Evals for AI agents & LLMs
Expert-labeled golden sets, scoring rubrics, side-by-side model and prompt comparisons, failure taxonomies, and regression suites that run before every model, prompt or retrieval change.
Nikita Simakov
Product leader across AI, consumer and B2B products. 7+ years turning uncertain bets into measurable businesses and shipped products.
Product consulting,
from bet to build.
Senior product leadership where the problem is consequential, the path is still unclear, and shipping matters.
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Fractional CPO
Strategy, operating cadence, team alignment and executive decision support.
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AI evals & safeguards
Evaluation systems for AI agents and LLMs: SME benchmarks, golden sets and scoring rubrics, safeguards against unsafe or off-policy output, and release gates that keep quality measured after launch. How it works
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AI strategy & implementation
Use-case selection, product design, evaluation and implementation inside real company workflows.
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Growth & monetization
Segmentation, experimentation, pricing and the systems needed to learn faster.
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Product audits & delivery
Evidence-led diagnosis followed by a practical roadmap and hands-on delivery leadership.
Selected clients & teams
Nothing ships
until it’s measured.
Core competence: the evaluation systems that decide whether an AI agent or LLM feature is accurate, safe and worth running inside a real workflow.
- Definethe task and what “good” means
- Test setreal cases, labeled with experts
- Runside-by-side model and prompt comparisons
- Gradeexpert and crowdsourced raters, scoring rubrics
- Release gateship only what passes
- Monitoruser feedback and a failure taxonomy
Safeguards
Adversarial and edge-case testing for hallucination, off-policy or unsafe output and data leakage, with guardrails and human escalation designed into the product rather than bolted on.
Workflow automation evals
Whether an agent actually finishes the real workflow: task completion, human handoff rate, cost of errors, and latency and spend per completed task, measured against the manual baseline.
In the record: AI Stat, every agent task scored against SME benchmarks and stress-tested on ~8,500 Agricultural Census questions before production; Lawrify at Yandex, a legal AI agent with an eval stack built end to end: expert-labeled golden sets, scoring rubrics, side-by-side model and prompt comparisons and a failure taxonomy; Lovel, evals across chat AI pipelines and image generation.
Trusted when the
stakes get real.
First-hand accounts from founders who worked with Nikita through pivotal product moments.
1 / 3
“Nikita introduced a disciplined product process where hypotheses were systematically tested, experiments were translated into measurable outcomes, and the team began shipping with consistent weekly cadence.”
“He combines strategic thinking with empathy, leadership with collaboration. He quickly earned the trust of both executives and team members, creating an atmosphere of ownership and accountability.”
“Mr. Simakov played a central role in shaping our product and growth strategy… transforming raw ideas into a coherent product roadmap.”