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A modern networking platform is only as useful as its ability to help people discover the right connections. A user may have thousands or even millions of potential profiles available, but if the platform cannot help them find relevant professionals, companies, communities, opportunities, or events quickly, the size of the network becomes less valuable.
This is why advanced search and filters should not be treated as secondary features in a networking application. They are core parts of the product experience.
A well-designed search system can allow users to find professionals by skills, job titles, industries, experience levels, locations, education, certifications, availability, interests, companies, languages, connections, and many other attributes. Advanced filters can then narrow those results so that users spend less time scrolling and more time taking meaningful action.
Developing such a platform requires more than adding a search bar to an application. You need a carefully designed data model, search architecture, indexing strategy, filtering logic, ranking system, intuitive interface, privacy controls, security mechanisms, analytics, and scalable infrastructure.
This guide explains how to develop a networking platform with advanced search and filters from the ground up.
A networking platform is a digital product that enables people or organizations to discover one another, communicate, establish professional relationships, exchange information, collaborate, and identify opportunities.
Traditional networking applications may provide basic profile discovery through names or simple keywords. An advanced networking platform goes considerably further.
Instead of allowing someone to search only for:
“Software developer”
the platform could support a much more precise query such as:
“Find senior Python developers with machine learning experience, located in Bangalore or Hyderabad, working in fintech, with at least seven years of experience.”
The system would interpret the query and apply multiple conditions to the available professional profiles.
An advanced networking search experience can combine:
The goal is not simply to return a large number of results.
The goal is to return relevant results.
That distinction is fundamental.
Networking is fundamentally a discovery problem.
Users typically join a networking platform because they want to find someone or something.
They might want to find:
If discovery is difficult, users may leave the platform even when the underlying network is excellent.
Advanced search addresses this problem by reducing the distance between intent and relevant discovery.
A basic system might work like this:
Query: Marketing
Results:
The results may technically contain the keyword but fail to satisfy the user’s actual intent.
An advanced system can understand additional constraints:
Industry: SaaS
Experience: 5 to 10 years
Location: Mumbai
Skills: SEO, Google Ads, Analytics
Availability: Open to opportunities
Now the platform can produce a considerably narrower and more useful result set.
This improves:
Building a networking search engine is different from building a conventional website search feature.
On a content website, the primary objective may be to find documents containing particular words.
On a networking platform, the system must understand people and relationships.
Consider a profile containing:
Product Designer with eight years of experience in fintech, specializing in UX research, design systems, Figma, and mobile applications.
A user searching for:
“UX researcher fintech”
should potentially discover this person even if “UX researcher” does not appear as the person’s exact job title.
This requires richer search logic.
Networking search may therefore combine:
Does the profile contain the searched terms?
Does the person have the requested skills, location, experience, or industry?
Does the person have mutual connections with the searcher?
Is the person active on the platform?
Does their experience align with the user’s objective?
Has the searcher interacted with similar profiles previously?
A mature system can combine all these signals into a ranking score.
Before selecting technologies, determine what type of networking platform you are building.
The search architecture should reflect the platform’s core use cases.
For example, a professional networking application might focus on:
Each model creates different search requirements.
Important filters may include:
Important filters may include:
Important filters may include:
Important filters may include:
The first step is therefore to define what users are actually trying to accomplish.
Different user groups require different search capabilities.
You should identify the primary personas before designing the search system.
For example:
They may search for:
They may search for:
They may search for:
They may search for:
They may search for:
The search interface should reflect these personas rather than trying to expose every possible filter to every user.
Advanced search works best when it is integrated with the rest of the platform.
A complete networking product may include:
Users create accounts using:
Profiles can include:
Users can:
Users can communicate through:
Search enables discovery of:
The platform can recommend:
Users can receive alerts about:
Advanced search becomes much more powerful when these components are connected.
A sophisticated networking platform should think beyond a single search field.
The search system can include several layers.
Users can type natural keywords.
Example:
React developer
Users can narrow results using predefined attributes.
Example:
The system suggests:
Users can express an intention rather than an exact keyword.
Example:
“People who build AI-powered healthcare products”
The system attempts to identify professionals whose profiles are semantically related.
Search results can be ordered according to relevance to the individual user.
Filter selection is one of the most important aspects of networking search design.
