Outreach and AI

Learn Reviewed · not yet fact-checked

What it is

An AI SDR is software that runs the outbound loop a human SDR runs: build a prospect list, research each person, draft a personalized message, send it on a schedule, triage what comes back, and book the meeting. What it does not supply is the strategy underneath — who to target, what to offer, whether the product fits the market, and what to do when a reply falls outside every pattern it knows.

Why it works this way

Most of an SDR's day is pattern work: find people who match a profile, gather the same few facts, adapt a template, log what happened. Language models make each of those steps automatable — but they are probabilistic, and occasionally confident and wrong. So autonomy is granted in levels, each one widening what the system may do alone:

• L1 — drafts for approval. The system prepares everything; a human approves each message before it sends.
• L2 — sends with a review queue. Routine messages go out automatically; flagged ones wait for review.
• L3 — autonomous with escalation. The system runs end to end and escalates only edge cases — unusual replies, sensitive accounts, low-confidence drafts.

How it behaves in practice

• Approval gates exist because of hallucination risk. A fabricated detail in a "personalized" opener is worse than no personalization — review catches what confidence scores miss.
• The quality floor is the data. Research and drafting inherit the accuracy of the underlying data; enrichment errors become personalization errors, at volume.
• Autonomy is earned per playbook. Teams typically start at L1, watch precision for a few weeks, and widen autonomy where the system proves reliable.
• Escalation keeps humans on exceptions. The human role shifts from writing every message to judging the cases the system cannot.

Common misconceptions

How Reply implements this

Jason AI is Reply's implementation of the model (AI SDR capability): it works from an offer you define, uses playbooks, and grounds drafts in a knowledge base. Autonomy maps onto two separate controls:

• Sending autonomy. In Approval mode, every generated message lands in a Pending approvals queue and nothing sends until you approve it — you can edit it, regenerate it with a short prompt, or delete it, which removes the prospect from the sequence. In Automatic mode, Jason generates and sends without waiting for approval.
• Sourcing autonomy. Autopilot mode keeps the sequence fed by adding new contacts every day based on the filters you set. Source limits bound it: the Sales Navigator source caps at 500 contacts per day, with defaults of 10 per day and 3 contacts per company, and signal-based sources filter candidates by ICP fit — 60% by default.

Scoring is relative to the sequence, not absolute per contact: the ICP score is computed against that sequence's offer, so the same contact can score differently in two sequences promoting different products. A LinkedIn Activity score (Frequent, Consistent, or Occasional) guides which channel to lead with. The full setup path is build an AI SDR.

Where this breaks down

An AI SDR amplifies the strategy it is given — a weak offer sent at volume fails at volume. Edge cases never disappear entirely, so an escalation path is permanent, not a transition phase. And trust is asymmetric: one hallucinated claim to a key account can cost more than the automation saved all quarter.

Related

• Reply classification
• Sequence design
• AI SDR in Reply
• Build an AI SDR

Build with Reply

• REST API: docs.reply.io — offers, playbooks, and knowledge bases
• MCP: agents.reply.io/mcp — Jason AI setup and approval operations