LLM Observability That Speeds Delivery

A small European building‑services contractor company bids on public‑sector and SME energy‑efficiency projects across multiple EU countries, where requirements vary by municipality, language, and building code. Its goal was to use large language models (LLMs) to accelerate bid preparation without losing accuracy.

The bottleneck wasn’t willingness to adopt AI, but its innate lack of predictability. Early LLM pilots produced promising but unreliable results, with common errors such as citations to the non-existent clauses of national codes, safety checks that missed local standards, and proposals that lost coherence mid document. Developers couldn’t see why answers changed from run to run, and the company had no common way to evaluate outputs against contract terms.

ABV was brought in as an end‑to‑end validation, observability, and compliance layer tailored to LLM workflows. Before deployment, the team built structured evaluations against a “golden set” of historical RFPs, and other documents across multiple languages. Passing criteria required grounded answers with verifiable sources, explicit code citations, and clear limits-of-scope statements. In production, ABV monitored prompts, retrieved context, and generated audit‑grade traces showing exactly which documents supported each claim.

Evaluations became a shared language. Subject matter experts tracked whether LLM outputs met commercial terms and warranty language. Real‑time dashboards made it clear to technical and non technical stakeholders when LLMs were functioning out of spec, allowing teams to plan targeted updates rather than engaging in broad guesswork. The impact was tangible. Bid preparation time dropped significantly, and mis‑citation of code clauses fell by more than 70% on monitored samples. Ballooning AI inference costs trended down as ABV’s evaluations informed smarter model and context routing without sacrificing quality.

ABV turned our LLM pilots into a dependable part of delivery. We can show exactly why a recommendation is valid, where it came from, and what it doesn’t cover. Our teams and clients can move faster with fewer surprises.

— Operations Director

With ABV in place, the company is positioned to automate documentation required by evolving EU regulations while maintaining a rigorous foundation of validation, observability, and safeguards. In a market of many small players and tight margins, reliable LLMs have shifted from a risky experiment to a competitive advantage.