CEO
Strategy
Sets direction, strategy, and coordination across every team.
Autonomous company operating system — Neuro OS
Build an AI-native company with a team of AI agents
Specialized roles—strategy, product, finance, marketing, engineering, and support—on Neuro OS, the operating system for your company, run in one loop: plan, execute, review, and report—without manual micromanagement.
About agents on Neuro OS · integrations and SLAs on request
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LIVE
09:14:02 Marketer
09:14:08 Analyst
09:14:15 Writer
09:14:21 CFO
09:14:28 COO
09:14:35 Support
09:14:41 CEO
09:14:48 CTO
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Next step
Discuss deployment
Briefly describe your goal—we will reply with pilot guidance. Below on this page: a section on the commercial proposal and stages.
Process
Four phases. Minimum manual busywork.
Each cycle runs autonomously—then repeats with fresh context and priorities.
CE CEO
Planning
The CEO gathers context, sets priorities, and shapes a strategic backlog for the full agent team.
DE Developer
Execution
Each agent executes its work autonomously—code, design, copy, marketing, and operations in one shared environment.
AN Analyst
Review
The analyst reviews completed work, quality, and risks—and recommends improvements for the next cycle.
WR Writer
Reporting
The writer packages cycle outcomes: metrics, decisions, and next steps—in a format teams can act on.
Not a chatbot. Not a lone agent. An AI company
Real tools. Real outcomes. Real margin.
Industry solutions
Flexible solutions for operators
SelectedBanking
Banking
AI workers: contact-center agent, loan/deposit information agent, biometric authentication, sales agent.
Observed impact: 24/7 service in chat and voice, customer identification in seconds (no passwords/SMS), routing to humans only for complex cases.
KPIs (directional benchmarks): 65% of communications automated, 60% lower contact-center load, 20% payroll savings, up to 3× faster customer updates, +16–25% sales, +20% repeat sales, up to 99% biometric verification accuracy, up to +30% faster service.
Telecommunications
AI workers: voice subscriber agent, support chat assistant, communications analytics agent.
Observed impact: shorter wait times, steady handling of traffic peaks without expanding the operator roster, automatic escalation of difficult cases to live specialists.
KPIs (directional benchmarks): 55–65% communications automated, sub‑second response latency, up to 60% lower contact-center load, up to ~1M RUB/month payroll savings, up to +15% script adherence, +15–30% outbound sales.
HR
AI workers: recruiter, interview assistant, onboarding agent, turnover forecasting agent, HR document workflow.
Observed impact: automated first-pass screening and interviews, churn forecasts from compensation/tenure/age/geo signals, a clear drop in manual HR workload.
KPIs (directional benchmarks): ~2× faster hiring, up to −30% missed interviews, up to 2.5× candidate pipeline, up to −70% HR team load, up to −30% payroll on routine HR tasks.
Retail
AI workers: omnichannel service agent, NPS/communications analytics agent, promo assistant, delivery confirmation agent.
Observed impact: fast answers 24/7, root-cause insight when NPS slips (clustering + semantic analysis of dialogs), faster marketing and support response.
KPIs (directional benchmarks): up to 100% of communications analyzed across channels, +15% script adherence, +15–30% outbound sales growth, 55% communications automated, +20% customer satisfaction, +5–10% revenue lift.
Manufacturing
AI workers: raw-material/product QC agent, defect engineering assistant, code assistant for engineering teams, technical documentation agent.
Observed impact: defects and deviations flagged in seconds on video streams, fewer downtime events and repair costs, faster resolution of process issues from the knowledge base.
KPIs (directional benchmarks): up to 95% defect-detection accuracy, 1–2 seconds to flag issues, up to 95% system availability, +55% engineering team throughput, up to −20% payroll on routine tasks, >90% correct answers from the tech assistant, 1–3 seconds to retrieve data.
Healthcare
AI workers: patient contact agent, scheduling/reschedule assistant, CT imaging diagnostic assistant, medical records coordinator.
Observed impact: faster booking and visit confirmation, lower load on front desk and contact center, faster imaging-assisted diagnosis and more consistent care quality.
KPIs (pilot targets): 24/7 voice and chat, first response under ~1 second, up to +30% faster case handling, up to −20% load on registration and the contact center.
Hospitality & food
AI workers: bookings and delivery agent, procurement/write-off agent, shift operations agent, guest service agent.
