For example, if you are looking for a Senior Backend Engineer for a fintech product, with strong Java experience and based in Portugal<\/strong>, you can give AI Search the full vacancy and add only the boundaries that really matter: Portugal, Java, and perhaps years of experience if that requirement is strict. You do not automatically need Spring Boot, Kafka, AWS, Kubernetes, PostgreSQL, fintech, REST APIs, CI\/CD, and every other phrase from the JD as separate mandatory filters.<\/p>\n The same principle works for lead generation. You could ask AI Search to find senior people responsible for recruitment strategy at US software companies that are actively hiring<\/strong>, then add only the necessary location, company size, or seniority constraints.<\/p>\n A useful rule is simple: let AI Search handle relevance; use filters to define the boundaries.<\/strong><\/p>\n Titles are far less standardized than they look. The same type of professional can be called differently depending on the company, industry, seniority structure, or simply how someone decided to describe their role.<\/p>\n Instead of building a long list such as Sales Manager, Senior Sales Manager, Business Development Manager, BD Manager, Sales Executive, Business Development Executive<\/em>, start broader where it makes sense. Sales \/ Business Development<\/strong> may give the system more room to identify the right people based on their actual experience.<\/p>\n Another useful approach is to start with AI Search and inspect the titles used by the most relevant candidates. The market itself can tell you which title variations you should consider.<\/p>\n Imagine, for example, that you need someone who works with enterprise clients on AI implementation. Relevant candidates may call themselves Forward Deployed Engineer, AI Solutions Engineer, Applied AI Engineer, Solutions Architect, AI Consultant, or Machine Learning Engineer<\/strong>. Their responsibilities may overlap heavily even though their titles do not.<\/p>\n So before adding ten exact titles, ask yourself: am I searching for a label, or for the work this person actually does?<\/strong><\/p>\n You do not need to create a perfect search syntax before using AI Search. You can paste a full job description, upload a file, or simply describe the person you need in natural language.<\/p>\n For recruitment, that could be: \u201cSenior Java engineer with strong Kafka experience who has worked on high-load distributed systems.\u201d<\/em> For sales: \u201cVP-level commercial leaders at cybersecurity companies selling enterprise software in Europe.\u201d<\/em> For sourcing: \u201cRecruitment leader who has built an internal talent acquisition function in a fast-growing SaaS company.\u201d<\/em><\/p>\n The clearer the context, the more information the system has for ranking. Add strict filters only where they represent something you genuinely cannot compromise on.<\/p>\n For common roles with a large talent pool, you may not need maximum ranking depth. For niche positions, unusual skill combinations, or senior searches, relevance at the top of the results can matter much more.<\/p>\n Search Accuracy<\/strong> controls how precisely the system ranks profiles. The higher the value, the more accurately Wandify prioritizes the candidates that best match the context. Higher accuracy may make the first search a little slower; all subsequent searches run at full speed.<\/p>\n It is especially useful for niche technical roles, ambiguous titles, executive searches, or situations where several different backgrounds could potentially fit the same position.<\/p>\n When you find a candidate who represents what you are looking for, use the Like button. A Like is not simply a way to save a profile. Wandify uses all liked candidates as additional positive context for the AI Search.<\/p>\n This is particularly valuable when the original vacancy is vague. Imagine your initial description is simply: \u201cProduct-minded Backend Engineer for an AI startup.\u201d<\/em> You review the results and find three people who closely match what the hiring manager has in mind. Like all three, and the system can use those profiles together with the original vacancy or query to improve the ranking.<\/p>\n Instead of trying to translate every nuance into additional filters, you are effectively showing the system: this is what good looks like.<\/strong><\/p>\n Sometimes it is easier to show the system a great profile than to describe it.<\/p>\n With Find Similar<\/strong>, you can use a LinkedIn profile link or a Wandify profile as additional context. You can also combine Find Similar with AI Search and filters.<\/p>\n For example, perhaps your company hired an excellent engineer last year and now needs someone with a similar background. Add that profile as a reference and specify what should be different this time: \u201cSimilar profile, but based in Germany and with B2B SaaS experience.\u201d<\/em> Then use Germany as a hard location filter.<\/p>\n The same logic works in lead generation. If your team already knows one decision-maker who perfectly represents your ICP, use that profile as a reference and combine it with your target company or market context.<\/p>\n You can also use several reference signals together. A difficult search might combine a job description + Find Similar + several liked profiles + a critical location filter<\/strong>. Each one adds a different type of context.