AI SDR
Use Frontline to qualify inbound leads, capture intent, enrich CRM context, and route high-fit prospects to sales.
Use this product state to connect the visible UI to the operational decision the lesson is teaching.
Learn the system by following the product states.
Use the screenshots as the primary map: start with the full context, trace the connected workflow, inspect the focused UI, then compare against the completed operating state.
Customer signalWorkflow pathVisible outcomeUse this product state to connect the visible UI to the operational decision the lesson is teaching.
Summary
Use Frontline to qualify inbound leads, capture intent, enrich CRM context, and route high-fit prospects to sales.
Concepts covered
Step breakdown
- Define the system to deployStart from this Sales operating system and confirm the business outcome, source signals, owners, and review gates.
- Create the Frontline assetsBuild the workflow canvas, agent prompt, channels, CRM records or tables, routing logic, and Max Activity evidence described in the blueprint.
- Launch the narrow versionDeploy the smallest reliable version first, keep human review visible, then use activity and analytics to expand the system.
What you will build
An inbound AI SDR that qualifies leads the moment they arrive on WhatsApp, captures structured intent data into CRM, and routes high-fit prospects directly to a sales owner — without manual triage.
The system covers: lead identification, CRM record lookup or creation, AI qualification conversation, structured output capture, and owner handoff or nurture routing.
When to use this
You have a single primary lead channel (WhatsApp) and want to add qualification before routing.
Your sales reps spend too much time on leads that do not fit your ideal customer profile.
You need a repeatable qualification framework that produces the same output regardless of who receives the lead.
You want to start smaller than a full AI SDR system before expanding to multi-channel intake.
System components
Studio workflow triggered on new inbound WhatsApp message.
CRM Person lookup: find existing contact by phone or create new record.
CRM Company lookup: enrich with domain, segment, size if available.
CRM Deal record: created at stage 'New Lead' with source, owner, and qualification output.
AI SDR agent: asks qualification questions conversationally and returns structured output.
Conditional Routing: branches on qualification outcome (high-fit, low-fit, missing-info).
Max Activity: records qualification summary, intent signals, and next action on the Deal.
Step-by-step implementation
Agent prompt
You are an AI SDR. Your job is to qualify inbound leads in a friendly, professional way.
Start by greeting the lead by name if available. Ask about their main goal or challenge in one sentence.
After one exchange, assess: company size, budget signal, decision-making role, and timeline.
Output a JSON: { qualification: 'high_fit' | 'low_fit' | 'missing_info', confidence: 0-100, intent: string, next_question: string | null, summary: string }.
Never mention competitors. Never promise pricing. If the request is outside your scope, say a specialist will follow up.
Workflow logic
High-fit (confidence ≥ 65): create Deal, assign to owner, send intro message, write Max Activity with handoff note.
Low-fit: log reason on Deal, write Max Activity, do not send follow-up automatically.
Missing info: send the next_question from the agent output as a WhatsApp reply. Re-enter qualification after the response.
After 3 unanswered follow-ups: write Max Activity as 'unresponsive', move Deal to nurture stage.
Final operating state
Every inbound WhatsApp lead results in a Person record and a Deal record with source and qualification fields populated.
High-fit leads have an owner assigned and a Max Activity entry with the full qualification summary.
Low-fit and missing-info leads are logged in CRM so they can be recovered from nurture or re-engagement workflows.
Workflow logs show each branch taken for every lead so you can audit qualification accuracy.
Troubleshooting
Lead creates duplicate Person records: ensure the phone number format is normalized before the lookup (strip spaces, country prefix).
Qualification conversation feels robotic: reduce the number of questions in the first turn. Ask one thing at a time.
High-fit leads not getting owner tasks: verify the Deal owner field is set before the notification node fires.
Agent output not routing correctly: log the raw agent output in a Max Activity note for debugging. Check JSON format matches branch conditions.
Operational playbook
Use AI SDR as part of the Frontline Solutions Sales operating loop: inspect the current product state, confirm the source context, and decide what should happen next.
The goal is not to memorize screens. The goal is to understand how the product surface supports repeatable work, AI assistance, and accountable handoff.
Best practices
Start with the operational job before changing configuration. Name the owner, define the trigger or source context, and decide how the result should be reviewed.
Prefer narrow, inspectable setups over broad automation. Teammates should be able to explain why the system took an action from the visible product state.
Platform layers involved
Studio defines the workflow and AI agent behavior. Channels capture the customer interaction. CRM provides customer memory. Max Activity shows what the system did and what needs follow-up.
Use the solution page as the business-facing map, then open the related product tutorials when you need configuration detail.
Outcome metrics
Track a small set of operational signals: response time, handoff rate, completion rate, escalation quality, CRM field completeness, reply rate, and repeated failure patterns.
The metric should reflect the business outcome, not only whether the automation ran.
Agent Builder visual map
FAQs
What does AI SDR teach?
Use Frontline to qualify inbound leads, capture intent, enrich CRM context, and route high-fit prospects to sales.
How should teams use this lesson?
Use it as an implementation guide: create the assets, connect the systems, verify the completed state, and operate the blueprint with review gates.
Which Frontline products are involved in this solution?
Most solution playbooks connect Studio workflows, Channels, CRM records, AI agents, and Max Activity. The business outcome is the entry point; the platform layers make it operational.
How should we decide whether to automate this use case?
Automate when the path is repeated, has clear source context, needs consistent follow-up, or benefits from AI classification, routing, summaries, or structured capture. Keep human review where judgment or risk is high.
What should be visible before this goes live?
Verify the workflow trigger, CRM context, channel permissions, AI agent instructions, handoff owner, logs, and Max Activity output so the team can trace what happened.
How do we keep the customer experience personal?
Use CRM context, conversation history, and approved message patterns. AI should use relevant customer memory, not generic copy, and workflows should escalate when context is missing.
What is the best first version of this playbook?
Start with one channel, one workflow, one owner group, and a narrow success metric. Expand only after logs, activity, and customer-facing outputs are trustworthy.