Your support logs already say what's wrong, turn them into structured insights
AI that classifies and summarizes support, sales and call-center dialogs at scale. Topics, satisfaction, agent patterns and training-data extraction, built originally for telecom support analytics.
Discuss a pilot →Quick facts
- Business size
- Operations with more than 10k conversations a month in telecom, banking, e-commerce, call centers
- Timeline
- 3-5 weeks test, 2-3 months pilot, 3-5 months production
- Budget range
- Pilot from €25k.
- Hardware
- Cloud-based.
- Data needed
- Dialog corpus (text or transcribed voice), topic taxonomy (or willingness to define).
- Evolution
-
- Genesis
- Custom-built
- Product
- Commodity
A vendor sells this result ready-made. We set it up and tune it to you.
What this scale means
Further right means more proven and cheaper. Further left means newer and riskier. Here is the test for each step.
- Commodity
- You could get the result yourself from a ready service, with almost no work. We rarely take these on.
- Product
- A vendor already sells this result turnkey, like shelf recognition from Trax or document reading from ABBYY. If one of them fits you, use it. You come to us when it does not: when it has to run on your own servers, cost less, or fit systems the product cannot reach.
- Custom-built
- Vendors sell only the parts. A tool like Tableau hands you charts, but the dashboards and metrics for your business still have to be built. That build is the work, and that is us.
- Genesis
- The approach exists but does not work reliably yet. You are betting on it maturing, so it costs more and carries more risk.
Expected outcomes
The Problem
Support operations generate enormous dialog corpora that nobody reads: chat logs, call transcripts, email threads. Sample-based QA reviews 1-5% of conversations; the rest remain invisible. The result: issue spikes get noticed weeks late; agent training stays anecdotal; chatbots that should learn from agent conversations don’t.
The historical alternatives are keyword-based topic tagging and manual QA sampling. They produce coarse, lagged, and inconsistent analytics. Real value requires reading every dialog at the structured-output level.
LLMs make this tractable. A dialog corpus of 100,000 conversations can be classified, summarized, and analyzed in hours of compute time, surfacing patterns invisible at the sample level.
What the Solution Does
A dialog-processing pipeline that classifies every conversation and surfaces structured insights.
- Ingest: dialog corpus (chat, email, transcribed voice).
- Per-dialog classification: topic, sub-topic, sentiment, resolution status, satisfaction signal, agent persona signals.
- Aggregation: patterns across volume, top topics, sentiment trends, agent benchmarks, time-of-day patterns.
- Anomaly detection: spikes in specific topics, sentiment shifts, unusual escalations.
- Training-data extraction: well-handled dialogs become reference examples for chatbot improvement and agent training.
Where It Fits
This makes sense if you…
- Operate support, sales or call-center with more than 10k dialogs a month
- See real cost from delayed issue-spike detection or unsystematic agent training
- Have dialog corpus (text or voice with transcription)
- Want analytics beyond what manual QA produces
This is probably not the right time if you…
- Operate small volume where manual review is sufficient
- Have voice-only dialogs without transcription infrastructure
- Cannot share dialog data with cloud LLMs (self-hosted available, with additional setup)
Business Value
Coverage. From 5% sampling to 100% analysis. Patterns invisible at sample-level become visible.
Speed. Topic spike that would take weeks to surface via manual QA gets surfaced in hours via automated analytics.
Discovery of unmeasured patterns. Almost every deployment finds 10+ topic patterns that were happening but weren’t being measured.
Chatbot training-data. Well-handled dialogs become the training corpus for chatbot improvement. This is the downstream value with the largest return.
Agent benchmarking. Aggregated patterns surface high-performing agent techniques and outliers needing intervention. Without dialog analytics, this is anecdote-driven.
How It Works
The architecture is similar to feedback-analysis but applied to two-party dialogs.
1. Topic taxonomy
We co-design with you. Typically 5-9 top-level categories per domain. The national telecom operator deployment used categories specific to telecom: support, billing, technical, account, services.
2. Per-dialog LLM classification
Each dialog passes through an LLM chain:
- Translate (if multilingual)
- Classify topic / sub-topic
- Classify sentiment per turn (or per dialog)
- Detect resolution status (resolved / unresolved / escalated)
- Extract structured signals (mentions of specific issues, brands, products)
This follows the feedback-analysis pattern: one LLM call per dimension. Asking for everything at once is less accurate and harder to debug.
3. Aggregation and analytics
Per-topic volumes, sentiment trends, resolution rates, escalation patterns. Daily / weekly / monthly trends. Drill-down to specific dialogs.
4. Anomaly detection
Statistical alerts on topic spikes, sentiment dips, unusual escalation patterns.
5. Training-data extraction
Dialogs with successful resolution and positive sentiment get tagged as reference examples for chatbot training or agent training material. Optionally, GPT generates instruction summaries (“how to handle this kind of question”) from successful dialogs, as the national telecom operator deployment did.
6. Continuous improvement
Topic taxonomy gets reviewed quarterly. Classification quality measured against held-out manually-labeled samples. Edge cases feed retraining.
Stack
OpenAI / Anthropic / self-hosted LLMs, ASR for voice transcription (when needed), Datapipe for the pipeline, Metabase / PowerBI for dashboards.
What You Need to Make This Work
Data. Dialog corpus. Topic taxonomy (or willingness to define).
Integrations. Read access to dialog source (chat platform, CRM, telephony with ASR). Write to your warehouse / dashboard.
Hardware. Cloud-based. Self-hosted LLM for data-residency-strict deployments.
Team. Support-ops lead. Quality / training lead. Data engineer.
Implementation Roadmap
1. Test (3-5 weeks)
Define taxonomy. Run pipeline on historical corpus. Validate accuracy. Output: working analytics with measured numbers.
2. Pilot (2-3 months)
Production deployment. Build dashboards. Wire up alerting. Output: working dashboards in use, documented business outcomes.
3. Production (3-5 months)
Full rollout. Continuous improvement. Optional: feed extracted training-data into chatbot improvement cycle.
Keep in Mind
- Topic taxonomy is design work. Get it wrong and the analytics are useless. Co-design carefully.
- LLM cost is real at high volume. Dedupe and smaller models for some steps keep costs manageable.
- Voice transcription quality matters. Bad ASR produces bad classification. We measure transcription quality first.
- GDPR / privacy. Dialog data is sensitive. Self-hosted LLM and on-prem deployment supported.
- Don’t use this for performance discipline. Aggregated patterns are fine. Using individual-agent monitoring as a discipline tool produces toxic dynamics. Use it for training. Surveillance backfires.
FAQ
Voice or text?
Both. Voice needs ASR first; text is direct. We can integrate with Google STT, Whisper, or self-hosted ASR.
Multi-language?
Yes. Translate-to-common-language approach (similar to feedback-analysis).
How does this compare to commercial conversation-analytics (Calabrio, NICE, Chorus, Gong)?
Commercial platforms are excellent for organizations that want a ready-made product to configure. Our approach fits when you need a custom taxonomy, a self-hosted LLM, integration with non-standard dialog sources, or substantial customization.
Can the extracted instructions feed our chatbot?
Yes, that integration delivers the most. Good dialogs become reference examples for chatbot improvement.
Ready to Discuss?
If you operate high-volume dialog operations and your current QA is sample-based and lagged, this pilot pays off quickly. We’ll walk through your dialog volume, your topics, and your current analytics, and tell you what to expect.
Case studies
Stell Stell dialogue framework AI Assistants