Common filters include:
| Filter | Example |
| Name | John Smith |
| Job title | Product Manager |
| Skills | Python, AWS |
| Industry | Fintech |
| Location | Mumbai |
| Experience | 5 to 10 years |
| Company | Startup |
| Education | MBA |
| Certification | AWS Certified |
| Languages | English, Hindi |
| Availability | Open to work |
| Connection | 2nd-degree |
| Verification | Verified |
| Interests | Entrepreneurship |
| Remote preference | Remote |
| Professional level | Senior |
| Community | AI Professionals |
Not every platform needs every filter.
The right filter set depends on the data available and the jobs users are trying to accomplish.
Search quality depends heavily on profile quality.
If professional information is stored as unstructured text, filtering becomes difficult.
For example, storing everything inside an “About Me” field makes it hard to answer questions like:
Find professionals with five years of Python experience.
Instead, important information should be represented as structured fields.
A profile might contain:
User
├── Basic Information
├── Professional Information
├── Skills
├── Experience
├── Education
├── Certifications
├── Location
├── Languages
├── Interests
├── Availability
└── Preferences
A structured data model makes search considerably easier.
Consider skills.
A poor implementation might store:
skills = “Python, python programming, Python developer, PY”
This creates inconsistent results.
A better design uses normalized entities.
For example:
Skill
id
name
slug
category
synonyms
A user’s skills could then reference the skill entity.
This makes it possible to treat:
as related terms where appropriate.
The same principle applies to:
Normalization improves both filtering and analytics.
For small applications, relational database search may initially be sufficient.
As the platform grows, a dedicated search engine may become useful.
Popular approaches include:
The choice depends on:
There is no universal best search engine.
A startup with 10,000 users does not necessarily need a complicated distributed search infrastructure.
Start with an architecture that solves today’s problem while allowing future expansion.
A relational database can handle many search requirements.
For example, PostgreSQL can support:
This may be sufficient for an early-stage application.
A dedicated search engine becomes more attractive when you need:
One practical approach is:
Primary database → Search index
The database remains the source of truth.
The search engine becomes a specialized read model optimized for discovery.
A typical architecture could look like:
User Profile
|
v
Primary Database
|
v
Event / Queue
|
v
Indexing Service
|
v
Search Engine
|
v
Search API
|
v
Web / Mobile Application
When a user updates their profile:
This decoupling can improve scalability.
Keyword search is the foundation of most networking discovery experiences.
Users should be able to search by terms such as:
The system should ideally search across multiple relevant fields.
For example:
name
headline
job_title
skills
company
industry
about
experience
education
However, not every field should have equal importance.
A matching job title might be more important than an incidental mention inside an “About” paragraph.
This leads to the concept of field weighting.
Keyword search has limitations.
Suppose a user searches:
“professionals building mobile banking applications”
A keyword engine may look for exact words.
A semantic search system attempts to understand the meaning behind the query.
Potentially relevant profiles could mention:
even if the exact phrase “mobile banking applications” is absent.
Semantic search can use embeddings or other language-model-based approaches to represent content according to meaning.
A hybrid architecture can combine:
Keyword relevance + structured filters + semantic similarity
This is often more useful than relying entirely on semantic search.
Skills are among the most valuable networking filters.
Users may want to find professionals with:
A good skill search should account for variations.
For example:
“React”
could potentially match:
if your skill taxonomy establishes those relationships.
You may organize skills into groups:
Technology
Marketing
Design
Business
Categories make filtering easier.
Job titles are notoriously inconsistent.
One company may call someone:
Product Manager
while another uses:
Product Lead
and another:
Head of Product.
A search system should consider title relationships where appropriate.
You can create a standardized job-title taxonomy.
For example:
Product Management
├── Product Manager
├── Senior Product Manager
├── Product Lead
├── Director of Product
└── Head of Product
However, be careful about automatically treating every title as equivalent.
A Head of Product and an Associate Product Manager have very different seniority levels.
Therefore, titles should ideally be associated with:
Industry filters allow users to discover people operating in specific sectors.
Possible industries include:
Industry classification can be based on controlled categories rather than arbitrary text.
This prevents fragmentation.
For example, without normalization you could have:
These may represent different concepts, but the platform should understand their relationships when appropriate.
Location is particularly important for professional networking.
