Deep dive
Start with one profession or region
A general professional network needs millions of members before it is useful. A focused one needs far fewer: if every healthcare worker in a region or every structural engineer in a country is there, employers follow. Focus also makes verification practical, because you can check licences, memberships or employers that matter to that audience.
Plan the cold start. Invite founding members, import profiles with consent from CVs or other networks, partner with associations and give employers a reason to post jobs early. Our MVP development approach keeps the first release lean so the budget goes into the features that make the community useful.
Profiles, graph and feed
Profiles are structured data: positions, dates, companies, schools and skills normalised against reference lists so search and matching work. Connections form a graph that answers questions such as second-degree contacts and mutual connections. For an MVP, a relational database with careful indexes handles this well; specialised graph stores only pay off at very large scale.
The feed starts chronological with simple boosts for connections and engagement. As activity grows, a ranked feed that balances connections, followed topics and quality takes over. Log impressions and interactions from the first release so ranking has data when you need it.
Jobs and AI matching
A basic job board is a list with filters. Matching is what makes it valuable: understanding that a profile with certain skills fits a role described in different words. Embedding models turn profiles and job descriptions into vectors, and semantic search finds close matches; a ranking layer then weighs location, seniority, salary and recency.
Hiring is a regulated use of AI. Show why a match was suggested, avoid sensitive attributes and proxies for them, let people correct their data and keep audit logs of model decisions. If you serve the EU, plan for the AI Act's high-risk requirements for recruitment systems; if you serve New York City employers, expect questions about bias audits.
- Normalise titles and skills against a taxonomy before you embed them.
- Keep humans in the loop for any rejection decision.
- Measure match quality per group, not only on average.
Trust, privacy and running costs
Fake profiles damage a professional network faster than any missing feature. Combine sign-up friction proportionate to risk, work email and ID verification, rate limits on viewing and messaging, and models that flag scripted behaviour. Scraping defences should log and throttle, not just block, so you can see who is probing.
Running costs are modest compared with media apps: hosting, search, email, verification fees and AI inference for matching. Maintenance is roughly 15-20% of the build cost per year. Our application maintenance and support team usually stays on for the recruiter roadmap.
Recruiter tools: where the revenue is
Members join a professional network for free, but recruiters and employers pay for it. Once the network has enough members in its niche, a recruiter workspace becomes the main commercial product: advanced search across the whole membership, saved searches with alerts, candidate pipelines, shared notes, team seats and limits on outreach that protect members from spam. Each of these is ordinary software, but together they form a B2B product with its own onboarding, billing and support.
Integrations make it sticky. Employers already run applicant tracking systems, so job sync and applicant export are often what turns a trial into an annual contract. Unified ATS APIs can reduce the number of individual connectors you maintain. Price recruiter seats annually, meter outreach messages and report clearly on response rates, because recruiting teams renew when they can show hires. Our SaaS development team builds the billing, roles and reporting that a B2B tier needs.