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Битрикс24 Day – большая презентация Нового Битрикс24. 14 мая 2026

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Key takeaways

Business

Business-focused summary (Bitrix24 Day 2026: “New Bitrix24”, 14 May 2026)

Core business thesis: AI shifts work from “tools” to “agents + automation”

The presenters frame AI as a step-change in how companies execute work: from searching/typing to getting decisions and execution done via agents, with communication, tasks, CRM, and even development increasingly automated.

1) Key strategic frameworks / operating playbooks emphasized

No formal OKRs/SWOT were stated, but several operating concepts and process patterns were repeatedly used:

  • Agent cycle (execution loop)

    • Agent perceives the situation → thinks what to do → uses tools (search/code/actions) → checks the result → repeats until the goal is met.

  • Decomposition → orchestration

    • Large tasks are broken into sub-tasks handled by multiple agents, then composed into a final deliverable (e.g., presentations/articles).

  • “One request → one outcome” task philosophy

    • A task should converge to a single result; AI enriches tasks from chat/audio/video inputs.

2) Product strategy & operational changes in Bitrix24 (big releases)

A) Collaboration: new AI-first communication layer in Messenger

  • Chat becomes the primary collaboration interface (text + files + voice/video + reactions).
  • Chat folders
    • Mobile now; later web/desktop.
    • Used to organize conversation contexts.
  • Bitrix24 Syn (calls + recording)
    • One-click joining.
    • Cloud recording automatically shared.
  • Bitrix24 GPT follow-up after calls
    • Automatically analyzes calls and generates:
      • soft-skill feedback
      • meeting objectives / actions
    • Emphasis: meeting knowledge should not be “lost” after the call.

Concrete operational KPI claimed

  • ~1,000,000 stickers per month sent internally on average (signal for emotional/chat engagement).

B) “Chats with external participants” (deal-making without granting full access)

  • External users can be invited into company chats via link/phone/email without Bitrix24 registration/authorization.
  • Access is scoped to conversation content: history/files/audio/video.

C) Tasks & projects: AI generates work artifacts from conversation

  • AI audio/video tasks

    • User sends a voice/video instruction in chat.
    • Bitrix GPT identifies the task and converts it into a structured task:
      • description
      • performers
      • checklist

  • Project AI (new “AI projects”)

    • Centralizes fragmented project communication into one workspace:
      • project chat
      • files, tasks, meetings, calendar
    • Includes multiple project agents (described as):
      • Agent 1: weekly info collection + publishing progress/analytics
      • Agent 2: personal assistant per employee (creates the optimal next set of tasks)
      • Agent 3 (implied): context-aware agent that uses full project context to answer specifically

  • Migration rule

    • Existing groups/projects/collaboration are automatically converted into new AI projects, preserving data.

D) Knowledge Base (AI-ready collaborative documentation)

  • New Bitrix24 Knowledge Base with:
    • modern block editor (text + tables + images + docs)
    • role-based access per article/section
    • collaborative editing inside Bitrix24
  • Positioned as an AI input asset for downstream automation.

E) Mobile operations: Check-in 2.0 + geolocation + mobile-first task plans

Designed for field/shift workers (presented as “up to 80% of employees” may not use a computer).

Check-in 2.0 capabilities

  • Start-of-day “emotion-enabled” check-in
  • Shift attendance metrics for managers (shifts worked / absences)
  • Geocoordinates verification
  • Multiple check-ins per worksite/object with result attachments
  • Work plans & daily reports generated by Bitrix GPT:
    • plans appear in the employee’s chat
    • daily summaries sent to the employee
    • roll up to manager reports

F) Bitrix GPT 55: performance + multimodal agent behavior

  • Claim: Bitrix GPT runs on “Contours of Russia” for low latency.
  • Adoption metric: >88% of employees on paid tariffs use Bitrix GPT in daily work.
  • Subscription model:
    • Bitrix GPT “unlimited” + 5,000+ apps from the marketplace.

Bitrix GPT 55 “agent” features

  • Thinking mode: shows reasoning process + more detailed answers
  • Online mode: if it lacks an answer, it goes online to collect and synthesize
  • Multimodal: images + documents/PDFs/tables
  • Long-context: understands tasks and related CRM/work artifacts

Rollout timeline

  • Bitrix GPT 55 agent mode available tomorrow on all portals
  • “Agency mode” rollout:
    • by end of May: all portals receive agent mode
    • by end of July: complete multimodal agent rollout

3) Wipecoding / Wipecode platform (the biggest operational shift)

Presented as enabling employees to create real working applications without traditional development bottlenecks.

How it works (operational model)

  • User generates an app by giving:
    • a key
    • documentation
    • what they want to build
  • AI uses:
    • API “methods” and Bitrix24 data access permissions
    • a platform-specific deployment mechanism
  • Deployment is designed to be safe and fast (minutes).

