AI SDR system
Build the full AI SDR operating system: WhatsApp lead intake, CRM person and deal creation, AI qualification, follow-up routing, Max activity, and sales analytics.
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
Build the full AI SDR operating system: WhatsApp lead intake, CRM person and deal creation, AI qualification, follow-up routing, Max activity, and sales analytics.
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
A complete AI SDR operating system that handles inbound leads from WhatsApp, creates CRM Person and Deal records, qualifies each opportunity through an AI agent, routes follow-up by outcome, and logs every interaction in Max Activity.
When fully deployed: a lead arrives, Frontline identifies or creates the Person, enriches Company context, creates a Deal at the correct stage, runs AI qualification, and routes to booked meeting, nurture, or disqualification — with no manual triage required.
When to use this
Your team receives inbound leads faster than sales reps can manually triage them.
You need qualification consistency across different reps, shifts, or markets.
Leads arrive through WhatsApp or Instagram and the first reply speed determines conversion.
Sales managers need pipeline visibility without relying on reps to manually log context.
System components
Studio workflow: trigger on new WhatsApp message or form signal, orchestrates the full lead-to-Deal path.
Channels: WhatsApp connected to the workflow as the intake source.
CRM Person record: name, phone, source, language, intent, consent, owner.
CRM Company record: domain, segment, region, employee range, enrichment status.
CRM Deal record: stage = New Lead, qualification score, next step, owner, source.
AI SDR agent: configured with qualification rubric, persona, and structured output format.
Conditional Routing node: branches on qualification score and intent signals.
Max Activity: qualification summary, missing context, recommended next action, owner task.
Step-by-step implementation
Agent prompt
You are the AI SDR for Frontline. Your job is to qualify inbound leads using the information available.
When a new lead contacts us, extract: name, company, role, budget signal, timeline, and primary pain point.
Qualification rubric — High fit: clear pain + budget signal + decision role + timeline under 90 days. Low fit: no budget signal or wrong segment. Missing info: required field absent.
Return a JSON object with: { qualification: 'high' | 'low' | 'missing_info' | 'disqualified', confidence: 0-100, summary: string, missing_fields: string[], next_action: string, owner_note: string }.
Do not send prices, make commitments, or answer product questions you are not certain about. Route those to a human.
Workflow logic
High fit + confidence ≥ 70: create Deal owner task, send meeting link or intro message, write Max Activity with handoff summary.
Low fit: move Deal to Nurture stage, add to re-engagement workflow, write Max Activity with reason.
Missing info: send a follow-up WhatsApp question for the single most important missing field. Re-run qualification after reply.
Disqualified: close Deal with reason, write Max Activity, stop follow-up sequence.
Error or agent timeout: route to human review queue, write Max Activity flagging the failure.
Final operating state
Every inbound WhatsApp lead has a Person, Company, and Deal record in CRM with source and qualification data.
The workflow canvas shows every branch path and each path has a Max Activity write as the final node.
Max Activity on each lead contains: qualification outcome, confidence score, missing fields, and the next action taken.
Sales owners receive handoff tasks only for high-fit leads. Nurture and disqualification paths run without owner interruption.
Analytics show: leads received, qualification rate by outcome, time-to-first-reply, deals created, and owner handoff rate.
Troubleshooting
Person not created: check the WhatsApp trigger field mapping — phone must be the Person identifier.
Wrong qualification branch: inspect the agent output in workflow logs. Verify the prompt returns valid JSON with the expected fields.
Deal owner not notified: confirm the Send Message node is connected to the high-fit branch and the owner field is populated on the Deal.
Max Activity missing: verify the Write Activity node is present on every branch including the error path.
Agent returns generic output: add more context to the agent node — pass conversation history, Person fields, and Company fields as variables.
Operational playbook
Use AI SDR system 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 system teach?
Build the full AI SDR operating system: WhatsApp lead intake, CRM person and deal creation, AI qualification, follow-up routing, Max activity, and sales analytics.
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.