How Acme Corp Monitors Its Financial AI in Real Time
Background Context
Acme Corp launched an AI financial agent that helped customers navigate spreadsheets, analyze SEC filings and earnings reports, and interpret market data. The product had clear value but carried equally high risk: in finance, trust is fragile. A couple hallucinated numbers or missing citations can cause customers to lose confidence and later churn.
To scale successfully, Acme needed a system that could ensure accuracy in real time, detect regressions before customers noticed, and continuously improve from how customers interacted with the agent.
Inefficient Status Quo
Before working with Judgment Labs, Acme had no systematic way to monitor the reliability of agent behavior. The only signals of failure came from frustrated customers filing complaints through Slack or email support. By the time engineers investigated, dissatisfaction had already spread. Additionally, engineers had little insight into behavior patterns drifting over time, such as the type of queries shifting from knowledge retrieval on SEC filings to workflow automation on spreadsheets over time.
This reactive loop meant that:
Issues with agent behavior were discovered late (days or weeks after deployment).
Silent regressions slipped by; e.g., prompt rewrites or provider-driven model updates that quietly degraded accuracy in ways testing didn’t cover.
Customer confidence eroded because failures appeared preventable.
Engineering team was operating in the dark over which workflows to optimize for.
“It was really hard to get ahead of production regressions or unexpected behaviors when we had no Sentry-style system over the non-deterministic nature of LLMs.” — Engineering Manager, Acme Corp
Judgment Labs Approach
Judgment started by grounding evaluation in Acme’s real customer signals. This made every interaction a measurable data point to ground evaluation metrics and methods from.
Mining End User Signals → Building Rubrics
Acme had rich production data: thumbs up/down ratings, open-ended feedback, inline edits, and spreadsheet corrections. Judgment engineers bootstrapped synthetic examples and reasoning traces at scale from this data.
By clustering and analyzing these signals with LLMs, we discovered which failure patterns correlated most with end user dissatisfaction and which workflows drove adoption (e.g., quarterly earnings summaries, EPS breakdowns). These insights were distilled into custom evaluation rubrics that reflected production usage patterns instead of abstract financial QA benchmarks.
This rubric-first process ensured that subsequent evaluators and monitoring systems were tuned to the exact failure modes that mattered most to Acme’s end users.
“Grounding evals in our own deployment data really helped us gain confidence that these metrics were telling the real story about agent quality.” — Senior AI engineer, Acme Corp
Real-Time Agent Behavior Monitoring (ABM)
With rubrics in place, Judgment deployed a post-trained judge model built on Acme’s financial data and customer history. It was tuned with paired examples of agent outputs and corrected analyst drafts, focusing on the most business-critical failure modes:
Hallucinated values in spreadsheets (e.g., fabricated ratios).
Citation mismatches (claims not properly backed by linked filings, or links pointing to non-existent sources).
Coverage failures (skipping key sections of 10-Ks or earnings transcripts).
Every response was evaluated by Judgment in real time, producing a risk verdict and rationale tied directly to Acme’s accuracy rubric.
Regression Detection
The monitoring system logged and flagged regressions across production deployments, including:
A prompt update that led the agent to mis-handle EPS calculations.
A silent model swap by an inference provider that introduced subtle citation gaps.
A tool-use regression where the financial summarizer call was failing/skipped, yielding incomplete analysis.
Instead of hearing about these regressions from customer complaints, Acme now received instant customized alerts and triage reports via Slack/email/Judgment’s platform.
Outcomes
Customer-visible errors leading to tickets/Slack complaints reduced by 55% through real-time detection and intervention.
Regressions caught instantly that instead would have lingered unnoticed for weeks or months.
Customer approval scores improved by 20% after 10 weeks, measured via in-product surveys and retention metrics.
“With Judgment, we went from reactive firefighting to proactive monitoring. We now know about failures before our customers do.” Head of AI Product, Acme Corp
Conclusion
By starting with customer signal mining to build rubrics, and layering on real-time monitoring and regression detection, Acme kept its financial agent reliable in a high-risk domain. The company reduced churn, increased adoption, and strengthened customer trust while also saving dozens of analyst-hours spent triaging errors. Today, Acme runs hundreds of thousands of customer interactions per week through Judgment’s monitoring pipeline, ensuring their agents stay accurate, efficient, and trustworthy over time.
