Research in AI and technology

This paper examines retrieval-augmented generation deployments across regulated and high-volume enterprise environments. We compare pure vector search with hybrid retrieval and reranking on domain corpora that include policies, ERP identifiers, and conflicting document versions.

Findings emphasize permission-aware retrieval, golden evaluation sets, and operational ownership as stronger predictors of production success than embedding model choice alone.

Messaging commerce introduces rate limits, session windows, and concurrent checkout constraints that traditional ecommerce stacks do not address. This research documents architecture patterns from multi-tenant WhatsApp commerce platforms operating at millions of messages per month.

We cover inventory reservation, queue isolation, payment reconciliation, and grounded AI support with human escalation — with load-testing guidance for festival and campaign peaks.

Many AI initiatives stall after a successful demo. This paper maps the engineering and governance gaps — integration contracts, security review, observability, and post-launch ownership — that separate pilots from operated systems.

We propose a production-readiness rubric and a delivery model that pairs architecture review with incremental rollout and measurable business outcomes.

Autonomous agents expand blast radius when tools can write to systems of record. This paper outlines allowlist design, confidence thresholds, escalation paths, and continuous evaluation for agentic workflows.

We argue that operating model — ownership, runbooks, and on-call — is as critical as model selection for enterprise adoption.