Skip to content
LOGIXELTECHNOLOGIES — home
03 / 06
Recruitment case studyIn production

SignalDesk

3.2x more qualified leads without adding headcount.

SignalDesk watches the same public signals a recruiter checks by hand — job changes, hiring posts, company growth — and turns them into a ranked, enriched prospect list delivered to each consultant every morning, with outreach templates and CRM logging wired in so the only manual step left is the actual conversation.

Signal monitoringAutomatic enrichmentRanked daily listCRM logging
Client
Mid-sized staffing firm
Industry
Recruitment
Our role
Signal design, enrichment pipeline, CRM integration
Status
In production
The short version

The consultants were being paid to talk to people — and spending their best hours researching who to talk to.

The outcome

Measured after launch,not promised before it.

013.2x
qualified leadsMore relevant prospects per consultant, because the research no longer depends on how much time they have.
029
BD hours a week returnedHours that went into manual research now go into conversations.
0327%
reply rateBetter-targeted outreach, reaching people at the moment a signal makes the message relevant.
01

The situation

The client is a staffing firm with around a dozen consultant desks, each covering its own niche. Business development started every morning the same way: consultants manually scanning for job changes, hiring posts and growing companies before they could make a single call.

Manual prospecting doesn't scale past a handful of desks

Consultants at a mid-sized staffing firm spent their mornings manually checking LinkedIn for who'd changed roles, who was hiring, and who fit their desk's niche, before they could make a single call. That's manageable for one consultant covering one niche. It breaks down across a dozen desks, because the research time scales with headcount while the hours in a morning don't.

02

Before and after

Before
  • Mornings spent manually checking signals, desk by desk
  • Contact and company details looked up by hand
  • Duplicated research across consultants
  • Outreach logged in the CRM after the fact, if at all
After — SignalDesk
  • Signals monitored continuously for every niche
  • Each match enriched with contact and company detail
  • A ranked, deduplicated list waiting every morning
  • Templates and CRM logging built into the workflow
03

How it works

Workflow orchestration schedules the signal checks, an enrichment API fills in contact and company detail, and the firm's existing CRM stays the system of record for outreach.

  1. 01 · Watch

    Signal monitoring

    Scheduled checks track job changes, hiring posts and company growth — the same public signals consultants checked by hand.

  2. 02 · Enrich

    Detail filled in automatically

    Every match gets contact and company information attached, so a lead arrives ready to act on.

  3. 03 · Rank

    Scoped per desk

    Each consultant's signal profile filters and ranks leads for their niche, with duplicates removed across desks.

  4. 04 · Reach out

    Templates & CRM logging

    Outreach starts from a template and every touch is logged to the CRM automatically.

Workflow orchestrationEnrichment APICRM integrationScheduled jobs
04

What we built

We built a pipeline that does the research before the consultant logs in:

  • 01Continuous monitoring of job-change, hiring and growth signals per niche
  • 02Automatic enrichment of each match with contact and company detail
  • 03A ranked, deduplicated list delivered to each consultant every morning
  • 04Outreach templates and CRM logging wired directly into the existing workflow
05

Why we didn't build one shared lead list

The easy way

One shared firm-wide list that every consultant filters themselves.

What we did instead

Per-desk signal profiles, so every list is already relevant.

The easy way to do this is one shared, firm-wide list of signals that every consultant filters themselves — it's a simpler build and a single pipeline to maintain. It wastes the thing that actually matters here: relevance. Two consultants on different niches would spend time filtering out most of the same list. We built per-desk signal profiles instead, so each consultant's morning list is already scoped to their niche — more configuration up front, but it's the difference between a tool people actually use every morning and one they learn to ignore.
06

How we delivered

Working software early, then the full build — with the client using it at every step.

  1. Week 1

    Desk profiles

    We interviewed consultants to define what a good lead looks like for each niche.

  2. Week 2

    First morning list

    A pilot desk received its first automated, enriched list on real signals.

  3. Build-out

    All desks & CRM

    Profiles for every desk, deduplication across the firm, templates and CRM logging.

  4. Handover

    Tuning guide

    Documentation so team leads can adjust signal profiles as their niches change.

07

What we learned

Lesson 01

Relevance beats volume

A shorter list that fits a consultant's niche gets used every morning. A longer generic one gets ignored within a week.

Lesson 02

Put the tool where the work already happens

Logging straight into the existing CRM mattered more for adoption than any feature on the list itself.

What's next

Feeding reply and placement outcomes back into each desk's profile, so ranking improves from what actually converts.

Run the numbers

Projects like this start with one question.Answer it for your business.

Where exactly are our leads going cold?

Our free tool answers it with your own figures — in your browser, no sign-up.

More on automation for recruitment
Next case study · E-commerce04 / 06
ReturnLoopA self-serve returns flow that halved support tickets
52%fewer tickets
More work

Other projects.

All case studies
88%less planning time
Workflow automation

Cut dispatch planning from three hours to twenty minutes

A regional courier planned every route by hand each morning, delaying the first pickups.

  • 1,400 hrs/yr saved
  • On-time rate up 12 pts
Process automationSchedulingIntegrations
View case study
24/7first response
AI agents

An intake assistant that clears the overnight queue by 8am

Patient enquiries arrived around the clock but were only triaged once staff logged in.

  • 63% auto-resolved
  • 0 missed urgent cases
AI agentTriageCompliance-aware
View case study
2 daysfaster close
Finance automation

Month-end close that finishes two days early

Reconciliation pulled numbers from five systems into a fragile spreadsheet.

  • 99.8% match rate
  • Audit trail built in
Data pipelineReconciliationReporting
View case study
Start a project

Tell us the job thatkeeps coming back.

We'll tell you whether it's worth automating, and what it would take. A founder or senior engineer replies within one working day.