Turn every conversation into a managed sales process
Analyse conversations against your sales book: check qualification and methodology, turn commitments into CRM actions and give leaders a view of the team.
Next: a short form and a discussion of your task. No files need to be uploaded now.
Demo
Aplora Sales demo interface. The figures on screen are sample data, not a client result.
A ready conversation summary
Commitments captured
Next actions
The data going into the CRM
A personalised follow-up draft
Prompts on missing qualification
Demo
Aplora Sales demo interface. The figures on screen are sample data, not a client result.
A quality score for the conversation
Adherence to your own methodology
Reasons deals are lost
Deals that need attention
Aggregated patterns across the team
Metric trends
Evidence snippets from the calls
Demo
Aplora Sales demo interface. The figures on screen are sample data, not a client result.
What the head of sales sees today
Manual QA covers a sample: a handful of recordings out of hundreds
The methodology exists on paper but doesn't take hold in behaviour
The CRM is filled in partially, and after the call rather than during it
Commitments and next actions get lost between the call and the e-mail
The outcome becomes visible too late to influence it
Sales leadership sees the outcome of conversations after the point where it could still act on it.
Turns calls, CRM data, and your own sales methodology into a system that drives management action.
Capabilities and details
The system reviews each conversation against your own sales book rather than a generic script: it checks qualification coverage, shows where a rep drifted from the methodology, fills CRM fields and tasks, drafts a personalised follow-up, and gives the head of sales a team-level view instead of a sample.
Reviews every conversation against your methodology criteria and turns the findings into actions in the CRM.
call transcription and structuring
lead qualification
methodology adherence
commitments and next actions
automatic CRM updates
personalised follow-up
analytics for the head of sales
Demo
Aplora Sales demo interface. The figures on screen are sample data, not a client result.
How it works
The route from input to outcome
Nodes expand: input, processing, output, control.
01Source
Call
Expand
Input
A recording from telephony or your conferencing tool.
Processing
The recording is ingested and matched to the deal and participants.
Output
A conversation queued for review.
Quality control
Recording rules — including exclusions — stay under the client's control.
Integration
Telephony, conferencing tools
02Automated step
Transcription
Expand
Input
The conversation audio track.
Processing
Speech-to-text with speaker separation and timecodes.
Output
A transcript you can return to at any point.
Quality control
Poor audio is flagged — no conclusions are drawn from a bad recording.
Integration
Recording storage
03AI step
Review against the sales book
Expand
Input
The transcript and your methodology criteria.
Processing
Checks required questions, objection handling, commitments and communication standards.
Output
A criterion-level score, each conclusion backed by the excerpt it came from.
Quality control
Contested cases route to manual review. The methodology version is recorded with the score.
04Business rule
Qualification
Expand
Input
The review output and your MQL/SQL definitions.
Processing
The collected information is matched against stage-exit criteria.
Output
A qualification status and a list of what is still unknown.
Quality control
Missing data is shown as missing rather than filled in by assumption.
05Target system
CRM fields and tasks
Expand
Input
The structured review output.
Processing
The deal record is updated and next-step tasks are created.
Output
A current deal record without manual data entry.
Quality control
Overwriting meaningful fields requires confirmation, and every change is logged.
Integration
CRM
06Human checkpoint
Follow-up
Expand
Input
Commitments, open questions and deal context.
Processing
A personalised message is drafted from what was actually said.
Output
A draft message for the rep.
Quality control
A person sends it. There is no unattended send to a client.
Integration
E-mail, messengers
07Monitoring
Leadership dashboard
Expand
Input
Scores, qualification statuses and CRM data across the team.
Processing
Patterns are aggregated; deals and conversations needing attention are surfaced.
Output
A team-level view that drills down to the specific excerpt.
Quality control
Visibility of individual scores is configurable — they need not be shown to the rep.
Feedback: monitoring returns data to the rules — the loop closes rather than ending at the last step
What the scoring rests on
The criteria are built from how you actually sell. The system brings no ready-made 'correct script'.
The system runs on your methodology, not a generic script
The client's sales book
MQL and SQL definitions
Required questions
Common objections
Commercial terms
Next-step rules
Communication standards
Funnel stages and stage-exit criteria
Why the conclusions can be trusted
Scorecards with transparent criteria — you can see what earned the score
A quote from the conversation beside every conclusion
An explicit confidence level, and a flag when data was insufficient
Manual review of contested cases
Methodology versioning: you can see which revision scored a conversation
A test set of calls for calibration
An audit trail of every change
Demo
Aplora Sales demo interface. The figures on screen are sample data, not a client result.
What it needs
call recordings from telephony or your conferencing tool
sales book, funnel stages and stage-exit criteria
MQL and SQL definitions
required qualification questions
deal and contact records from the CRM
What it produces
a structured conversation summary
commitments and next actions
populated CRM fields and tasks
a drafted personalised follow-up
a conversation score against methodology criteria
a leadership dashboard with trends and exceptions
What can be measured
Categories, not promised values. Numbers appear only in cases, with a baseline and a stated method.
share of calls actually reviewed
management time spent on QA
CRM field completeness
time to follow-up after a conversation
stage-to-stage conversion
share of conversations with full qualification coverage
rep adoption
What it connects to
A specific service is named once the integration is verified.
telephony and call recording
CRM
e-mail and SMS
calendar
Slack / Teams
your own APIs
Where the human stays
The rep sends the follow-up. The head of sales calibrates the criteria. The decision stays with a person.
How rollout works
1
Interviews and sales book collection
2
Building the scoring criteria
3
Testing against historical calls
4
Calibration with the head of sales
5
Integration with telephony and CRM
6
A limited launch
7
Team training
8
Monitoring and development
AI has to be governable, not just useful.
