AI Sales Management Platform (IoT)
A purpose-built field device that records every customer conversation and turns it into requirements, sentiment and next-best-action intelligence for management.
The brief
Field sales lived in a blind spot. Representatives met customers all day, but management only ever saw a short, subjective note written from memory hours later — if it was written at all. The real substance of each meeting, which products drew interest, what the customer actually needed, the objections, the tone, was lost the moment the rep walked out.
The client wanted to capture the ground truth of every customer interaction and turn it into structured, comparable intelligence the leadership could act on — without burying reps in paperwork or relying on what they happened to remember.
- Capture the ground truth of every customer visit, not a rep's after-the-fact note.
- Record conversations on a purpose-built device, with consent, and sync securely.
- Transcribe and mine each conversation for products, requirements and feedback.
- Score customer sentiment across the whole dialogue.
- Roll everything into a consolidated report to management with recommended actions and a sales prediction.
- Maintain a per-customer conversation history for context across visits.
Our AI-native approach
We built a purpose-built IoT device that the representative uses during a visit to record the customer conversation, with consent, and sync it securely once back in coverage. On the backend, an audio pipeline transcribes each recording and a language stack mines it for the products discussed, the customer's requirements and feedback, the sentiment across the dialogue, and a forward-looking sales prediction.
Everything rolls up automatically into a consolidated report submitted to management — complete with recommended next actions. Following our AI-driven agenda, we began by mining existing call notes and recordings during discovery to learn the client's products, the language reps and customers actually use, and the objections that recur.
What we built
Purpose-built recording device
A rugged IoT device the rep carries into the field captures the customer conversation with consent and syncs securely to the cloud.
Per-customer conversation history
Every visit is stored and organised into a searchable timeline per customer, so context carries across meetings.
Speech-to-text transcription
Recordings are transcribed into searchable text, handling the multilingual, code-mixed way field conversations really happen.
Requirement & product extraction
NLP pulls out the products discussed, the customer's stated needs and any feedback or objections raised.
Sentiment analysis
Customer sentiment and interest are scored across the conversation, not guessed from a rep's summary.
Consolidated management report
An automatic report goes up the chain with sentiment, requirements, recommended actions and a sales prediction for each visit.
How we built it
We began discovery by mining existing call notes and recordings to learn the client's products, the language reps and customers really use, and the recurring objections — so extraction was tuned to reality. Consent, encryption and access control were designed in from the start, because the source material is sensitive customer audio.
A managed pod built the device firmware, the secure sync, the speech-and-language pipeline and the management dashboard together, delivering in phases and validating extraction and sentiment against human-reviewed transcripts before trusting the automated reports.
How it works
Record
The rep captures the customer conversation on the device, with consent.
Sync
Audio uploads securely to the cloud through the AWS IoT layer.
Transcribe
Speech-to-text turns each recording into a searchable transcript.
Analyse
NLP extracts products and requirements, scores sentiment, and predicts the outcome.
Report
A consolidated report with recommended actions is submitted to management.
The system pairs automatic speech recognition with a layered language stack: named-entity extraction maps mentions to the client's real product catalogue, requirement mining structures what the customer asked for, and a sentiment model reads tone across the whole exchange rather than a single line. A predictive layer then scores conversion likelihood and proposes the next best action, learning from the per-customer history so it reasons across visits, not just one call.
Because the source material is sensitive customer audio, consent, encryption and role-based access were designed in from the start — the platform turns raw recordings into safe, structured insight, and only the derived intelligence travels up to management.
The impact
Leadership gained ground-truth visibility into what actually happens in every customer meeting.
Reporting shifted from subjective, delayed notes to automatic, consolidated intelligence within minutes.
Sentiment and requirement signals let managers coach reps and follow up on real interest, fast.
Predictive scoring helped prioritise high-intent customers and standardise the next-best-action.
Technology stack
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