Conversation Analysis
Call and chat analysis: structured data from every conversation
Send call recordings, voice feedback or chat transcripts through the API. MaeSense returns agreed fields with supporting quotes, timecodes and confidence — close to real time. Not a transcript, not a summary: data your team and your models can verify and use.
What goes in — and what comes back
You send
Conversations as they are
- Recorded sales and support calls (audio, no transcription needed)
- Voice feedback and voice messages
- Chat and messenger conversations
- Any text transcripts your systems already produce
You get back
Agreed fields for your process
- Customer need, mandatory parameters and facts
- Constraints, objections, conversation outcome and the agreed next step
- A structured note, request categories and reasons, checklist-based quality score
- For every important field: a supporting quote or timecode and a confidence score
Verifiable fields instead of a retelling
A summarizer returns a free-form retelling. MaeSense returns agreed fields with explicit states — so results can be checked against the source, compared across conversations and fed to your internal products and models.
- A value — or an explicit “not discussed” state
- A supporting quote or timecode from the conversation
- A confidence score for every important field
- A schema version for controlled integration updates
{
"need": {
"value": "family SUV, budget up to 35k",
"evidence": "…we need a crossover for the family,
thirty five is really the ceiling…",
"timecode": "01:42",
"confidence": 0.93
},
"trade_in": { "value": "not_discussed" },
"objections": [
{ "type": "delivery time",
"timecode": "04:10" }
],
"outcome": "test drive agreed",
"next_step": "test drive, Saturday 12:00",
"schema_version": "lead-context/1.4"
}One service — four ways teams use it
Real estate, auto, finance, e-commerce, services: the fields and categories are configured for your process, not the other way around.
Sales
Lead context and demand qualification
Need, mandatory parameters, constraints, objections and outcome from every sales call and chat — as a consistent data layer for your products, dashboards and ML models.
Sales & service teams
Call notes and profile enrichment
A uniform structured note with facts, missing details and the agreed next step — instead of free-form manual retelling. Your systems decide where the note lands.
Quality control
Conversation quality control
Checklist-based scoring for sales and support conversations: mandatory questions, missed steps, script compliance — every verdict backed by a quote or timecode, so reviews are verifiable rather than subjective.
Support & CX
Voice of the customer and feedback categorization
Categories, reasons, urgency and tone for calls, voice feedback and chats — one typology across channels, so complaint and churn analysis works on comparable data.
How we keep quality measurable
Our advantage is not a single exclusive model. We take responsibility for the measurable quality of the whole system — from model routing to the stability of your integration.
Several models, one result
We select and combine multiple speech and language models — English, Spanish, German, French, Russian and more — and take responsibility for the final format, not for a single model’s output.
Tuned on your real conversations
Fields, categories and reference examples are configured on a sample of your actual calls and chats — your taxonomy, applied consistently at any scale.
Reference sample and versioning
Quality is measured on an agreed reference sample with acceptance criteria. Every schema change is versioned and re-checked before it reaches your stream.
One format for voice and text
Calls, voice feedback and chat threads come back in the same schema — your internal products work with one data format regardless of the channel.
Plugs into your systems — without becoming one of them
API in, API out
You push audio or text and receive structured JSON back, close to real time. A stable, versioned format — no UI to retrain your team on.
Not a CRM
MaeSense does not store customer profiles and does not need names or phone numbers as separate fields. You decide how results connect to profiles and internal systems.
Data handling agreed upfront
Placement, masking, retention periods, access to the review sample and deletion of audio are agreed before the first recording is processed — in line with local data-protection requirements.
From first sample to production in weeks
Pricing is custom: it depends on your volume, channels and the solutions you launch. Start with a short brief — we will reply with a pilot plan and a quote.
Agree the schema
We pick one conversation type and 5–10 fields, agree examples and acceptance criteria on a small recent sample of your recordings.
Run a limited pilot
You send new recordings through the API; MaeSense returns structured results close to real time. Your specialists verify quality on a limited flow before you connect the results to production processes.
Scale what works
After acceptance, the same API and schema serve your production stream. New fields, checklists and categories are added as versioned updates.
Frequently asked questions
Which languages are supported?
How is this different from a call summarizer?
Do you need access to our CRM or customer profiles?
How do you handle personal data in recordings?
Can it work in real time?
Is this the same product as the AI Sales Agent?
Tell us about your conversations — get a pilot plan and a quote
Describe your call or chat stream in a short brief: channel, monthly volume and what you want to know from each conversation. We will reply with a concrete plan within one business day.