AI automation,
evaluated
before it scales.
Zeal defines and bounds what your AI workflows do in production. We run a machine-accelerated eval pipeline, supply the domain ground truth and independent judgment the machine can't, and hand your leadership the score — on any stack.
Three ways to engage.
- 01
AI Reliability Audit
A structured analysis of your AI feature — eval coverage, failure modes, observability gaps. You walk away with a named failure taxonomy, reproducible test cases, and a prioritized roadmap. Delivered in 1–3 weeks.
- 02
Eval-Driven Sprint
We instrument, test, and fix one named AI workflow. You receive working evals, fixed failure modes, a reliability baseline, and a regression suite that keeps it honest after we leave.
- 03
Continuous Reliability Retainer
Ongoing eval monitoring, monthly reliability reports, and on-call advisory. We watch your AI in production — independently — so your engineering team doesn't have to. AI reliability as a managed service.
The ZEAL Reliability Loop
Every engagement runs the same four-phase loop. A machine surfaces failures at scale; we supply the customer-support ground truth and independent judgment it can't. The loop produces a continuously-running eval workspace your team can operate after handoff.
A trace-mining agent clusters production conversations into named issues. We turn each cluster into a severity-rated failure mode that matters to your business — built against your policies, not a generic rubric.
Convert each failure mode into a binary evaluator. We define what 'correct' means in your domain and validate every LLM judge against human labels. Published validation scores.
Fix what the data ranks highest — prompts, tool descriptions, escalation thresholds, retrieval. Drafted as PRs for in-house agents; prioritized recommendations for vendor platforms.
Every confirmed failure becomes a permanent online evaluator and offline regression case, so it can't silently recur. Drift monitoring keeps watching after handoff. The system compounds.
Zeal Sentinel — AI Customer Support Auditing
Your AI support agent is making promises to customers. We independently audit whether it's keeping them. New eval tools let teams self-grade faster than ever — but faster self-grading is still self-grading. Sentinel is the independent layer between your vendor's metrics (and your own dashboards) and the truth your VP CX needs to show the board.
See Sentinel →Your AI vendor is grading their own homework. So is every tool you run yourself. We are not.
The stakes just went up.
Air Canada's AI chatbot was held legally liable for its outputs in February 2024 (Moffatt v. Air Canada). The deployer is responsible — not the AI vendor.
Only 37% of teams running AI agents evaluate them against live production traffic; nearly half don't run any offline tests before shipping. (LangChain, State of Agent Engineering 2025 — 1,340 respondents)
Automated eval agents (LangSmith Engine, Braintrust) make it easier than ever to grade your own AI. Easier self-grading raises, not lowers, the need for an independent audit layer.
A senior AI architect. No full-time hire required.
Subscribe to get reliable architectural guidance, async Slack access, and monthly AI reliability recommendations — scoped to your specific stack. Three engagement tiers, from periodic input to a weekly fractional-Head-of-AI cadence.
Periodic senior input. Slack access and a monthly architecture recommendation.
Regular counsel. Six hours per month, two recommendations, and a monthly review session.
Fractional Head of AI. Weekly cadence and a quarterly named-workflow review.
Ready to know if your AI is working?
Every AI automation ships with uncertainty. We turn that uncertainty into named, reproducible evidence — so your team can fix what matters and your leadership can see the score.