Strategy, operating cadence, team alignment and executive decision support.
AI initiatives
that win.
Simak Growth helps companies launch the AI initiative that wins its market: find the wedge, ship the first product people actually use, and prove its quality with evals before it scales. Led by Nikita Simakov.
4×monthly revenue
at Sidekick
at Sidekick
400Kpeak monthly users
at Gentlyx
at Gentlyx
~$1Mmobile ARR added
at VL Projects
at VL Projects
+12%search engagement
at Yandex
at Yandex
Product consulting,
from bet to build.
Senior product leadership where the problem is consequential, the path is still unclear, and shipping matters.
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
Use-case selection, product design, evaluation and implementation inside real company workflows.
Segmentation, experimentation, pricing and the systems needed to learn faster.
Evidence-led diagnosis followed by a practical roadmap and hands-on delivery leadership.
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
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.
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.
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; Yandex Lawrify, 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; Gentlyx, evals across chat AI pipelines and image generation.
The work,
case by case.
From consumer AI to search infrastructure, each role is documented through the problem, the work and the available evidence.
2025—NowVL ProjectsHead of Product · Consulting~$1M ARR added
Mobile as a growth engine
- Oct 2025 — now
- Fractional · consulting
- Consumer app portfolio · US & Europe
- With the CEO and data team
The work. Made the case to the CEO to prioritize mobile, then led a cross-functional experimentation roadmap across the app portfolio. Rebuilt cohort, behavioral and experiment analytics with the data team so segment-level product decisions run on data.
The outcome. Added approximately $1M in mobile ARR in 12 months on a roughly $2M baseline and shortened time-to-insight from 14 days to 3.
Read the full caseCompanyPE-style consumer app operator (Bending Spoons model), ~3M MAU CV
~$2Mbaseline+$1Min 12 months
2024—NowAI StatAI Product Lead · Kazakhstan statistics~10K MAU
Public statistics, conversationally accessible
- Dec 2024 — now
- Center for Behavioural Economics & Big Data Analysis
- Production LLM agent
The work. Prioritized the top three user journeys, designed the knowledge base and RAG retrieval layer, and took the assistant from pilot to full rollout.
The evals. Tracked the quality of every task the agent executed against SME benchmarks built with the bureau’s subject-matter specialists, then stress-tested it live during the Agricultural Census on roughly 8,500 questions before production.
The outcome. In production at around 10K MAU, including businesses, and 300–500 requests per day, with F1 around 88–90%.
Read the full caseCompanyNational AI assistant on stat.gov.kz, officially launched May 2026 source
National Statistics BureauKnowledge base→RAG retrieval→Agent task→SME benchmark→ProductionEvery task scored against expert answers · ~8.5K census questions
ConsultingSamruk EnergyAI implementation · Video analyticsScope: video analytics
AI in operational video analytics
Product consulting engagement focused on implementing AI into Samruk Energy’s video analytics workflows.
Company7.9 GW installed capacity, ~⅓ of Kazakhstan’s electricity output (2025) source
Visit Samruk EnergyConsultingMinistry of Industry & ConstructionRepublic of Kazakhstan · AI implementationScope: AI implementation
Practical AI for public-sector workflows
Product consulting engagement focused on practical AI implementation with the Ministry of Industry and Construction of the Republic of Kazakhstan.
CompanyFormed September 2023; oversees industry, mining, construction and housing source
Visit the Ministry2024—2025Gentlyx / LovelFractional CPO · Consumer AI300–400K MAU
From concept to break-even consumer AI
- Aug 2024 — Sep 2025
- Fractional CPO
- 12-person team
- Chat AI + image generation
The work. Launched the app 0→1 and was responsible for the chat AI pipelines and the image-generation flow end to end: prompt and model orchestration, output quality, latency and per-request cost. Built the operating model and delivery process for a 12-person team.
The evals. Ran evals on both the chat pipelines and the image-generation flow, so output quality was tracked alongside latency and cost per request.
The outcome. Scaled to 300–400K MAU and reached break-even after 11 months at approximately $25K monthly revenue.
