Using Manual Filters

A precise search built with structured filters, for when you know exactly what you want and need full control.

CLIENT-ONLY · How-To

Outcome

A search built criterion by criterion, with full control over every filter.

Prerequisites

Access to the search dashboard

Steps

Open the search dashboard.
Click the Manual Filter tab and open the filter panel.
Set any combination of the filters below.
Click Find talents.

[SCREENSHOT: Manual Filter panel with multiple filters applied]

Available filters

Job Titles. One or more roles (e.g. “Data Scientist”, “Software Engineer”). More AI suggestions expands your titles into the adjacent ones candidates actually use (six titles can become twenty-five). Each title is individually settable as must-have, can-have, or excluded, and as current role only or current and past.
Companies. Candidates who have worked at specific organizations.
Excluded Companies. Filter out specific companies, e.g. direct competitors.
Skills. Technical or soft skills (e.g. “Python”, “Project Management”). The skills filter matches only the structured skills list on a profile (the dedicated skills section). If a candidate uses the tool daily but never added it as a skill, this filter misses them. For broader coverage, use Keyword or Boolean Search instead, which scans the full profile text (bio, role descriptions, summaries) rather than just the skills section. A good pattern: filter on the 1-2 must-have skills, then add adjacent keywords through Boolean to widen the net.
Location. City, region, or country, with radius. Works best with cities and a radius around them rather than broad regions. A region like “Oost-Vlaanderen” or “Greater London” stretches results across very different sub-areas; cities plus radius keep results tight to the commute zone you actually care about. When a radius crosses a national border (Kortrijk plus 40 km reaches France), the Allow Cross Border Results toggle decides whether foreign profiles come along.
Industries. Specific sectors (e.g. “FinTech”, “Healthcare”). Industries are LinkedIn-synced, so the label on a company can be counter-intuitive. EY sits under “Professional services”, not “Financial services”. Supplement and nutrition brands often sit under “Sports” rather than “Food and beverages”. Some food brands list as “Manufacturing”. If you know your market’s industry labels, run one search with the industry filter and one without, then merge results into the same project.
Schools. Educational background or specific universities. Via the three-dots menu, each university can be set to must-have or excluded, not just included. The field-of-study filter bundles alternative names for the same degree and tolerates spelling variations.
Certifications. Required certifications (e.g. “PMP”, “AWS Certified”).
Years of Experience. Counts total career years across all roles, not years in a specific function. A profile with 10 years across marketing, sales, and ops shows up under “10+ years” even if only 2 of those years are in the role you’re hiring for. Use it as a broad seniority filter, then let the AI Co-pilot assess role-specific tenure inside the project.
Graduation Year. Better than Years of Experience for junior profiles. Filtering on “graduated in the last 3 years” reliably surfaces juniors; filtering on “0-3 years experience” can miss recent grads whose profiles list internships or student jobs as work history.
Languages. Language requirements, now with proficiency levels per language. Treat the filter as optional, not a hard gate. Languages on Wiggli depend on what the candidate listed on their profile, and many candidates simply don’t list their native language (a Flemish candidate working in Antwerp may have nothing under languages). The AI Co-pilot is better at this: it can infer language from bio language, location, company names, and other signals on the profile. Use the filter for nice-to-have, use the Co-pilot for hard requirements.
Company Size. Number of employees at the candidate’s current or past company. Useful for targeting startup vs scale-up vs enterprise experience.
Founded Year. Year the company was founded. Pairs well with Company Size for startup-focused searches.
Funding Stage. Companies in Seed, Series A, Series B, Series C, etc. Recently funded companies have hiring capacity, so this filter is useful both for sourcing (candidates likely working at companies that are expanding) and for business development (companies likely to need recruiting help). Most relevant in markets with active VC funding; less applicable in regions where funding rounds are rare.
Keyword or Boolean Search. Advanced logic to combine or exclude terms.
Nationality. Appears when your prompt includes a nationality (for example “digital marketeer, Emirati”) and is available in specific regions. [GAP: how nationality is derived and which regions have the filter]

Verification

Results match every active filter. Removing a filter and re-running expands results predictably. Saving as a Saved Filter preserves every criterion.

Troubleshooting

Too few results. Loosen the strictest filter first, usually experience or location radius. Remove industry as a test.
Too many results. Add Excluded Companies, raise the experience minimum, or narrow location.
Boolean not working. Use straight quotes for exact phrases. See Boolean search syntax and operators.

Best practices

Build in layers: must-haves first, nice-to-haves second. Zero results on the must-haves means the search is over-specified.
Use Excluded Companies to remove direct competitors on sensitive roles.
Combine structured filters with Boolean: filters for clean criteria, Boolean for fuzzy keyword matching and skills that may not be in structured data.
Leave precise, judgment-based criteria (years in a specific field, current vs past role) to the AI Co-pilot rather than forcing them into filters.
Save high-performing searches so you don’t rebuild them.

Still stuck?

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