Revolutionary automatic self-learning system. The AI agent analyzes every dialogue, identifies patterns, and improves its prompts — without human intervention. With 50% anti-hallucination protection.
Why manual AI agent improvement is a dead end
Without updates, prompts become outdated. The bot repeats the same mistakes, loses relevance, and conversion drops month after month.
Reading dialogues, analyzing patterns, rewriting prompts — that's days of specialist work. And humans will still miss things.
Improvements are made chaotically: someone noticed a problem — fixed it. But 90% of other issues remain in blind spots.
Without validation, AI starts inventing non-existent links, promising features that don't exist, and referencing mythical "rule 45".
Fixed one thing — broke another. Without change history and contradiction checking, every update is a lottery.
When there are hundreds of dialogues per day, manual analysis is simply impossible. Automation is needed — but it requires complex infrastructure.
Complete cycle from dialogue analysis to applying improvements — every 4 hours
Every 4 hours, the system collects new dialogue analyses, aggregates recurring patterns (problems, customer needs, missed opportunities), passes them to 3 specialized AI agents for improvement generation, validates each change for hallucinations and policy compliance, and only after successful verification applies changes to the vector database. You receive a detailed Telegram report.
Each agent is responsible for its domain and improves the corresponding vector database
Analyzes sales strategies, identifies successful approaches, and suggests improvements to increase conversion.
Optimizes contact information collection methods with GDPR compliance and progressive disclosure principles.
Develops and improves dialogue scenarios for various customer interaction situations.
Every change goes through multi-level verification with weight coefficients
All URLs and resources are checked in knowledge_base and interaction_policies. All cross-references to rules and strategies — in reference DBs or target table.
Analysis of metadata.history_check, context_change_level. If substantial + edit → automatic rejection, delete + append recommended.
Does it solve a real problem from analysis? Are there degradation metrics? Is this a substantial improvement, not cosmetic?
Does knowledge_base search confirm capabilities? Doesn't promise non-existent features? Matches actual product workflow?
Bot stays in reactive mode? Proactive contacts only through managers, email and SMS?
GDPR compliance. No dark patterns. Honesty in scenarios. ROI justified. Side effects minimized.
50% of validation weight — verifying accuracy of all mentioned resources
Checked ONLY in reference databases. Strict policy — not found = rejection.
Three-stage check: first reference DBs, then target table.
Supabase + Embeddings for semantic search and knowledge storage
Product knowledge base — prices, features, algorithms, capabilities
Interaction policies — communication channels, rules, limitations
Sales strategies — argumentation, objection handling, deal closing
Contact capture methods — progressive disclosure, value exchange
Dialogue scenarios — use cases, escalation, branching logic
System works without your involvement — 24/7, every 4 hours
Automatic launch every 4 hours. 6 learning cycles per day. System automatically determines if there's new data to analyze.
Batch processing of all new dialogue analyses. Aggregation of recurring patterns. Iterative improvement.
Notifications for each cycle: start, progress, results. Detailed report with approved and rejected change counts.
Benefits of automatic self-learning for your business
AI agent gets smarter every 4 hours. Response quality grows exponentially without your involvement.
50% of validation weight on resource verification. AI will never invent non-existent links or features.
No need to read dialogues and rewrite prompts manually. The system does it automatically.
Pattern aggregation identifies frequent problems (≥2 times). Improvements target real customer pain points.
Logging every change in ai_learning_log. Telegram reports. You see what changed and why.
Multi-level validation. Contradiction checking. Changes don't break existing logic.
Advanced system for serious business