Users might search for:
Location can be represented hierarchically:
Country
State
City
Local Area
For geographic search, you can also store latitude and longitude.
This allows queries such as:
Professionals within 25 km of a location.
However, privacy should be considered carefully.
Users generally should not have their precise residential coordinates exposed to other users simply because they enabled location-based search.
A platform can provide approximate location instead.
Experience filters are useful for recruitment, consulting, mentorship, and business networking.
Example options:
Or use a numerical range:
Minimum experience: 5 years
Maximum experience: 12 years
Experience can be calculated from structured employment records.
Avoid relying solely on manually entered totals if you have reliable employment history data.
Education can include:
Users may want to find:
MBA graduates in consulting.
Or:
Computer science professionals from a specific university.
Education data should ideally use normalized institution identifiers.
Otherwise, the same institution could appear as:
Normalization improves search consistency.
Certifications are particularly valuable for specialized professional networking.
Users may search for:
Certification records can include:
Certification
name
issuing_organization
issue_date
expiration_date
credential_id
verification_status
If the platform supports credential verification, verified certifications can become a separate search filter.
Company-based discovery is useful for both networking and recruitment.
Users may search for:
Employees at a particular company.
Or:
Former employees of a company.
A company profile should ideally have a unique identifier.
This prevents problems caused by variations in company names.
For example:
could potentially refer to the same organization.
Professional relationships can make search results more useful.
Possible connection filters include:
Suppose a user searches for a cybersecurity expert.
A professional who shares three mutual connections may be more approachable than someone completely outside the user’s network.
The platform can therefore expose:
3 mutual connections
as both a filter and a ranking signal.
Availability filters can be useful for:
Possible states include:
Availability should be controlled by the user.
It should never be inferred solely from profile inactivity or employment status unless the user explicitly permits that behavior.
Interests help users discover relationships beyond formal job attributes.
Examples:
Interests can be represented using tags or normalized topic entities.
This also allows the recommendation engine to discover potential connections based on shared interests.
Advanced users may appreciate Boolean operators.
For example:
Python AND Django
or:
React OR Angular
or:
“product manager”
Exact phrase matching can be particularly useful when searching for specific roles.
However, Boolean syntax should not become mandatory.
A good system can provide advanced controls while keeping the default experience simple.
The true power of advanced networking search comes from combining filters.
For example:
Role:
Software Engineer
Skills:
Python
AWS
Docker
Experience:
5 to 10 years
Industry:
Fintech
Location:
Bengaluru
Availability:
Open to opportunities
The search engine must efficiently process all these constraints.
This is where good indexing becomes important.
Poorly designed queries can become slow as the user base grows.
Users often perform repetitive searches.
A recruiter might repeatedly search:
Senior React developers in Bengaluru.
Instead of forcing the recruiter to rebuild the filters each time, the platform can allow them to save the query.
A saved search might contain:
Search name:
Senior React Developers
Keywords:
React
Location:
Bengaluru
Experience:
5+ years
The system can then provide alerts when new matching profiles appear.
This feature can become a powerful engagement mechanism.
Autocomplete improves the search experience by reducing typing and guiding users toward valid searches.
As the user enters:
data
the system could suggest:
Suggestions can come from:
Autocomplete should be fast.
Users expect near-instant responses while typing.
Returning results is only half the challenge.
The system must decide which results appear first.
A simple ranking model might consider:
Relevance
+ Profile completeness
+ Skill match
+ Experience match
+ Location match
+ Relationship strength
+ Activity
For example:
Score =
0.35 × text relevance
+ 0.20 × skill relevance
+ 0.15 × experience relevance
+ 0.10 × location relevance
+ 0.10 × relationship score
+ 0.10 × profile quality
These numbers are illustrative rather than universal.
Real systems should tune ranking based on actual user behavior and search-quality evaluation.
Two users searching for the same term do not necessarily need identical results.
Suppose two people search:
Marketing expert
A recruiter may benefit from candidates.
A founder may benefit from consultants.
A salesperson may benefit from marketing decision-makers.
Personalization can incorporate:
However, personalization should not create an opaque or overly narrow experience.
Users should still be able to understand why a result is relevant.
Search is intentional.
Recommendations are exploratory.
A networking platform should ideally provide both.
Search answers:
“Who am I looking for?”
Recommendations answer:
“Who else might be valuable to me?”
