Two candidates on your screen. Same job title: “Project Manager”. Same years of experience: 6 years. Same LinkedIn skills: Agile, Scrum, Stakeholder management.
LinkedIn says: identical. Your intuition says: check the employers. You google the company names. 5 minutes later you know: completely different profiles.
One works at an IT scale-up. The other at a construction company. For your IT vacancy, candidate A is relevant. Candidate B is not.
How often does this happen to you? Every week. Every search. You filter on job title and location, but the real question remains: which employers tell me whether someone fits?
The problem with job titles
A job title without employer context is meaningless.
“Project Manager” can mean:
- IT projects at a SaaS scale-up (agile sprints, EUR 2M budget, multi-stakeholder)
- Construction projects at a contractor (waterfall methodology, EUR 50M infrastructure)
- Marketing campaigns at an e-commerce company (2-week sprints, cross-functional)
- Manufacturing projects (lean, six sigma, supply chain optimization)
Same title. Four completely different roles. Different methodology, different stakeholders, different complexity.
The title tells you what someone does. The employer tells you how and in what context.
Without that context you filter blindly. You add candidate B to your shortlist because the title checks out. Then it turns out in the interview that the experience doesn’t align. You’ve lost an hour. The candidate has lost an hour. The hiring manager asks: why was this candidate presented?
Why context only now has a solution
This problem has existed for 20 years. Recruiters have always known that employer context is important. So why was it never solved?
Because it’s manually impossible.
In a search of 800 profiles there are on average 600-700 unique employers. If you google each employer for 5 minutes, you’re spending 50-60 hours. For a single search.
You can’t spend 50 hours on research before you start screening. So you screened on titles. And missed candidates.
What changed? Three things:
- AI can process and analyze company information in seconds
- Databases like Crustdata contain company data on 60+ million organizations
- NLP (Natural Language Processing) understands nuance and context
SourceLens combines these three. It analyzes the last 8 employers of a candidate across 18 dimensions. Automatically. In 8 seconds per candidate.
The 18 dimensions that actually tell you who someone is
When you evaluate a candidate, you want to know:
Company:
- Sector/industry
- Company size (startup, SME, corporate)
- Revenue and growth stage
Market:
- B2B or B2C
- Customer segment (Enterprise, Mid-market, SMB)
- Geographic focus (local, EU, global)
Product/Service:
- Product type (SaaS, on-premise, physical product, services)
- Proposition complexity
Sales/Commercial (for commercial roles):
- Sales model (inside sales, field sales, consultative, channel)
- Deal size (transactional vs. enterprise)
- Sales cycle length (weeks vs. months)
Way of Working:
- Decision speed (startup chaos vs. corporate process)
- Ownership structure (how independently someone works)
- Culture (quota-driven vs. relationship-driven)
These 18 dimensions tell you not just what a candidate has done, but in what context they did it.
And context determines whether experience is transferable to your vacancy.
Case study: Project manager A vs project manager B
Suppose: you’re looking for an IT Project Manager for a SaaS scale-up. Agile workflow, multi-stakeholder environment, fast decision-making.
You get two candidates:
Candidate A:
- Role: Project Manager
- Employer: CloudBase (scale-up, 120 employees)
- Context: SaaS platform, agile, 2-week sprints, EUR 2M budget, multi-stakeholder (product, engineering, sales), 18-24 month projects
Candidate B:
- Role: Project Manager
- Employer: DeltaMech (construction company, 800 employees)
- Context: infrastructure projects, waterfall methodology, 3-5 year duration, EUR 50M budget, stakeholders (contractors, municipality, regulators)
Same title. Same years of experience. But:
- Candidate A works in short iterations. Candidate B in long planning phases.
- Candidate A switches daily between product and sales. Candidate B works with contractors and government.
- Candidate A works with a EUR 2M budget. Candidate B with EUR 50M (completely different scale and responsibility).
For your IT scale-up vacancy, candidate A is relevant. Candidate B is not.
Without employer context you only see “Project Manager, 6 years experience, Agile”. With employer context you see the difference in 8 seconds.
How to analyze employer context (manual vs automatic)
Manual method
- Open LinkedIn profile
- Google each employer
- Check website: what do they do, which sector, how big
- Search on LinkedIn: B2B or B2C, who are their clients
- Try to determine: sales model, complexity, way of working
- Note your findings
- Repeat for the next employer
Time per candidate: 5-10 minutes Time for 100 candidates: 8-16 hours
Automatic method (SourceLens)
- Export LinkedIn profile to SourceLens (1 click)
- AI analyzes last 8 employers across 18 dimensions
- Employer context is added to the candidate profile
- Matching engine compares context with your vacancy criteria
- Matching score with justification per criterion
Time per candidate: 8 seconds Time for 100 candidates: 13 minutes
From 8-16 hours to 13 minutes. From guessing to knowing.
Why this is only now possible
Ten years ago this was science fiction. What changed?
Advanced AI models understand company context from unstructured data. They can analyze a company website and conclude: “This is a B2B SaaS company with consultative inside sales, 12+ month sales cycles, enterprise clients.”
Databases like Crustdata contain structured information on 60+ million companies worldwide. Sector, size, revenue, headcount, location.
NLP recognizes nuance. The difference between “enterprise sales at a SaaS company” and “transactional sales at a product company” is subtle. AI understands that subtle difference.
SourceLens combines these three technologies. The result: instant employer context for every candidate in your search.
From job titles to employer context
The old workflow:
- Boolean search in LinkedIn on job title + location
- Get 800 profiles back
- Scroll, open profile, google employer, evaluate
- 3-4 hours later: 80 candidates in your shortlist
- Of which 40% turn out not actually relevant (discovered later)
The new workflow:
- Boolean search in LinkedIn on job title + location
- Export 800 profiles to SourceLens (Chrome extension, 2 minutes)
- AI analyzes employers across 18 dimensions, matches against your criteria
- 45 minutes later: 80 candidates with matching score and justification
- Shortlist is immediately usable, 95%+ relevance
Stop filtering on job titles. Start filtering on employer context.
The candidate with the perfect title at the wrong type of company doesn’t fit. The candidate with a different title at the right type of company often does.
Context determines relevance. Not the title.
Conclusion: Context is the new standard
Job titles are labels. They categorize. But they don’t tell the story.
The employer tells the story. The sector, the organization type, the sales model, the complexity. That’s what determines whether someone aligns with your vacancy.
As a corporate recruiter, you can’t know the market for every role. As an agency recruiter, you build that knowledge for years. As an independent recruiter, you regularly step into unknown sectors.
With employer context that’s no longer necessary. The context is automatically added. For every candidate. Every search. Every role.
From 800 profiles to 80 relevant candidates. From guessing to knowing. From job titles to employer context.
See employer context for your next 100 candidates. Try SourceLens free for 14 days at sourcelens.ai
Arthur Balabrega is the founder of SourceLens and has 20 years of experience in recruitment. He built SourceLens because he saw recruiters spending 90% of their time figuring out whether employers are relevant — time better spent actually talking to candidates.