths, CRM update and a defined human escalation.
Production AI systems for sales & operations.
Drebot AI maps, builds and improves reliable workflows for lead qualification, booking, CRM operations and sales intelligence — with monitoring, evaluation and human handoff built in.
Founder-led · Built for production · Measured in the workflow
lead→ qualification→ booking→ CRM→ human escalation
You probably recognise at least two of these.
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Leads get routed by hand
Someone reads the inbox, decides who follows up, and pastes context into the CRM. It works until volume moves.
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Follow-up arrives late
The intent was there on Tuesday. The reply goes out on Friday, to a colder person.
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The CRM stops matching reality
Stages are filled in after the fact, so the record describes what happened, not what to do next.
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Booking breaks on exceptions
The calendar handles the common case. Reschedules, edge conditions and unusual requests fall back to a person.
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Visibility comes too late
Managers learn about a stalled deal at the review, not at the moment it stalled.
Five workflows, not a catalogue of agents.
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Lead qualification
Fragmented signals turned into one visible qualification flow, with the reasoning behind each decision kept attached.
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Booking
Scheduling designed around the conversation and its exception paths, not only around free slots.
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CRM operations
The next action stays connected to the context that produced it.
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Sales intelligence
Reporting that answers a specific operating question, with the evaluation layer behind it.
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Evaluations
Checks that tell you when the system is drifting, before a customer does.
Real systems, with their status stated.
Turning fragmented lead signals into a visible qualification flow
Inbound and outbound signals collected into one flow with observable state.
Sales intelligence with an evaluation layer behind it
Reporting built alongside the checks that keep it honest.
Labelled honestly rather than counted as case studies.
BLAGO agent
WordPress SEO panel
Procurement platform
The part that separates a system from a demo.
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Monitoring
The workflow state is observable while it runs, not reconstructed afterwards.
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Idempotency
A retry does not create a second lead, a second booking or a second message.
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Evaluation
Outputs are checked against what the process actually needs.
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Human escalation
The system knows the edge of its own competence and hands over there.
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Measurement
Success is defined in the workflow before anything is built.
How a project moves from problem to improvement.
Discovery
We name the decisions the system has to support, and the ones it should stay out of.
Process mapping
Where leads, context or time fall through the gaps between people and tools.
Implementation
The smallest reliable system that removes the bottleneck, built into the real process.
Measurement
Agreed signals, watched in the workflow rather than in a slide.
30-day optimization
Edge cases, handoffs and the parts that only surface under real load.
Steven Drebot
Hands-on implementation partner for sales and operations workflows. The person who maps the process is the person who builds it and the person who watches it run in production.
Is this an agency or one person?
Founder-led. You talk to the person who maps the workflow and builds it.
Do you replace the sales team?
No. Automation goes where the workflow is clear. Human escalation stays where judgment still matters.
What happens on a discovery call?
We look at one workflow, identify where it leaks, and decide together whether AI is the right layer to add. If it is not, that is a valid outcome.
Bring the workflow. Leave with a clearer system.
In a focused discovery call we identify where leads, context or time fall through the gaps, and decide whether AI is the right layer to add.