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Generative AI

Voice AI Sales Assistant

A voice-activated GenAI assistant integrated with CRM, giving field sales reps hands-free access to information and logging on the move.

Client
Global life-sciences company
Discipline
Generative AI
Engagement
Proof of concept
Hands-free
voice interaction designed for the field
CRM-integrated
logging and lookups without a screen
Proof of concept
validated the approach before broader rollout

Context

Field sales reps at a global life-sciences company needed fast, hands-free access to CRM information and a quicker way to log activity between and during visits, where typing on a phone isn't practical.

The challenge

A voice assistant that's genuinely useful in the field has to understand natural speech, connect reliably to CRM data, and respond fast enough to be worth using instead of a screen.

Our approach

Natural voice interaction

We built a voice interface with natural-language understanding so reps could ask and instruct in plain speech.

Wired into CRM

The assistant integrates with the CRM to both retrieve information and log activity, keeping records current without manual entry.

Proven as a POC

We delivered it as a proof of concept to validate the experience and the integration before scaling.

Field RepVoice inputSpeech-to-TextTranscriptionGenAI Intent LayerQuery vs. logging actionCRM IntegrationAuthenticated read/writeVoice ResponseHands-free output
Spoken requests are transcribed, interpreted as CRM queries or logging actions, executed against the CRM, and answered by voice

Architecture

Voice as the interface, designed for actual field conditions

Field sales reps for a life-sciences company are often in situations where pulling out a phone and typing isn't practical — between meetings, driving, or in a clinical setting where screen time is inappropriate. The assistant is voice-first: reps speak naturally to ask questions or log information, and the system responds via voice, designed around the constraint that the rep's hands and attention are often unavailable for a traditional app interaction.

A GenAI layer that turns spoken requests into CRM actions

Speech is transcribed and passed to a GenAI component that interprets intent — is this a query (what's the latest interaction history with this account) or a logging action (record that this meeting covered X) — and translates that intent into the appropriate CRM operation. This is where the 'assistant' framing matters: the rep isn't navigating a CRM's data model via voice commands, they're talking naturally and the assistant handles the translation to structured CRM queries and updates.

CRM integration as the foundation, voice as the access layer

The CRM integration layer is what makes the voice interface useful rather than a novelty — without real read/write access to the CRM, a voice assistant can only have generic conversations. The integration handles authenticated, permissioned access to the rep's CRM data, so queries return real account information and logged notes actually appear in the CRM record, making the voice interaction a genuine alternative to opening the CRM app, not a separate disconnected tool.

What we built

  • A voice interface for field sales interactions
  • Speech-to-text transcription and intent classification
  • A GenAI layer translating spoken intent into CRM queries and updates
  • Authenticated CRM integration for real-time lookups and logging
  • Voice-based response generation for hands-free operation

Technology stack

Generative AI
Intent classificationLLM-based query/action translationVoice response generation
Voice / Speech
Speech-to-textText-to-speech
Integration
CRM integration (authenticated read/write)MCP-based tool access
Engineering
PythonMobile/voice client

Results & impact

The proof of concept showed field reps could access and update CRM data hands-free by voice — pointing to less admin time and better-kept records across the sales team.

  • Field sales reps could query account information and log meeting notes hands-free, fitting the assistant into how their day actually works rather than requiring screen-based interaction.
  • CRM data stayed current because logging happened in the moment via voice, rather than being deferred until the rep had time to sit down with a screen — reducing the common problem of CRM data going stale because updates get postponed.
  • As a proof of concept, the project validated the core voice-to-CRM interaction pattern with real field reps before any decision on broader rollout — derisking the larger investment by testing the riskiest assumptions first.
  • The architecture separating intent translation (GenAI) from CRM integration (authenticated access layer) meant the same pattern could extend to other CRM actions beyond the initial proof-of-concept scope without re-architecting the core integration.

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