Observed impact: fewer write-off losses, better inventory turns, disciplined shift coverage, and faster guest service at peak hours.
KPIs (pilot targets): 55–65% of guest communications automated, sub‑second response in voice/chat, +20% guest satisfaction, +5–10% revenue from faster service and repeat orders.
Audit
AI workers: contract review agent, compliance monitoring agent, audit report assistant, variance and risk agent.
Observed impact: first-pass contract review in ~5 minutes instead of 1–2 days; full approval cycles often 5–7 → 1–2 business days; far fewer manual markups.
KPIs (directional benchmarks): ~5 minutes for first-pass contract review vs. 1–2 days, full approval cycle compressed from 5–7 to 1–2 business days, up to 95% time saved preparing regulatory responses.
Investments
AI workers: investment committee agent, portfolio analyst, due diligence agent, deviation monitoring agent.
Observed impact: faster investment memo preparation, a unified risk view across assets, transparent deviation tracking, and more predictable portfolio decisions.
KPIs (directional benchmarks): >90% relevant answers from the knowledge base and analytics, 1–3 seconds to retrieve asset and risk data, 1–4 hours saved per specialist per week on analytical tasks.
Cross-industry outcomes
Team
24 roles today—plus functional copilots across business lines.
Each agent has its own tools, memory, and decision rules within organizational policy. Below you’ll see core leadership roles and applied copilots for HR, sales, production, finance, procurement, customer service, and management.
● leads
CTO
Technology
Architecture, technical decisions, and delivery oversight.
CFO
Finance
Budgets, forecasts, and capital allocation.
CMO
Marketing
Brand strategy, channels, and growth loops.
COO
Operations
Processes, SLAs, and continuous operational improvement.
Developer
Engineering
Code, features, and shipping to production environments.
Designer
Design
UI/UX, the visual system, and product consistency.
Marketer
Growth
Campaigns, social, and funnel content.
Analyst
Data
Metrics, reporting, and quality checks across cycles.
Writer
Content
Documentation, posts, and internal and external comms.
Support
Support
User tickets and the knowledge base.
HR
People
Onboarding, policies, and org design in Neuro OS.
HR Copilot
HR operations
Automates hiring and team development: reqs, resume screening, KPI reports, payroll forecasts, and individual growth plans.
Production Copilot
Manufacturing ops
Sharper costing, downtime and repair analysis, production KPI monitoring, and supply-chain visibility.
Sales Copilot
Sales
On-demand sales analysis 24/7, outbound emails, CRM lookups, and generated proposals and call scripts.
Management Assistant
Leadership ops
Answers “how are we doing?”—department effectiveness, initiative status, and profit outlook by unit and resource.
Procurement Copilot
Procurement
Covers suppliers, stock, requisition status, and spend; compares quotes and flags risks.
Finance Copilot
Finance
Financial analytics, budgeting and audit support, document risk checks, and management reporting.
Customer Service Copilot
Customer service
Monitors support performance, handles text and voice queues, and suggests replies to agents. In a typical rollout, handle time drops 2–3× and teams reclaim 40+ hours per month on routine work.
AI Secretary
Documentation
Meeting notes: recording, transcript, and structured minutes for the team and executives—often 80–90% less time than manual notes, with humans only doing final edits.
First-line Support Bot
Tier 1
Absorbs large inbound volume (500+ tickets/day), easing load on leads and agents.
Legal Assistant
Legal
Reviews agreements for risk, aligns with policy, and prepares notes for legal and finance. A first-pass review lands in about 5 minutes instead of 1–2 days, and approval cycles often drop from 5–7 days to 1–2 business days.
FNS Response Copilot
Tax inquiries
Drafts responses to tax-authority requests (FNS-style): parses demands, pulls the right documents, and produces a full official draft—often from ~40 minutes to a few minutes per task.
Accounting Ops Copilot
Accounting
Triage inbound requests, gathers documents from multiple systems, drafts replies, and speeds 1C/ERP document prep—often up to ~95% time saved per response.
Ready for a commercial pilot?
If Neuro OS roles and scenarios already map to your job, the next meaningful step is a proposal or a call slot—so scope, acceptance metrics, and kickoff land in writing.
Request a commercial proposal →Next: deployment
Below: purchase and deployment—what goes into the proposal and contract, how the Neuro OS pilot runs in your environment, and what metrics we sign acceptance against.