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CASE STUDY / AUTOMATION

Lead automation

A system that identifies sales signals in public sources, enriches them with useful context and feeds the results into an outbound workflow.

The aim was to turn scattered signals into practical sales work: identify relevant companies, prioritise outreach and automate the early stages through to the CRM.

Concept illustration: public data flows through a signal engine into company records and an outreach workflow.

The challenge

The right company. The right moment.

A job opening, a procurement notice or a change on a company website can point to a current need. These observations are scattered across sources. Reviewing them manually, matching them to the right company and turning them into outreach takes time. Each signal needs context and a clear next step.

How the system works

From a public signal to a lead ready for follow-up. Each stage builds on the data from the previous one.

    • Public job listings
    • Company websites
    • HILMA procurement notices
    • Other public sources

    • Data processing and matching
    • Relevant signal detection
    • Filtering
    • Scoring

    • Company information
    • Contact people
    • Additional context
    • Prioritisation

    • Outreach workflow
    • CRM integration
    • Response tracking
    • Suppression lists and follow-up

    • Leads ready for sales
    • Targeted outreach
    • Less manual work
    • New business opportunities

Under the hood

Technical implementation

Programmatic data processing, language models and workflow automation connect public signals to sales activity. Each technology has a defined responsibility.

Technical BlueprintTechnologies and responsibilities · one system

  1. Python

    Data processing

    • Source data normalisation
    • Matching companies and observations
    • Signal filtering and prioritisation

    Deduplication removes repeated company records and observations before further processing.

  2. LLM

    Information interpretation

    • Interpreting source text
    • Extracting structured information
    • Supporting automated reading and writing

    Language models help turn unstructured source material into data the system can process.

  3. Make.com

    Workflow automation

    • Workflow orchestration
    • Response processing
    • Coordinating integrations and follow-up

    Automated replies are separated for further handling. Suppression lists account for requests not to be contacted.

  4. HubSpot

    CRM

    • Company and contact records
    • Lead follow-up
    • Sales process tracking

    Company and contact enrichment, together with CRM synchronisation, provide the information needed for sales work.

The result

From signals to sales work.

The system brings prospect identification, data enrichment and follow-up into a single process. Instead of isolated observations, sales teams get prioritised information and a clear next step. Automation reduces manual data transfer and makes lead handling more systematic.

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