Pipeline v2.3-DE
How Howzer works
A customer sends a message. Howzer reads it, understands the problem, scores the risk, finds patterns across thousands of messages, and drafts a professional reply. All in seconds.
Three stages, one pipeline
Every message flows through three stages. The entire process typically finishes in under 5 seconds.
Analyze
Multiple AI models examine the message simultaneously, detecting language, tone, emotions, the underlying problem, and business risk.
Enrich
The system adds business context: who is this customer, what do company policies say, and are other customers reporting the same issue?
Respond
A professional reply is drafted, routed to the right team, and flagged for human review when needed. Full audit trails included.
Analysis: understanding the message
Multiple AI models examine every message from different angles, building a complete picture in a single pass.
Language Detection
Identifies the language of the incoming message. The pipeline is optimized for German but handles other languages automatically. If the language can't be determined with high confidence, a multilingual mode kicks in.
Understanding Tone
Determines the overall tone: is the customer happy, unhappy, neutral, or expressing mixed feelings? Optimized for German text with high accuracy.
Detecting Emotions
Goes beyond positive or negative to identify specific emotions like anger, frustration, disappointment, fear, joy, and surprise. Smart logic prevents under-reporting on clearly negative messages.
Finding the Root Cause
Identifies what the customer is actually complaining about. Not just the words, but the underlying category of problem. Matches against known issue types and discovers new ones automatically.
Assessing Business Risk
Scores the overall business risk: will this customer leave? Will they escalate? Could this damage the brand publicly? The result is a risk score split into four tiers: Low, Medium, High, and Critical.
Enrichment: adding business context
After analysis, the system pulls in context from multiple sources to give the full picture. Who is this customer? What do the policies say? Are others reporting the same problem?
Text Metadata Auto-Extraction
German customer emails follow predictable structures. The system automatically extracts structured metadata from free text. No manual tagging required.
Customer History
Pulls the customer's full profile: how long they've been a member, past issues, whether their satisfaction is improving or declining, and whether they're at risk of leaving.
- Membership tier and tenure
- Interaction frequency (30, 90, 365 days)
- Sentiment trend: improving, stable, declining, or volatile
- Churn risk score based on behavioral signals
- Special flags (e.g. legal threat history, long tenure)
Policy Lookup
Searches internal policy documents to find the rules, coverage terms, and compensation guidelines relevant to this customer's issue. This prevents the system from making claims that don't match actual policies.
- General terms and conditions
- Service coverage details
- Compensation guidelines
- Escalation procedures
- GDPR and compliance rules
Pattern Detection
Looks across all recent feedback to find patterns. Are multiple customers reporting the same problem? Is there a regional spike? A seasonal trend? This turns individual messages into actionable intelligence.
- Geographic clusters (e.g. regional service issues)
- Time-based spikes (sudden complaint surges)
- Provider-specific problems
- Seasonal patterns
- Cascade patterns (issue → follow-up → cancellation)
Response: drafting the reply
The system generates a professional reply that references the actual issue, follows company policies, and matches the right tone.
Context-aware
Responses reference the full analysis context: sentiment, emotions, risk, customer history, and relevant policies.
Quality-checked
Every response is scored against quality standards before it reaches a reviewer. Below-threshold drafts are automatically flagged.
Runs locally
All response generation runs in your infrastructure. No data leaves your network.
Tone calibration
The response engine adjusts its tone based on the feedback type, from warm and brief for positive messages to composed and specific for critical issues.
Smart routing: the right message to the right team
After analysis and enrichment, the system decides who should handle each message. Low-risk items can be handled automatically, while critical issues are escalated immediately.
Auto-eligible
Low risk, low emotion intensity. Can potentially be sent without human review.
Standard review
Moderate risk or emotion. Routed to response engine with human review required.
Retention specialist
High churn risk and high customer value. Flagged for retention team, especially near renewal.
Escalation
Critical risk or legal threat history. Requires immediate human review at the highest tier.
Pattern-aware routing
When feedback is part of a known pattern (e.g. multiple customers reporting the same issue), the routing system flags it accordingly. This helps teams prioritize systemic issues over isolated ones.
Pipeline versions
Howzer maintains separate pipeline versions optimized for different languages.
German-optimized pipeline with native German sentiment, emotion detection, and root cause analysis.
English-language pipeline with language-specific optimizations.
Make risk visible. Respond with confidence.
Less noise, more clarity. Howzer helps your team hear the real message behind every request, so replies feel fast, fair, and human.