AI-Assisted Integration: Schema Matching, API Mapping, and Workflow Optimization

Abstract

Enterprise integration across heterogeneous platforms exposes three persistent failure modes: semantic misalignment between schemas and APIs, unsafe contract evolution under version drift, and operationally brittle orchestration tuned by heuristic rather than signal. This article proposes a unified, policy-aware framework that addresses all three simultaneously. A hybrid schema-matching ensemble pairs conservative symbolic matchers with deterministic, grounded LLM scoring to achieve measurable F1 gains over symbolic baselines. API contract normalization, validated through consumer-driven contracts (CDC) and staged rollout, reduces promotion regressions attributable to silent payload changes. An observability-driven control loop (ODCL) treats telemetry as a first-class control input, adaptively tuning retry parameters, concurrency ceilings, and routing decisions against SLO error budgets. Results on open-specification corpora demonstrate hybrid matching F1 improvement of approximately 8 to 15 points, P95 latency reduction of 18% at high concurrency, and a 36% reduction in technical error volume. Governance is formalized through evidence packs, which are immutable bundles of prompts, model identifiers, decisions, and test artifacts. This positions the method for deployment in regulated environments and subjects it to normative scrutiny regarding the appropriate epistemic role of LLM inference in consequential integration pipelines.