A recommendation engine can consider:
Search and recommendation systems can therefore complement one another.
A good search interface should not overwhelm users.
A common pattern is:
————————————————
Search professionals, companies, skills…
————————————————
Filters
[Location] [Industry] [Experience] [Skills]
————————————————
342 results
[Profile]
Name
Job title
Company
Skills
Location
[Connect] [View Profile]
————————————————
Advanced filters can open through a dedicated panel.
The interface should distinguish between:
Frequently used filters.
Less frequently used options.
Specialized controls for power users.
Mobile interfaces have less screen space.
A good mobile search experience might use:
Search
[ Search field ]
Filters
[Filter chips]
Sort
[Relevance ▼]
Results
——————
Profile
Details
Actions
——————
Filters can open in a bottom sheet or full-screen panel.
Selected filters should remain visible as removable chips.
For example:
Bengaluru × | Python × | 5+ years ×
This allows users to quickly understand why they are seeing certain results.
Desktop applications can provide more persistent filtering.
A common layout is:
————————————————
Search bar
————————————————
| Filters | Search Results |
| | |
| Location | 342 professionals |
| Industry | |
| Experience | Profile |
| Skills | Profile |
| Education | Profile |
————————————————
A left-side filter panel works well for information-dense professional products.
Filters should be:
Users should immediately know what the filter means.
Selecting a filter should produce expected results.
Users should be able to remove filters easily.
Results should update quickly.
Important filters should not be hidden too deeply.
Do not show dozens of irrelevant controls.
A useful principle is:
Expose complexity progressively.
Begin with common filters and provide advanced options for users who need them.
A search result should contain enough information for the user to decide whether to investigate further.
A professional result card might display:
Name
Headline
Current organization
Location
Top skills
Experience
Mutual connections
Verification indicator
Availability
Action buttons
For example:
Priya Sharma
Senior Product Designer
Fintech | UX Research | Design Systems
Bengaluru
8 years experience
5 mutual connections
[Connect] [View Profile]
The objective is to minimize unnecessary clicks.
Some users may want additional information before opening the complete profile.
A profile preview could display:
This can appear in:
It can make the search process faster.
Search should lead naturally to action.
Users may want to:
Avoid forcing users through unnecessary navigation.
If a search result identifies the right professional, the next action should be obvious.
Advanced search introduces privacy challenges.
Users should control how discoverable their information is.
Possible visibility settings include:
Profile can be discovered by anyone.
Only registered users can search for the profile.
Only existing connections can view certain details.
Profile is excluded from search.
Different profile fields can have different visibility rules.
For example:
Privacy should be designed at the data-access level, not simply hidden through frontend UI.
Search APIs are valuable targets for abuse.
Security mechanisms should include:
Do not allow users to manipulate query parameters to retrieve fields they are not authorized to access.
For example, if a private email field is excluded from the user interface, the backend should still prevent the API from returning it.
Networking platforms can become targets for:
Search should therefore be subject to reasonable controls.
Potential safeguards include:
Do not make search unnecessarily restrictive for legitimate users.
The goal is to balance discovery and safety.
Search quality depends on data accuracy.
If users enter:
Software Engineer
and:
software engineer
and:
Software Engg
you may end up with inconsistent classification.
Validation and normalization can help.
Use:
But preserve user-generated content where appropriate.
Not everything needs to be converted into a rigid taxonomy.
Users quickly notice slow search.
Performance depends on:
Avoid returning unnecessary fields.
Instead of sending an entire profile object with every result, return only what the search card requires.
Example:
{
“id”: “123”,
“name”: “Priya Sharma”,
“headline”: “Senior Product Designer”,
“location”: “Bengaluru”,
“skills”: [“UX”, “Figma”, “Research”]
}
The full profile can be requested separately.
Some searches are highly repetitive.
For example:
Software developers in Bengaluru
may be executed thousands of times.
Caching can reduce infrastructure load.
Potential cache targets include:
However, personalized search results are harder to cache because they vary by user.
Caching should therefore be applied selectively.
Search indexes should be designed around query patterns.
If users frequently filter by:
those fields need efficient indexing.
For a relational database, this could involve carefully designed indexes.
For a dedicated search engine, fields can be mapped according to their intended behavior:
Correct field mapping is critical.
Search results can potentially contain millions of profiles.
Never load all results at once.
