6 items across 1 sections
Production multimodal scam-intelligence for ScamCheck: screenshot/image upload, lightweight OCR (Cloud Vision + Gemini fallback), deterministic fraud-signal detection, gated deep Gemini-vision analysis, and semantic comparison against known scam clusters via BigQuery VECTOR_SEARCH. Cost-gated, serverless, scale-to-zero.
Large-scale evaluation of the ScamCheck multimodal scam-detection pipeline: a 1,000-sample synthetic corpus (en/hi/hinglish/mixed, 10 scam + 7 legit categories), precision/recall/F1, per-language and per-category breakdown, adversarial robustness, leaderboard analytics, caching/stress harnesses, cost model, scaling path, and known weaknesses.
The A Square Solutions semantic intelligence layer built on Vertex AI embeddings + BigQuery VECTOR_SEARCH: intelligent chunking, hybrid lexical+vector retrieval, snippets & confidence, semantic enrichment (topic/scam/trust/GEO), scam-pattern clustering, GEO/AI-search readiness scoring, and retrieval observability. Production, serverless, scale-to-zero, canonical 768-dim.
Production, serverless GCP infrastructure for the A Square Solutions ecosystem: Vertex AI embeddings for Tier-A posts/service pages/ScamCheck/TrustSeal, a vector-ready BigQuery store with VECTOR_SEARCH, a TrustScore/ScamCheck API on Cloud Run, semantic internal-link intelligence, Cloud Scheduler automation, and a realistic spend model in INR. Serverless-first, scales to zero, no idle VMs.
How AI Execution Lab runs autonomously on free/hobby plans: model-tier routing, content-addressed caching, semantic deduplication, publish throttling, empty-queue early-exit crons, Firestore read/write minimization via increment counters, and batched embeddings. Includes expensive-operation analysis, scaling bottlenecks, the cheapest viable architecture, and estimated monthly cost ranges.
How AI Execution Lab uses Vertex AI Gemini 2.5 (Flash + Pro) exclusively: dependency-free service-account auth, model-tier routing with automatic fallback, graceful rate-limit handling, token usage + cost tracking, Vertex quota monitoring, batched multilingual embeddings, and a deterministic mock fallback. Includes the exact env vars, IAM roles, and a go-live checklist.