<\/p>\n One search does not always show the entire market. That does not mean the first search failed. Different formulations can surface different groups of people.<\/p>\n Suppose you are looking for a Head of Sales<\/strong>. Your first search may use the full vacancy, Ukraine, and 7+ years of commercial experience. Then run a broader version: \u201cSenior commercial leader who has built B2B sales teams and owned revenue growth.\u201d<\/em><\/p>\n Without forcing an exact Head of Sales title, the second search may surface Commercial Directors, CROs, VP Business Development, Revenue Directors, or Managing Directors<\/strong> who have exactly the experience you need.<\/p>\n This is particularly useful for difficult roles. One search gives you the obvious matches. Another helps you discover the people your original assumptions might have excluded.<\/p>\n If you or a colleague already created a useful LinkedIn search, you can bring it into Wandify using a LinkedIn Search URL<\/strong> instead of rebuilding the criteria manually.<\/p>\n An important detail is that Wandify does not use your LinkedIn account to import candidates from that search URL. A colleague can simply send you the link, and you can work with it even if your own LinkedIn account is temporarily unavailable.<\/p>\n Once the candidates are imported, you can add them to folders, use Wandify filters, review the results, enrich data, and continue working with that audience inside your own workflow.<\/p>\n You can also add candidates through the Chrome Extension<\/strong> or CSV Import<\/strong>, which means your Wandify database does not have to start from zero.<\/p>\n A good search should not lose its value once a vacancy closes. Candidates you find today may become relevant again three months later.<\/p>\n Folders and statuses allow you to build reusable pools such as Backend Engineers | Portugal<\/strong>, AI Talent | Future Pipeline<\/strong>, Sales Leadership | Reviewed<\/strong>, or Strong Profiles | Keep Warm<\/strong>. Sales teams can apply the same logic to prospect segments such as HRTech | US | TA Leaders<\/strong> or Cybersecurity | Enterprise Sales<\/strong>.<\/p>\n The more structured the database becomes, the less often the team has to restart the same research from scratch. Since you can continue adding profiles through Wandify search, LinkedIn Search URL, the extension, and CSV import, the database can grow together with your team’s network.<\/p>\n If your team already works in an ATS, there is no need to create unnecessary duplicate work.<\/p>\n For CleverStaff users, Wandify has a native integration with CleverStaff<\/a>. Candidates can be exported directly from search results or folders into a CleverStaff vacancy, with or without contacts. Inside CleverStaff, the integration can also be used to update candidate profile information, update contacts, or refresh both.<\/p>\n This allows CleverStaff to remain your core ATS while Wandify handles sourcing and enrichment around it.<\/p>\n If you use another ATS, CRM, or an internal recruitment system, Wandify also provides a public API<\/a>. It can be used to build custom workflows for profile enrichment and contact enrichment, including retrieving professional information and available contact data based on identifiers such as a LinkedIn profile.<\/p>\n This does not mean Wandify has a ready-made native two-way integration with every ATS. It means your team can connect Wandify enrichment to an existing system through the API instead of having to redesign the entire workflow.<\/p>\n Automation works best after you know exactly who you want to contact.<\/p>\n A weak workflow is Search \u2192 Select everyone \u2192 Launch campaign<\/strong>. A stronger one is Search \u2192 Review \u2192 Segment \u2192 Folder \u2192 Campaign<\/strong>.<\/p>\n In recruitment, passive senior candidates, active job seekers, people already connected with the recruiter, and completely cold candidates may require different communication. In sales, a Founder, VP Sales, and Head of Talent Acquisition may all be relevant targets, but they rarely have the same problem or respond to the same message.<\/p>\n The goal is not simply to automate more people. It is to automate the repetitive parts while keeping the audience and message relevant.<\/p>\n When connecting LinkedIn to Wandify, configure the account around your normal usage. In the LinkedIn Automation Setup guide<\/a>, Wandify recommends selecting the location from which you normally use LinkedIn so the activity remains consistent with your regular access pattern.<\/p>\n2. Search for the role, not only the exact job title<\/h2>\n
3. Describe who you need in your own words<\/h2>\n
4. Use Search Accuracy strategically<\/h2>\n
5. Like strong profiles to give AI more context<\/h2>\n
6. Use Find Similar when an example says more than a description<\/h2>\n
7. Try a second search angle before deciding the market is empty<\/h2>\n
8. Already have a good LinkedIn search? Don\u2019t rebuild it<\/h2>\n
9. Build a database you can reuse<\/h2>\n
10. Keep your ATS, and use Wandify around it<\/h2>\n
11. Organize your audience before launching outreach<\/h2>\n
![\"\"](\"https:\/\/wandify.io\/blog\/wp-content\/uploads\/2026\/09\/screenshot-1-1-300x30.png\")
<\/p>\n12. Automate LinkedIn activity while keeping control of the account<\/h2>\n