Good requests wait in the wrong inbox
Response time depends on a person noticing a form, email or marketplace notification and forwarding it manually.
Lead operations
AI lead qualification automation
Turn fragmented inbound requests into structured, reviewable opportunities with source attribution, duplicate checks, fit reasoning and a responsible owner.
Start with one bounded workflow. No automatic actions are added without explicit rules and ownership.
Where work gets stuck
Problems worth fixing before adding another tool
We map the current route, the system of record, exceptions and decision owners before choosing AI, automation or custom code.
Qualification criteria stay implicit
Budget, stack, urgency and strategic fit are judged differently by each person, so priority changes from day to day.
CRM context arrives incomplete
A lead may be created without source, original request, match reasons or the next action needed from the owner.
Delivery path
From a real workflow to a controlled system
The smallest useful phase comes first. Each phase produces something reviewable and can stop without committing to a broad rollout.
Define fit
Turn your commercial criteria into required fields, exclusions and review rules.
Connect permitted sources
Use official APIs, forms, webhooks or approved mailbox access with source attribution.
Evaluate a sample
Compare system scores with human decisions and document false positives and misses.
Operate safely
Add queues, thresholds, alerts and manual approval before enabling recurring monitoring.
Expected outcomes
What the team should be able to observe
One normalized intake
Permitted sources enter a common model with clear provenance and duplicate protection.
Explainable prioritization
Rules and AI analysis produce a score with visible reasons instead of an unexplained label.
Human-approved response
The owner receives the context, recommended next step and draft while retaining control over every external reply.
Typical deliverables
What is handed over
- Lead data model, qualification policy and exclusion rules
- Source connectors, normalization and deduplication
- Explainable scoring and human review queue
- CRM routing, notifications and response-draft handoff
- Monitoring, usage controls and operating documentation
FAQ
Questions before scoping
Does AI submit proposals or send replies automatically?
No. The default design prepares context and a draft, while a person reviews and sends every proposal or message.
Can the scoring match our technical stack and minimum project fit?
Yes. We combine deterministic exclusions with AI analysis for ambiguous requirements, then show the reasons behind the score.
Can we start without connecting every source?
Yes. A pilot should begin with one reliable source and a manual check so the qualification policy can be corrected before expansion.
Project fit
Describe one workflow that is slowing the team down
We will identify the data, integrations, exceptions and smallest useful validation before estimating implementation.