Read the full case Visit Lovel300–400KMAU at scale
- M0Launch 0→1
- M11Break-even · ~$25K/mo
Chat AI pipelinesImage generationEvals on both
2022—2024Sidekick BrowserCPO · Acquired by Perplexity4× revenue
Finding the cohorts that compound
- Dec 2022 — Aug 2024
- 20+ person cross-functional org
- Consumer + B2B
The work. Owned the 12–18 month product strategy, roadmap and consumer growth funnel. Narrowed more than ten user segments to the four strongest PMF cohorts, ran pricing, paywall, messaging and localization experiments, and built B2B from zero while cutting time-to-market by about 50%.
The outcome. Lifted activation from roughly 60% to 77%, tripled DAU in 18 months, grew monthly revenue 4× to approximately $700K, and reached 200+ B2B teams in three months. Sidekick was later acquired by Perplexity.
Read the full caseCompanyKleiner Perkins-backed; acquired by Perplexity in May 2025 source
Sidekick joined PerplexityBefore~60%
After77%
Acquired by Perplexity
2024—2025AvitoFractional PM · Revenue attribution & CRM~30% accuracy lift
One methodology across eleven businesses
- Oct 2024 — Oct 2025
- Fractional · consulting
- 11 business verticals
- Engineering to C-level
The work. Led a redesign of revenue attribution across 11 verticals, aligning engineering, analytics, finance, sales, vertical leads and C-level stakeholders on one methodology.
The outcome. Improved attribution accuracy by approximately 30% across a marketplace serving roughly 70M monthly users.
Read the full caseCompany72M+ monthly audience (2025), founded 2007 source
Visit AvitoOne
model
model
2021—2022YandexLead ML/AI Product Manager+12% engagement
Search, discovery and a legal AI agent
- Mar 2021 — Dec 2022
- 4 teams aligned
- Lawrify: 15-person team
The work. Aligned Search Ranking, NLU, Ads and Design around shared outcomes, and launched two recommendation and discovery surfaces in the Yandex SuperApp, winning distribution against 40+ competing internal services.
Lawrify Legal domain Built a legal AI agent 0→1 with a 15-person team, translating law-firm workflows into product requirements for legal data extraction.
The evals. Built Lawrify’s eval stack end to end: expert-labeled ground-truth sets, scoring rubrics, side-by-side model and prompt comparisons and a failure taxonomy. Human evals drove the quality and latency trade-off.
The outcome. Search engagement up 12% across SERP and Alice, ad revenue up about 1.1%, roughly 1.5M MAU on the discovery surfaces in two months, and LTV up 18% through attribution and experimentation tooling. Lawrify reached its first ~$10K in revenue in four months and cut response time from about 60 seconds to 15–20.
Read the full caseCompany75M+ MAU search and AI platform CV · ~69% of Russian search (Q4 2025) source
Visit Yandex75M+platform MAU
1.5Mdiscovery MAU
~$10Kfirst Lawrify revenue
2019—2021HoffProduct Manager · Mobile & checkout+7% payment completion
Simpler foundations, faster releases
- Mar 2019 — Apr 2021
- Mobile & checkout
- With the CTO
The work. Simplified the shared API surface with the CTO, modularized the mobile apps, and improved local payment methods and checkout UX for a major omnichannel retailer.
The outcome. Reduced API methods from roughly 200 to 120, cut mobile release cycles from four weeks to two, increased checkout conversion 0.8%, and improved payment completion 7%.
Read the full caseCompany~6M MAU omnichannel retailer CV · 60+ stores in 27 regions; ₽80.7B revenue, ~30% online (2023) source
Visit Hoff~200 methods→~120 methods
VentureKarta / HeyCardLaunch PMFirst 100 paid users
Evidence-led launch
Defined the core customer segment and positioning from early user evidence, took the product to launch, and landed its first 100 paying users.
Company20,000+ monthly users (company-reported, 2026) source
Visit KartaConsultingWhiteLarkConsulting · GTM & positioning · PaymentsPositioning
Clarifying payment orchestration
Consulting engagement on go-to-market and positioning for WhiteLark, a payment orchestration platform.
Company50+ payment provider connections (company-reported) source
Visit WhiteLarkOutcomes shown here come from Nikita’s supplied resumes and confirmed consulting record; approximate notation is retained where provided. “Company” lines describe each organization, from public sources or Nikita’s CV as linked, and are not Nikita’s results.
Trusted when the
stakes get real.
First-hand accounts from founders who worked with Nikita through pivotal product moments.
“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.”
Building something
ambitious?
Tell me about the product and where it’s stuck, and I’ll reply by email.