Every chat you run, turned into numbers,
not just a transcript nobody rereads
Thousands of chat conversations hold real signal about what is working and what is not, and nobody has time to read them all. We build a layer that classifies every conversation automatically, so patterns surface instead of staying buried in a chat log.
What it is and who needs it
A chat analytics product classifies conversations at scale, intent, sentiment, objections raised, where a conversation dropped off, turning a pile of chat logs nobody has time to read into patterns your team can act on. It fits any business running real conversation volume through sales or support channels where the content of those conversations holds information currently going unused. It is not worth building for low conversation volume a person can genuinely read through directly; the value is in scale a human cannot keep up with manually.
What is inside
Every conversation gets classified automatically on dimensions that matter for your business, intent, sentiment, which specific objection came up, where in the flow a prospect stopped responding, rather than a generic sentiment score that does not tell your team what to actually do differently. A dashboard aggregates classified conversations into patterns, which objection is costing the most conversions this month, which channel’s conversations run more positive, surfacing what would otherwise require reading hundreds of chats by hand. Alerting flags sudden shifts, a spike in a particular complaint, a drop in close rate on one channel, so your team notices a real change quickly instead of weeks later in a monthly report. Conversation data is handled with privacy scoped tightly to what the analysis actually needs, not retained or exposed beyond that purpose.
How we build it
We start by defining, with you, what classifications actually matter for decisions your team makes, since generic sentiment scoring often does not map to anything actionable. Classification gets validated against a sample of conversations your team has already read and categorized by hand, confirming the model’s tags match human judgment before trusting it at scale. The dashboard gets built around the specific patterns your team wants to track, not a generic analytics template. We launch on one channel, validate that the classifications genuinely reflect what is happening in those conversations, then expand to additional channels.
What to watch
The real risk is classification that technically runs but does not reflect what your team actually cares about, a sentiment score that is directionally fine but useless for deciding what to fix, which is why classification dimensions get defined with your team’s real decisions in mind before any model call happens. Privacy handling of conversation data is the other serious consideration, especially across jurisdictions with different rules, so data retention and access get scoped tightly to what the analysis genuinely needs rather than kept indefinitely by default. Revisit the classification categories periodically, since what matters to track tends to shift as your sales or support process evolves. Review the classification categories with your team every few months, since what counts as a meaningful signal tends to shift as your sales or support process itself changes.
Timeline and price
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| MVP | from $1,800 | One channel, intent and sentiment classification, basic dashboard | 4 to 5 weeks |
| Production | from $4,500 | Multi-channel, objection and drop-off tracking, shift alerting | 6 to 7 weeks |
| Full control (handover-ready) | from $7,650 | Everything in Production, plus a full handover package: architecture docs, test suite, admin access audit, and a walkthrough so your own team or another vendor can run it without us | 7 to 8 weeks |
Running cost on top of the build is usually $20 to $65 a month in model classification calls, depending on conversation volume.
What you own at the end
You own the classification logic, the stored results, the dashboard and the full source code, running on your own infrastructure. Documentation explains exactly what each classification dimension measures, so your team can trust and extend the system without us.
Related
Pairs with the lead scoring product for turning conversation signal directly into prioritization, and the AI sales agent product for the conversations this often analyzes. See the analytics service page and the AI agents service page for related builds. Real builds: the seven-channel AI sales agent case study, with its 46-objection playbook, and the real estate CRM case study, which classified 382 dialogues and found a 2.7x cost difference between channels. Have thousands of chat logs nobody has time to actually read? Get in touch.
FAQ
How much does a chat analytics product cost?
From $1,800 for classification and a basic dashboard on one channel's conversations. Multi-channel coverage with objection tracking and alerting runs $4,500 to $7,500.
How long does it take?
Four to five weeks for one channel once you have a reasonable volume of historical conversations to classify against. Multi-channel coverage and deeper classification (objections, drop-off points) extends this to six to eight weeks.
What is the stack?
Python for the classification pipeline, Claude or GPT for understanding conversation content and intent, PostgreSQL for storing classified results, and a dashboard layer (usually Next.js) for the patterns your team needs to see.
Who owns the classified data and the dashboard?
You. The classification logic, the stored results and the code run on your own infrastructure, with conversation data handled according to whatever privacy rules apply to your business.
Can it tell us why a specific conversation did not convert?
Yes, classification tags the objections and drop-off points in individual conversations, so your team can read the handful of conversations the dashboard flags as representative, rather than reading all of them.