slava vidomanets

Mid-market teams investing in AI often get stuck between pilots that never scale and systems they can’t govern. Here’s what actually works 👇 I help mid-market companies ($20M–$1B revenue) deploy AI agents inside revenue and customer operations with full CRM attribution, security controls, and cross-team adoption without betting the business on unproven tools. I’m Slava Vidomanets, Managing Partner at StrataBlue. We’ve been building automation and operational systems since 2005, long before “AI agents” became a category across revenue operations, customer experience, and internal workflows. Where most teams get stuck: - AI pilots that never make it past one team - Inbound leads going cold outside SLA windows - Call, chat, and form volume with no reliable routing or attribution - Manual QA and escalation creating risk at scale - Legacy systems and CRMs that require governance, approvals, and auditability So here's what we actually deploy to solve all of those issues): 1️⃣ Frontline Interaction & Intake Agents AI agents that handle, qualify, route, and log inbound interactions across sales inquiries, customer support, internal requests (HR, IT, Ops) via calls, chat, email, and forms with structured data written directly to systems of record (CRM, ticketing, HRIS). 2️⃣ Workflow Execution & Follow-Through Agents Agents that operate inside existing workflows to progress work that usually stalls, including lead follow-up, case resolution, employee requests, onboarding steps, and internal approvals - all compliant, logged, and attributable. 3️⃣ Oversight, QA & Risk-Control Agents AI that continuously reviews interactions and actions, enforces standards, flags risk, and escalates exceptions across sales, service, and internal teams which increases coverage and governance without increasing headcount. Results delivered include: - $680,000 in pipeline influenced in 45 days through AI-assisted conversations - $300,000 recovered from stalled and missed follow-ups within 30 days - SLA compliance improved across inbound channels - QA coverage increased without adding operational overhead - Hiring and escalation cycles reduced from days to minutes Our deployments typically run on 60–120 day rollout and optimization cycles, starting with one workflow and expanding across teams once benchmarks are met. If you’re evaluating AI agents and care about data flow, permissions, reporting, and risk, we should probably talk.