AI Inside Your CRM: Lead Scoring and Follow Up

Where leads and deals slip through the CRM

Most CRMs hold more leads than a sales team can work properly. Web form leads sit until someone checks the queue, trade show lists get imported and forgotten, and an opportunity that looked strong after a March demo has had no logged activity since. The CRM records all of this faithfully and does nothing about it.

Tepia sees the same three gaps in most sales teams. First, speed: inbound leads wait hours for a reply, and every hour of delay gives a competitor time to answer first. Second, focus: reps work leads in the order they arrived rather than by likelihood to close. Third, follow through: after a good call, the recap email and next task get written late or not at all.

AI helps with each gap when it works from your own data. A lead score trained on your closed deals knows that a forty person manufacturer in Ohio who downloaded a spec sheet converts better than a large enterprise visitor reading the blog. A follow up drafted from the call transcript includes the details the prospect actually mentioned.

AI actions across the sales cycle

Tepia places each AI action at a specific CRM moment and pairs it with a human checkpoint.

Tepia sets the checkpoints with your sales leaders rather than by default. Some teams auto send first replies to inbound demo requests during business hours; others want every message reviewed. Call summaries rely on recordings, so Tepia connects to your calling tool and its transcripts, building on our AI call notes and summaries work.

Lead scores that sales actually trusts

Sales teams ignore scores they cannot explain. Tepia builds lead scoring models on your won and lost history, then shows the top reasons next to each score in the CRM: industry match, company size in your strongest range, two visits to the integrations page, a reply from a director level contact. Reps see why a lead ranks high and can disagree with a click, and that feedback is stored for the next retraining.

• Training data: one to three years of closed opportunities with their source, company details and activity history, cleaned of duplicates first.
• Fit and intent: fit scores use company attributes; intent scores use behavior such as email replies, site visits and meeting requests.
• Bias check: Tepia reviews whether a score simply mirrors where reps happened to focus in the past before it goes live.
• Refresh: scores update as new activity arrives, and the model is retrained quarterly or when win patterns shift.

Where your CRM’s built in scoring already works, Tepia keeps it and focuses on the follow up gaps instead.

Integrations and sending rules

Tepia connects AI features to the CRM through its APIs and to the tools around it:

• Salesforce through REST APIs and platform events, with scores and drafts stored on standard or custom objects; see Salesforce integration services.
• HubSpot through its CRM APIs and custom objects, with drafts placed in the rep’s task queue.
• Microsoft Dynamics 365 through Dataverse APIs for teams on the Microsoft stack.
• Calling and meeting tools for transcripts, and calendars for booking links.
• Twilio and SendGrid for SMS and email, with sending throttled per rep to protect domain reputation.

Outbound rules are part of the build, not an afterthought. Tepia checks unsubscribe status, follows the CAN SPAM Act on commercial email, requires recorded consent before any text message under the TCPA, and keeps every AI drafted message attached to the contact record so the history is complete.

How Tepia approaches AI CRM automation

1. Discovery: a review of your CRM objects, pipeline stages and data quality, plus interviews with reps, managers and sales operations. Deliverables are an Investigation Summary with a data quality report, an Interview Summary of how reps prioritize today, and User Stories such as a rep sending a recap within ten minutes of a call.
2. Design: score and reason displays in the CRM layout, draft review panels, alert rules and manager briefs, delivered as wireframes that follow your CRM’s own interface patterns.
3. Development and Testing: data cleanup scripts, the scoring model, drafting prompts and integrations. The Alpha scores historical leads so sales leaders can compare it with what actually closed; the Beta runs with one team. Test plans cover score accuracy on held out deals, draft quality reviews and sending rule enforcement.
4. Training: short sessions for reps on reading scores and editing drafts, and admin training for sales operations on rules and thresholds.
5. Launch: team by team rollout, duplicate merges applied in reviewed batches, and support reps on hand during the first pipeline reviews.
6. Support: quarterly retraining, prompt updates as messaging changes, and new automations requested by sales leaders.

Typically Discovery runs 1 to 3 months, development and QA 2 to 6 months, and launch about a month; well kept CRM data shortens the early phases. See our process.

Built in CRM AI or a custom layer

Salesforce, HubSpot and Microsoft now ship AI features inside their CRMs, and for teams whose process matches the defaults, turning those on is a reasonable first step. Tepia will tell you if that is where you should start.

A custom layer makes sense when scoring needs data the CRM does not hold, such as product usage, ERP order history or service tickets; when you run more than one CRM after an acquisition; when you want models and prompts you own and can move; or when your sales motion, such as dealer networks or field reps, does not fit the vendor’s assumptions. Tepia also builds custom CRMs when the CRM itself is the problem. Browse our work or contact Tepia.

Frequently asked questions