80% of deal cards in CRM contain incomplete data. Customer information is scattered across chats, calls, and emails, and managers spend up to 30% of their time searching for it. This is typical for companies using Bitrix24 without automation.
The CRM bot we have developed solves this: it reduces the time to handle a single deal by 40% and increases data accuracy to 95%. The average savings for a 10-manager team is substantial — data from our projects. Request a consultation to see how it works for your team.
The bot subscribes to CRM events via event.bind, uses REST API to read/write fields, and updates data in real time. Unlike standard Bitrix24 business processes, the bot handles 10x more events without delays. Response speed to an event is 5x faster than manual processing.
How the bot integrates with CRM?
The CRM bot is deployed at two key points in the Bitrix24 interface:
- Internal Bitrix24 chat — commands and notifications via the
immodule. - Widget inside the CRM card — via the CRM element mechanism (application with type
CRM_DETAIL_ACTIVITY), displayed directly in the deal or contact card.
The second option is more powerful: the manager never leaves the card, and the bot shows data and accepts commands right there. Implemented via REST application.
What CRM automation delivers?
The bot automates key customer touchpoints:
- Auto-fill cards after calls: transcription → NLP → write deal fields via
crm.deal.update. - Recommend next step based on stage and history: "Last contact 5 days ago, stage 'Proposal' — time to call."
- Enrich company data by INN or domain from DaData, SPARK, Clearbit.
- Summarize new lead history: via OpenAI API — extract all activities in 2 minutes.
Reducing manager workload
The bot subscribes to CRM events via event.bind or webhooks. Key events: crm.deal.onUpdate, crm.deal.onAdd, crm.activity.onAdd, crm.lead.onAdd.
Example logic on stage change:
def on_deal_update(event_data): deal_id = event_data['data']['FIELDS']['ID'] deal = bitrix.call('crm.deal.get', {'id': deal_id}) if deal['STAGE_ID'] == 'C2:PREPARATION': # stage "Proposal Preparation" bitrix.call('tasks.task.add', { 'fields': { 'TITLE': f'Prepare proposal for {deal["COMPANY_TITLE"]}', 'RESPONSIBLE_ID': deal['ASSIGNED_BY_ID'], 'DEADLINE': '+3 days', 'UF_CRM_TASK': [f'D_{deal_id}'] } }) Step-by-step bot setup guide
1. Create a REST application in Bitrix24 with CRM and IM permissions. 2. Set up webhooks for CRM events via `event.bind`. 3. Implement a microservice in Python/Node.js with processing logic. 4. Deploy the microservice on a server with persistent connection. 5. Test all scenarios in a test CRM.What's included in the work
| Stage | Description |
|---|---|
| Audit and design | Analysis of sales department processes, bot scenario description, solution architecture |
| Bot implementation | Microservice development (Python/Node.js), webhook setup, event subscription, NLP integration |
| Card widget | Placement in CRM_DETAIL_ACTIVITY, interface configuration |
| Testing | All scenario verification, load testing |
| Deployment and documentation | Server deployment, manager instructions |
| Training and support | Team session, 1 month support |
Case from our practice: bot assistant for B2B sales
Our client is a manufacturing company with 8 managers, average deal cycle of 45 days, and multiple approval stages. Problem: managers forgot to update CRM, lost negotiation threads after vacation.
What was done: the bot subscribed to crm.deal.onUpdate and crm.activity.onAdd. On each deal change — it updated the internal dialog state (Redis, key = deal_{ID}, value = JSON with history).
Pre-call briefing: 30 seconds before connection, the bot sent the last 3 activities, agreements, open questions.
Auto-meeting protocol: manager entered key points → bot formatted into a protocol attached to the deal + created tasks for next steps.
Loss trigger: if deal not updated for 7 days — bot messaged: "Deal not updated for 7 days, risk of loss." Buttons: call, write, archive.
Result: card fill rate increased from 40% to 85% in the first month, number of "forgotten" deals dropped 4x. Additional revenue amounted to millions of rubles per quarter.
Technical stack and effort
The bot is implemented as a separate microservice (Python/Node.js) with persistent connection via webhooks and REST API. For dialog state storage — Redis or PostgreSQL.
| Component | Effort |
|---|---|
| CRM event subscription and basic logic | 16-24 h |
| NLP/OpenAI integration for summarization | 16-32 h |
| Widget inside CRM card | 16-24 h |
| Data enrichment from external APIs | 8-16 h |
| Dialog state, Redis | 8-16 h |
| Deployment and monitoring | 8-16 h |
Development timeline: from 3 to 8 weeks depending on scenario complexity. Cost is calculated individually after audit. We have 10+ years of experience in Bitrix development, over 50 implemented projects — we guarantee quality and deadlines. Contact us for an accurate estimate for your business. Request a consultation to assess automation potential.