Content par jayen

Professional networking

Reference build

Three times the industry connection rate, in 15 milliseconds

A matching engine that models people as vectors across skills, goals, seniority, industry and geography — and returns a ranked, business-rule-aware shortlist in fifteen milliseconds.

3xindustry connection rate

Aik nazar mein

Muddat
39 hafte
Team ka hajm
4 afraad
Muahide ki naueeyat
Nayi tameer
Project ki qism
Web application
Shoba
B2B SaaS

Soorat-e-haal

Challenge kya tha

Good matches need signal, and asking users for signal directly means a preference form nobody finishes. Beyond that, naive pairwise matching is O(n²) and stops being computable somewhere around 25,000 users.

Hum ne kya kiya

Infer the signal from the profile graph, retrieve approximately, then re-rank exactly — which is what keeps the whole thing inside 15ms.

Wo faisle jo aham the

  • Approximate retrieval, exact re-ranking

    An HNSW index over pgvector for sub-10ms nearest-neighbour retrieval, then a re-ranking pass applying mutual connections, past interactions and verification status. Precision where it changes the answer, approximation where it does not.

  • Negative feedback as a first-class signal

    Explicit 'not relevant' signals fed back into ranking. This moved the numbers more than any change to the model did.

Kya badla

industry average acceptance rate
3xindustry average acceptance rate
end-to-end recommendation latency
15msend-to-end recommendation latency
match relevance
4.3/5match relevance
30-day retention
62%30-day retention
  • 15ms — 12ms retrieval, 3ms re-ranking
  • 4.3/5 — user-reported
  • 62% — at 25K+ monthly actives

Three times the industry-average acceptance rate, 4.3/5 self-reported relevance, and 62% thirty-day retention across 25,000+ monthly actives.

Istemal shuda khidmaat

  • Vector-based matching engine on pgvector with HNSW
  • Business-rule re-ranking layer
  • Feedback instrumentation feeding ranking weights

Hum kya mukhtalif karte

Har mansoobe mein aisi aik baat hoti hai. Ise shaya karna hi asal nukta hai — jis case study mein koi pachhtawa na ho wo saboot nahi, tashheer hai.

The biggest improvement did not come from a better model, it came from better feedback loops. We would instrument the negative signal on day one rather than adding it once the rankings looked suspicious.

Istemal shuda khidmaat

Mazeed kaam

Shuru karne ke liye tayyar hain?

Aisa hi project shuru karenKaam