AI / Machine Learning / nn-08
Product Recommendation Siamese Model
Converts enterprise data into faster decisions, measurable quality gains, and defensible automation.
Commercial Scale
$23,800 USD
Risk Reduced
Automation Reliability
Executive Situation
An enterprise catalog produced stale recommendations because collaborative signals were sparse and product metadata changed faster than user purchase histories.
Modular Solutions Response
We trained a two-tower neural retriever over customer behavior, product text, category hierarchy, and margin rules. Candidate generation runs through Faiss while business constraints rerank the final list for availability and account eligibility.
Industry
Manufacturing
Category
Neural Networks & Deep Learning
Specialty
Retraining
Evidence Basis
Model + MLOps
parameters
63M dual-encoder towers
latency
27ms candidate retrieval
training
75 GPU hours
loss
L = -log exp(s⁺/τ) / Σ exp(sⱼ/τ)
Enterprise Security Gate
Network Access Restricted.
Detailed files, client-specific assumptions, and delivery channels remain controlled.