01Human-in-the-loop for critical actions
02Versioning of prompts, rules and models
03Test sets and manual QA
04Logs of inputs, outputs and the rules applied
05Error handling, retries and an exception queue
06Quality and cost monitoring
07Rollback
08Roles, access and data retention policy
How this differs from adjacent tools
Approach
What it gives
Limitation
AI notetaker
Recording and a conversation summary
Doesn't reflect your methodology or management context
CRM-native AI
Automation inside the CRM
Limited customisation of the process and the scoring criteria
Manual QA
A deep read of one specific conversation
Sampling, and an expensive use of management time
Aplora SalesOur approach
Methodology, instrumentation and action
Requires the sales leader to take part in calibration
The limitation of Aplora Sales is stated plainly: without calibrating the criteria together with the sales leader, the system will not work.
Cases
Where this has been applied
Manufacturing · Aplora Sales
Client under NDA
The best salesperson's playbook rolled out across a manufacturer's whole sales team
Conversion to a sale three months after rollout
+25%
Client figures: compared with the pre-rollout period in the same funnel
Conversion to a sale after six months
+72%
Client figures: compared with the pre-rollout period in the same funnel
Case details
The team's result rested on two people; the rest closed several times less on the same workload. Nobody knew what exactly the leaders did differently: conversations were not reviewed, and the gap was explained away as “experience”.
We validated the system inside our own operations first
Conversation review in an admissions funnel: how we validated the system on ourselves
Conversion from hot leads
×3.3
LearnIT figures, CommaCRM export
Share of calls that went nowhere
−40 pp
LearnIT figures, CommaCRM export
Case details
Conversation quality was judged from a few recordings a month. The reasons for a no stayed a hypothesis, commitments were captured from memory, and the CRM was filled in after the fact and only partially.
We validated the system inside our own operations first
Lead scoring in the intake funnel: advisors stopped calling everyone in order
Hot and cold leads sat mixed together. An advisor spent the day on people with neither a reason nor an intent, while leads showing clear readiness went cold in the queue.
We validated the system inside our own operations first
The funnel from lead to purchase in one picture: where the money was actually leaking
Google versus Facebook lead quality by conversion to sale
×3 in Google's favour
An observation from LearnIT's own funnel data over the period reviewed
Conversion at four or more touches versus one
×4–5
An observation from LearnIT's own funnel data over the period reviewed
Case details
Channels were compared on cost per lead rather than on the eventual sale, so a cheap channel looked better than an expensive one even when it brought people who don't buy. Nobody counted the losses between stages: for marketing the funnel ended at the webinar, for sales it began at the call.
Potential estimate: sales from the hot-lead pool identified
+14–22 sales
Aplora's calculation from a comparable segment's conversion: an estimate of the potential from calling 57 hot leads, not a measured result
We validated the system inside our own operations first
A sales candidate is assessed from a recorded conversation, not from the impression they leave
Hiring decisions were made on the impression the interview left. Interviewers assessed candidates differently, a mistake surfaced a month into the job, and the head of sales spent hours on reviews and arguments about who was better.
A school's sales team stopped depending on any one advisor's discipline
Progression to a second contact
+22%
Client figures: before and after within the same sales funnel
Overall conversion to a deal
+5 pp
Client figures: before and after within the same sales funnel
Case details
There was no priority, and strong leads were lost in the queue. Conversation quality was checked on a sample, follow-up depended on whether the advisor remembered, and the manager spent hours listening to recordings instead of working the bottlenecks.
Advisor time spent on admin
−70%
Client figures: their own measurement of CRM operations
Can conversations in different languages be analyzed?
Yes, where speech recognition quality is adequate for that language. We verify this on your own material before implementation: we take a set of real recordings and show how the system handles them. If quality is not good enough for a given language, we say so before any work starts.
How is our own methodology taken into account?
We start by collecting your sales book: funnel stages and stage-exit criteria, MQL and SQL definitions, required questions, common objections, commercial terms, next-step rules and communication standards. The scoring criteria are built from that. No generic script is imposed — and if the methodology does not yet exist in explicit form, capturing it comes first and is a separate piece of work.
Can the AI get it wrong?
Yes. That is why every conclusion carries the excerpt it came from, the system reports a confidence level, and cases with insufficient data are flagged separately. Contested reviews route to manual check, criteria are calibrated with the head of sales on a test set of calls, and every methodology change is versioned.
What does the rep see?
The conversation summary, the commitments captured, the next actions, the data going into the CRM, a follow-up draft, and prompts about qualification questions that were left unasked. A working tool, in other words — not just a score of their own performance.
Can we hide individual scores from reps?
Yes, visibility is role-based. In practice that is a common choice early on: until the criteria are calibrated, an individual score creates resistance and works against adoption. The head of sales still sees both the aggregate picture and the individual conversations.
How are call recording rules handled?
Recording rules and participant notification remain on your side and are configured in your own telephony — the system works with the recordings you pass to it. Requirements depend on jurisdiction and conversation type, so the specific procedure is agreed with your legal counsel before go-live.
Where are recordings and transcripts stored?
The hosting model is chosen to fit your requirements: inside your own perimeter, in a region you select, or on our side under an agreed retention policy. Retention period, access rights and deletion procedure are fixed in the contract before work begins, not decided along the way.
Which CRMs are supported?
We work with any CRM that exposes an API or a supported integration, including in-house systems. We verify yours during the assessment: what matters is not only that an API exists, but which fields are writable and how permissions are structured. A logo wall is no substitute for that check — we don't promise an integration before it.
Let's look at this workflow on your own material
Discuss your calls, methodology and team needs. We'll check whether the economics work and name the next step.