Common strategies include:
Page 1
Page 2
Page 3
Results load automatically as users move down.
The server returns a cursor representing the next position.
Cursor-based approaches can work particularly well for large datasets.
You cannot improve search without measuring it.
Track events such as:
A useful metric is:
How often does a search result lead to a meaningful action?
Another useful metric:
How often do users receive no results?
A high zero-result rate can indicate:
Search quality should not be measured solely by speed.
Important metrics include:
Are the top results useful?
How many returned results are relevant?
How many relevant results are found?
Do users interact with results?
Do searches result in connections, messages, hires, or other desired outcomes?
Do users repeatedly change their search?
Repeated reformulation may indicate that the first result set was unsatisfactory.
A scalable networking platform might have the following architecture:
Mobile / Web
|
v
API Layer
|
————————–
| | |
Profile Search Messaging
Service Service Service
| | |
v v v
Database Search Index Database
|
v
Ranking Engine
Not every project needs microservices.
For an early-stage product, a modular monolith may be more practical.
You can separate services later as scale and organizational needs justify it.
A search API might look conceptually like:
GET /api/search/people
with parameters such as:
q
location
skills
industry
min_experience
max_experience
company
education
availability
connection_level
page
limit
sort
The backend should validate all parameters.
It should also enforce:
Avoid accepting arbitrary database expressions from clients.
A basic relational schema might contain:
users
profiles
skills
user_skills
experiences
education
certifications
companies
industries
locations
connections
interests
user_interests
This normalized structure supports professional relationships and structured filtering.
The search index can then flatten the information into a search-oriented document.
For example:
{
user_id,
name,
headline,
skills,
job_titles,
industries,
companies,
location,
experience_years,
education,
certifications,
interests
}
The relational database and search index serve different purposes.
A modern networking platform may use:
The specific framework is less important than maintaining clean separation between:
Search interactions should feel responsive.
Search results may differ depending on whether someone is:
Authorization should happen server-side.
For example:
User A searches for User B
|
v
Check visibility rules
|
v
Return permitted fields
Never assume the frontend can safely enforce privacy.
Search becomes more valuable when connected to notifications.
For example:
A new professional matching your saved search joined the platform.
Or:
Someone matching your “Senior Python Developer” search updated their availability.
Users can choose notification frequency.
Possible options:
Notification controls should prevent the feature from becoming intrusive.
Search and recommendations can share infrastructure.
For example, both systems may use:
A search query could also become an implicit preference signal.
If a user repeatedly searches for:
AI product managers
the recommendation system may learn that this category is relevant to them.
Such signals should be handled responsibly and with appropriate privacy considerations.
A small platform may have:
10,000 users.
A successful platform might eventually have:
10 million users.
The architecture should be capable of growing without requiring a complete rewrite.
Important scalability considerations include:
The most important principle is to identify future bottlenecks early without overengineering the first release.
A networking platform can be deployed using major cloud providers or other hosting infrastructure.
Typical components include:
Application servers
Database
Search engine
Object storage
Cache
Message queue
Monitoring
Logging
CDN
A managed search service can reduce infrastructure maintenance.
The right choice depends on:
Search functionality requires multiple layers of testing.
Test:
Test:
Test:
Test:
Test:
Several mistakes repeatedly create poor search experiences.
Search should influence the profile data model from the beginning.
Structured fields are necessary for meaningful filters.
More filters do not automatically mean better search.
Different users use different terminology.
Professional relevance matters.
Search can expose sensitive information if improperly designed.
Without data, it is difficult to know whether search actually works.
A small application may not need a complex distributed search architecture on day one.
The cost of developing a networking platform with advanced search depends on several variables.
Major factors include:
A simple web application costs less than:
Basic keyword search is considerably simpler than:
A simple profile requires less backend work than a detailed professional identity system.
Identity verification, privacy controls, moderation, and abuse prevention increase development requirements.
Large-scale search requires more sophisticated infrastructure.
Costs vary depending on whether development is performed by:
For projects where you need a dedicated development partner, an experienced company such as Abbacus Technologies can be considered for building and scaling custom software products.
A reasonable development roadmap could look like:
Define:
Design:
Build:
Implement:
Add:
Add:
Improve:
You do not necessarily need to build every component yourself.
Third-party services can potentially provide:
Building everything internally gives maximum control but increases development and maintenance requirements.
