AI Sales Assistant Development

Where an AI sales assistant gives reps time back

Sales teams rarely lack data. They lack the time to read it before a call and to record what happened after one. A rep with forty accounts cannot reread every email thread, check open service tickets and scan six months of orders before each conversation, so they skip it. Afterward, notes are entered late, briefly or not at all, and the CRM slowly stops reflecting reality.

Tepia builds assistants that sit in that gap. An industrial distributor’s outside reps get a brief that shows an account’s order frequency dropping and two late deliveries last month. An insurance agency’s producers get renewal summaries that compare the current policy with what the client asked about last year. A B2B software company’s account executives get call notes, risks and next steps written to the opportunity within minutes of hanging up.

Each assistant is built into the systems your team already uses. Tepia adds a backend service that reads from your CRM, inbox and order data, and surfaces results in the CRM, in Outlook or Gmail, or in a mobile app for field reps.

What the assistant does at each point in the sales cycle

Tepia maps the assistant to specific moments, each with a clear output and a clear point where the rep stays in charge:

Teams usually start with the after call step because it saves the most typing and immediately improves CRM data quality.

Connecting the CRM, inbox and call data

The assistant’s usefulness depends on joining data that normally sits in separate tools. Tepia builds the connections and the field mapping so each source lands in the right place.

• CRM: Salesforce, HubSpot or Microsoft Dynamics 365, reading accounts, contacts and opportunities and writing notes, tasks and stage changes through their APIs.
• Email and calendar: Outlook through Microsoft Graph or Gmail through Google Workspace APIs, to read threads tied to an account and create drafts.
• Call data: transcripts from Zoom, Microsoft Teams or your phone system, matched to the right opportunity by attendee and time.
• Orders and service: NetSuite, Dynamics or your ERP for purchase history, plus your helpdesk for open issues.

Tepia writes AI generated values into pending fields or drafts first, never straight over data a rep entered. Our Salesforce integration and HubSpot integration pages cover CRM specifics.

Rules that keep the assistant trustworthy with buyers

A sales assistant speaks for your company, so Tepia sets firm rules before any draft reaches a customer.

• No unapproved sends: external emails and messages are drafts until a rep sends them. Some teams later allow automatic sends for low risk messages like meeting confirmations.
• No commitments: the assistant never promises discounts, delivery dates or contract terms; it flags those topics for the rep.
• Contact preferences: do not contact flags, unsubscribes and regional consent rules in your CRM are checked before any outreach is drafted.
• Territory access: a rep’s assistant only reads accounts that rep can see in the CRM.
• Traceability: every brief and draft lists the records it was built from.

Tepia also tunes drafts to your team’s voice using approved examples, so a recap from a field rep at a distributor does not read like a software startup’s marketing email.

How Tepia approaches AI sales assistant development

Tepia runs its six phase process with sales operations and a few working reps involved throughout, managed by a US based project manager.

1. Discovery: ride alongs or call shadowing, a CRM data quality review and an audit of available call and email data. Deliverables are an Investigation Summary, an Interview Summary and User Stories such as a rep filing notes from a site visit while driving to the next one.
2. Design: wireframes for the brief, the confirm notes screen, draft review and the mobile voice note flow, matched to your CRM layout or app style guide.
3. Development and Testing: integrations first, then each assistant step behind a flag. Alpha with sales ops, Beta with one team, under functional, user acceptance and non functional test plans covering field mapping accuracy and response time.
4. Training: short hands on sessions by role, built around real scenarios like preparing for a renewal meeting.
5. Launch: rollout by team or region, with support reps watching adoption and correction rates.
6. Support: new steps added, such as pipeline alerts, once the first ones are trusted.

Typically Discovery takes 1 to 3 months and development and QA 2 to 6 months. See our process.

Built in CRM AI or a custom sales assistant

Major CRMs now ship AI features for summaries, email drafts and forecasting. If your selling happens mostly inside one CRM and its data is complete, Tepia will suggest you start there.

A custom assistant is worth building when:

• The useful signal lives outside the CRM, in ERP orders, service tickets or call recordings.
• Your reps work in the field on phones and need voice first capture, not desktop screens.
• Your product catalog is technical, and answers must come from spec sheets and inventory you control.
• You run a custom CRM or a heavily customized one that packaged AI features do not understand.

Explore AI inside your CRM for lead scoring and follow up, and field sales rep mobile apps for outside teams. Then talk to Tepia about how your reps spend their week.

Frequently asked questions