Security & governance mechanisms emphasized

  • Blackhole servers
    • isolated/invisible servers not directly reachable from the internet
    • authorization-scoped access to needed data/apps
  • Admin controls:
    • access scopes
    • white/black lists
    • restrict model access and time-based rules

Economic model emphasized

  • Pricing:
    • pay per minute of deployment/usage
    • “day-night rates” (server sleeps/wakes automatically; example mentioned: 29 days)

Platform adoption metrics (speaker)

  • 1,583 users
  • ~15,000 portals
  • 440 applications created
  • ~500–605 keys/servers (subtitle counts partially garbled; latest mentioned: 49 new servers added just yesterday)
  • Claim: active commercial usage by clients/partners; “incredible reviews”.

4) CRM & Digital Workplaces: “CRM AI” + speech analytics + agent-led sales operations

CRM AI: automate deal creation & enrichment from calls/chats/emails

Key automation stages

  1. CRM setup using Bitrix GPT Suggests fields, stages, funnel, kanban transitions.

  2. Call processing

    • call → speech-to-text → extract deal fields → auto-fill CRM
    • reduces manual time; example claim: up to 15 minutes of manager time saved per call
  3. Chat processing
    • extracts deal info and next actions
    • captures “promises” into the deal so managers don’t re-listen/re-read calls
  4. Spam protection
    • AI identifies spam calls/chats/emails
    • adds contacts to CRM exceptions to prevent deal creation

Business messaging

  • “Less effort, fewer people needed” to reach results via automation.

Speech Analytics 2.0 (automated script management)

Problem addressed

  • Managers previously maintained sales scripts manually (holidays, new offers, multiple business areas).

New approach

  • AI selects and updates the “right script” for each call type and manager performance.
  • Evaluates:
    • compliance with script
    • soft skills
    • recommendations for improvement
  • Output delivered into messenger as bot digests / operational control.

Operational KPI claimed

  • AI scenarios save up to 40% of managers’ time
  • Presented as significant relief due to script maintenance complexity (usage cited as “third place”).

Repeat sales automation: conversion & NPS use case

Repeat sales conversion metrics

  • Current repeat-sales conversion: >26%, growing
  • Company audits:
    • 48% in one company
    • 56% in another
  • Target guidance:
    • aim to bring repeated deals closed to ~50% (implied KPI goal)

NPS system built via Wipecoding

  • Custom forms collect customer NPS after purchase/service delivery.
  • If assessment is bad:
    • automatically connect a manager
    • triggers message + follow-up workflow
  • Engagement/conversion claims:
    • conversion started around 4%
    • later average 8.8% of voters
    • “more than 9,000 responses” processed

Experimentation / optimization

  • AI optimizes dispatch time:
    • earlier assumption: “evening”
    • tested and found best sending time for that client segment: 10:00 am
  • Result: increased response frequency; “conversion rates growing year-on-year”.

5) Online Booking: service resource reservation + AI call scripts

Online booking is positioned for service businesses such as:

  • beauty salons, clinics, car services, rentals, and similar providers.

End-to-end operational playbook

  • Booking grid blocks:
    • reserve specialists/resources per client
    • prevents double booking
  • Notifications chain (real-time):
    1. confirmation request the day before
    2. morning reminder, with rationale that clients can change plans in the morning rather than 1 hour before
    3. no-show handling: immediate message asking whether to wait/reschedule
    4. if no response: manager contacts client and rebooks an alternative slot
  • AI “AI call script” for live rescheduling:
    • agent calls client → checks availability → reschedules instantly

KPI/time-to-serve claim

  • If there’s no quick response, average client defection to a competitor happens within 10–15 minutes.

6) Concrete actionable recommendations (what companies should do)

  • Adopt agent-first workflows
    • orchestrate work in chat/voice/video so tasks can be auto-generated.
  • Centralize project context
    • stop scattering comms/files across channels; use AI projects to keep context for accurate agent answers.
  • Use the knowledge base as an AI training asset
    • publish internal process docs with access rights so AI answers reliably.
  • Enable AI in CRM operations
    • auto-fill deals from calls/chats; let AI handle spam filtering and promise tracking.
  • Use Wipecoding for internal automation
    • create internal mini-apps (dashboards, calculators, workflow automations) instead of waiting for dev cycles.
  • Govern with security controls
    • use blackhole servers + access scoping + admin policies for safe employee-generated apps.
  • Optimize sales operations with repeat sales + NPS
    • measure repeat-deal conversion and feedback loops; personalize outreach timing and content with AI.

Presenters / sources mentioned

  • Maxim Zrodovsky (restaurateur/entrepreneur; spoke about learning/using Wipecoding)
  • Andrey Karpaty (credited with coining “wipe coding / wipecoding”)
  • Sasha Vartanyan (prepared “Wipecoding for humanities students” course)
  • Yury (referenced as “talk more about the platform”; likely co-presenter)
  • Andrey (mentioned in context of “Come on, Andrey, talk more…”)
  • Additional named entity/source: Claude and Codex (model leaders mentioned as current approaches)
  • “Wipecode / Wipecoding agents platform” and Bitrix24 leadership team (multiple speakers; names not consistently clear due to subtitle quality)

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