Using external services can accelerate development but introduces:
Evaluate each component individually.
Advanced search can support multiple monetization models.
Offer advanced filtering as part of a professional subscription.
Recruiters may pay for:
Organizations may pay for:
Companies could promote relevant profiles or opportunities, subject to transparency and user trust.
Users could receive enhanced professional discovery through subscription plans.
Monetization should not undermine search quality.
Administrators need tools for managing search quality.
The dashboard may provide:
Administrators may also need to manage:
Search can unintentionally amplify problematic profiles or content.
Governance mechanisms should address:
Search ranking should not automatically reward users simply because they generate large amounts of activity.
Quality and trust matter.
Search interfaces should work for users with disabilities.
Important considerations include:
Do not rely solely on visual indicators.
For example, if a profile is verified, the verification status should be communicated accessibly rather than only through an icon.
If the networking platform targets multiple countries, search must support multilingual data.
Challenges include:
For example, users may search for the same profession using different terms.
Internationalization should therefore be considered during data-model and search design rather than added as an afterthought.
Artificial intelligence can significantly improve networking discovery.
Instead of forcing users to construct complex filters, AI can interpret natural language.
A user could enter:
“Find senior cybersecurity professionals in India who have worked in banking and are open to consulting.”
The AI system could convert the request into structured search parameters such as:
Experience: Senior
Industry: Banking
Skill: Cybersecurity
Location: India
Availability: Consulting
The search engine then executes the structured query.
This approach combines conversational UX with deterministic filtering.
Natural language search can make advanced functionality easier to use.
Instead of selecting:
a user can describe the desired professional.
The platform can extract:
The system should show the interpreted filters before executing the search.
This gives users control and reduces unexpected results.
The future of professional networking discovery will likely involve increasingly intelligent systems.
Potential developments include:
Users describe the person they need rather than constructing filters manually.
Search considers professional relationships and network proximity.
Profiles are represented by meaning rather than only keywords.
Results reflect current availability and professional changes.
The platform explains why someone was recommended.
For example:
Recommended because you share three skills and two professional communities.
Systems personalize discovery without exposing unnecessary personal information.
If you are starting from scratch, avoid attempting to build every advanced feature simultaneously.
A practical sequence is:
Document the most important discovery use cases.
For example:
Users need to find professionals based on skills, role, location, experience, and industry.
Create normalized fields for all important search attributes.
Implement:
Track:
Introduce:
Add:
Introduce:
This incremental strategy reduces risk.
Imagine a user wants to find a mentor.
They type:
Product managers in fintech with 10+ years of experience who mentor startups.
The application sends the query to the search API.
The system identifies:
Role:
Product Manager
Industry:
Fintech
Experience:
10+ years
Interest:
Startup mentoring
The search engine retrieves matching profiles.
The ranking layer considers:
The user sees the most relevant professionals first.
They open a profile.
They notice:
4 mutual connections
They request an introduction.
The search system has therefore contributed directly to a successful networking interaction.
Before launching, verify the following.
Developing a networking platform with advanced search and filters requires much more than creating a search box and connecting it to a database.
The strongest networking platforms treat discovery as a core product capability.
Users should be able to describe whom they want to meet, what expertise they need, which industries interest them, where those professionals are located, what experience they have, and what opportunities they are currently open to.
The foundation is structured professional data.
From there, developers can build keyword search, filtering, autocomplete, Boolean queries, geographic discovery, saved searches, relevance ranking, personalization, semantic search, and AI-assisted natural-language discovery.
The technical architecture also matters. A relational database can be appropriate for an early product, while a dedicated search engine can become valuable as query complexity and data volume increase. A search index should be treated as a specialized discovery layer rather than the sole source of truth.
Equally important are privacy, security, accessibility, performance, and search-quality measurement. A technically sophisticated search engine is not successful if users cannot trust it, understand it, or obtain useful results.
The most effective development strategy is incremental:
Start with structured profiles → build reliable keyword search → add filters → improve ranking → measure user behavior → introduce semantic and AI-powered discovery.
This approach lets you create a networking platform that is useful at launch while establishing the technical foundation required for much larger scale.
Ultimately, advanced search should not simply help users find more people.
It should help them find the right people.
That is what turns a large networking database into a genuinely valuable professional network.
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