Operations

Route Sales Inquiries with AI Classification

Enterprise, SMB, Self-Serve, Partner — route inbound interest to the right sales motion, with Best Match because every inquiry needs a lane.

Overview

Sales routing has a property moderation doesn't: every inquiry must go somewhere, now. This setup classifies inbound sales interest into four motions under Best Match ambiguity — the closest lane wins even on imperfect fit, because a slightly-wrong lane that responds beats a perfect lane that never hears about it. Definitions encode the routing signals (seats, security and procurement requirements → Enterprise; reseller or integration framing → Partner), and high/medium/low confidence tells the SDR team which auto-routes to double-check.

Workflow

Route on first touch
Best Match never refuses, so every inquiry gets a lane immediately — the speed your funnel actually needs.
Encode segment signals in definitions
Seat counts, procurement language, reseller framing — the definitions carry your segmentation playbook.
Spot-check medium and low
Confidence tells the SDR team which auto-routes deserve a second look before the rep invests time.

Why This Works

Best Match fits routing economics: a fast near-miss beats a slow perfect
Signal-bearing definitions make the routing auditable and tunable
Confidence creates a lightweight QA loop without slowing the funnel

Best for

Sales teams with distinct motions per segment
Inbound volume that outpaces manual routing
Funnels where response speed beats routing perfection

Not for

Qualification (Qualified/Nurture/Unqualified) — that's a different label set with Strict rules
Extracting the inquiry's details — extraction is the other tool

Use cases

Sending enterprise signals to enterprise reps on first touch
Keeping partner inquiries out of the customer pipeline
Routing every inquiry somewhere — fast — with a confidence trail

Customize This Resource

Opens this setup in Data Classification Prompt. Generate to get the full classification prompt — then adjust the labels, ambiguity mode, and confidence output.

Open in Data Classification Prompt

Prompt Template

TASK
Route inbound sales inquiries to the right sales motion.
Assign exactly one of the labels defined below to the text in the input.

LABELS
- Enterprise: A large-organization inquiry — many seats, security or procurement requirements.
- SMB: A small-or-medium business inquiry with standard requirements.
- Self-Serve: An individual or small-team inquiry served by the standard signup flow.
- Partner: A reseller, agency, or integration partner inquiry — not a direct customer.

CLASSIFICATION RULES
- Read the entire text before deciding — the deciding signal may come late.
- Judge by content and intent, not by tone, length, or formatting.
- Match the text against the definitions, not just the label names.
- Use only the labels defined above — never invent a new label.

EDGE CASE RULES
- If the text fits more than one label, choose the one matching its primary purpose — what the writer mainly wants.
- If two labels fit equally, prefer the more specific definition over the more general one.

AMBIGUITY POLICY
- Always return the closest label, even when the fit is imperfect.
- Never refuse to classify.

CONFIDENCE
- With each label, report a confidence level: high, medium, or low.
- high = the text unambiguously fits the definition; medium = a good fit with minor uncertainty; low = the best available choice among the labels.
- Confidence reflects your certainty about the fit — it is self-reported, not computed.

OUTPUT FORMAT
Return your classification as a single valid JSON object with a "label" key and a "confidence" key.

EXAMPLE OF A VALID RESPONSE
{
  "label": "Enterprise",
  "confidence": "high"
}

OUTPUT RULES
- Return only the JSON object — no text before or after it.
- Do not wrap the output in markdown code fences.
- Return the label text exactly as defined above — no paraphrasing, no abbreviations, no new labels.

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