Scenario & metrics
One process, baseline time/errors, target KPI after automation.
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AI + Workflow
AI automation
Workflow and integrations first. LLM applied surgically where it removes manual work with measurable impact.
AI automation is not replacing BPM with ChatGPT. It is workflow, integrations and LLM where the model truly removes manual work: email classification, document field extraction, proposal assembly, lead qualification.
Bober AI Systems designs contours where 70–80% of logic is deterministic rules and API calls, and LLM is a narrow layer with eval and human fallback. That reduces hallucinations and inference cost.
Typical stack: n8n or custom Python orchestration, CRM/1C webhooks, private GigaChat for NLP steps, message queue for reliability. ELIA case: workflow + templates delivered main ROI; AI for non-standard wording.
We start with one scenario: inbound email → classify → CRM task; or invoice scan → OCR + LLM validation → posting. Next scenario after measured impact.
Production in 3–6 weeks. NDA, on-prem LLM on request. Budget from €4,000.
If AI is not needed — we say so on Discovery. Often n8n + CRM delivers value without a single LLM token.
When plain AI fails
- ChatGPT bot not connected to CRM — managers duplicate work
- LLM generates text but does not trigger system actions
- No orchestration: classification exists, routing is manual
- Pilot cannot be measured — no before/after metrics
Output
- Automation contour: triggers → rules → LLM → CRM/ERP actions
- n8n or custom workflow with logging and retry
- Eval set for AI steps and quality monitoring
- Documentation and team handover
Approach
Workflow design
Triggers, branches, human-in-the-loop, idempotent actions.
AI steps
Prompts, structured output, validation, eval on real data.
Production
Monitoring, alerts, runbook. Scale to adjacent processes.
AI automation stack
- Trigger layer: webhooks, email, schedule, CRM events
- Orchestrator: n8n / Temporal / custom Python
- Integration layer: CRM, 1C, Telegram, document storage
- AI layer: classification, extraction, generation with eval
- Observability: logs, metrics, dead-letter queue
Typical impact
−50%
manual steps in target scenario
3–6 wks
to production
2–4 mo
payback
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ELIA Suite — изолированные воркспейсы
+32% конверсия quote→заказ
15 воркспейсов без пересечения данных. 87% техзапросов через автоматизацию, −40% нагрузки на SAV, +32% конверсия quote→заказ. ROI за 4 месяца.
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OCR — извлечение текста из документов
Документы обрабатываются пакетно за минуты вместо часов ручного ввода, снижается доля ошибок при переносе данных и ускоряется закрытие периода.
View case study →FAQ
- n8n or custom code?
- n8n faster for integrations. Custom for strict SLA and complex logic.
- LLM quality control?
- Structured JSON, schema validation, sample eval, escalation on low confidence.
- Without cloud LLM?
- Yes. Private GigaChat or self-hosted — standard for enterprise.
- Example scenarios?
- Ticket classification, field extraction, proposal generation, lead qualification.
- Budget?
- From €4,000 for first production scenario.
- Support?
- SLA on workflow and prompt tuning optional.
Related services
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Workflow and integrations first. LLM applied surgically where it removes manual work with measurable impact.