From Pilot Sprawl to Production‑Grade AI

An enterprise SaaS provider for procurement and contract lifecycle management serves global B2B customers handling thousands of agreements per month. Its mission: use large language models (LLMs) to convert dense supplier contracts, policies, and email threads into accurate drafts and decisions, without compromising confidentiality, fairness, or regulatory obligations.

For this company, the bottleneck was controlling variability in the wild. LLM outputs shifted across different document types, making it hard to prove that the system was factual and policy‑conformant across environments. Developers lacked a unified view of AI feature performance across versions, and product teams had a hard time creating objective pass/fail criteria tied to outcomes. Pilot and demos for features such as LLM‑powered contract Q&A and redlining assistant worked well in theory but could not be easily transformed into an enterprise ready solution, delaying enterprise rollout.

ABV was selected to solve this with an end‑to‑end platform for LLM validation, observability, and compliance. Teams created a scenario bank of annotated contracts and playbooks, then ran structured evaluations that measured AI precision/recall, groundedness, hallucination, prompt‑injection resistance, PII leakage, and jurisdictional consistency. In production, ABV traced every LLM call, including prompt, retrieved passages, tool/function calls, and final output, linking them to versions and experiments. The unified and user-friendly nature of the platform made it easy for engineering and product teams to view a unified and cohesive picture of the product status that could be easily shared to stakeholders with varying levels of technical expertise.

The impact was decisive. Time‑to‑approve new LLM features dropped by weeks. Hallucinations per 100 answers declined by almost a quarter, and groundedness on high‑risk clauses reached acceptable levels across languages. Product teams no longer wasted hours testing and justifying release quality to stakeholders, and leadership no longer felt the need to micromanage what previously felt like a high risk project.

ABV turned our LLM work from an art into a science. We can finally understand exactly why an answer was produced, and where it’s grounded.

— Senior Engineer

With ABV, the organization is set to automate compliance for upcoming regulations (e.g., EU AI Act obligations on transparency, risk management, and technical documentation) while scaling a durable foundation of validation, observability, and guardrails. What once slowed LLM adoption has become a catalyst for safe, trustworthy, and scalable document automation.