Prospecting & CRM

    AI sales automation

    Sales automation should help teams prioritize, personalize and follow up better. It should not send more generic messages.

    01

    Lead scoring based on business signals

    02

    Personalized drafts with human validation

    03

    More reliable CRM follow-up

    Method

    Simple.
    Measurable.

    Step 1

    Define offer, ICP, sectors and qualification criteria

    Step 2

    Design sourcing, scoring, drafting and validation workflow

    Step 3

    Set up tracking and anti-spam rules

    Proof and context

    Conversion and pipeline examples are project benchmarks, not guarantees. Performance depends on offer, market and data quality.

    Related pages
    AI auditAI training
    Typical cases

    Real situations.

    01

    Lead scoring

    Context

    High inbound volume with manual prioritization and incomplete CRM data.

    Evidence to collect

    Metrics to monitor: reply rate, follow-up delay, CRM completeness, and opportunity quality.

    02

    Personalized drafts

    Context

    AI prepares sales drafts that are reviewed before sending.

    Evidence to collect

    Guardrail: no message is sent without human validation and every sequence keeps a CRM trace.

    Questions

    Are messages sent automatically?

    The recommended setup is semi-automated: AI prepares, scores and drafts, then human validation protects your brand.

    Can it connect to LinkedIn, Malt or job boards?

    Yes when APIs and terms allow it. Otherwise we use controlled research, CRM tracking and assisted manual actions.

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