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Prismforce
Client Delivery
Graph-Based Skill Recommendation Engine
Prismforce⚡The recommendation system ignored hierarchical skill relationships - related skills treated as independent nodes with no structural modeling; every taxonomy expansion triggered full batch retraining, blocking updates until recompute completed; inference latency under production concurrency exceeded the sub-50ms SLA required for live platform use.
⚠️Prismforce needed real-time skill recommendations against a large, evolving taxonomy. The existing system missed hierarchical skill relationships, went stale under profile updates, and couldn't hit sub-50ms latency for live platform use.
⚙️Real-time recommendation engine using a weighted directed graph encoding multi-level skill hierarchy relationships as typed edges with dynamic weight updates. Lightweight mathematical scoring heuristics minimize computational overhead per inference call. Update model handles dynamic node additions without full graph recomputation.
🛡️Deterministic traversal logic produces consistent outputs under frequent profile and taxonomy updates. Heuristics keep inference paths predictable and bounded. Latency profiled under realistic production concurrency on NVIDIA T4 before deployment.
🚀~30% improvement in recommendation relevance. Sub-50ms inference on NVIDIA T4 under production load. Dynamic updates eliminated batch retraining on taxonomy expansion.
↳ +30% recommendation relevance • sub-50ms latency • single NVIDIA T4 under production load
Weighted Directed GraphMulti-Level Skill HierarchyDynamic Node UpdatesMathematical Scoring HeuristicsReal-Time InferenceNVIDIA T4