What lead qualification means in AgentFlow
Lead qualification is the step between raw inbound interest and a decision. Instead of relying on operators to manually read every submission and decide its priority, AgentFlow delegates the initial evaluation to an AI model. The result is a consistent, auditable signal that operators can accept, adjust, or override — depending on what your business rules require. In practical terms, qualification turns an unstructured form response into a scored record that travels through the rest of the pipeline.How AI scoring works
AgentFlow sends each incoming lead to OpenAI for evaluation. The model receives the lead’s submitted data alongside your configured qualification criteria and returns a structured response. That response contains three components:- Score — a numeric value representing overall fit
- Confidence — a rating reflecting how certain the model is in its assessment
- Summary — a short, readable explanation of why the lead received that score
What data feeds into scoring
AgentFlow draws on two categories of input when evaluating a lead: Form fields — the data a lead explicitly submits through your intake form. This typically includes information such as company name, use case description, team size, and any qualifying questions you have included. Intake metadata — contextual signals captured at the time of submission, such as submission timestamp, entry channel, and referral source where available. The richer your intake form, the more signal the model has to work with. Thin submissions receive lower confidence scores, which surfaces them for closer operator attention.Qualification criteria you can configure
You define what a qualified lead looks like for your business. AgentFlow evaluates each submission against your configured criteria, which can include:- Fit signals — indicators that a lead matches your target customer profile, such as industry, company size, or stated use case
- Business rules — explicit thresholds or conditions, such as minimum team size or required budget range
- Disqualifying factors — signals that immediately reduce a lead’s score, such as stated incompatibility with your product
What the score output looks like
Every evaluated lead receives a structured qualification record. The table below shows the fields included in that output:| Field | Type | Description |
|---|---|---|
score | Integer (0–100) | Overall qualification score; higher values indicate stronger fit |
confidence | String (low / medium / high) | Model certainty in the assigned score |
fit_signals | Array of strings | Positive indicators identified in the submission |
risk_signals | Array of strings | Flags or concerns identified in the submission |
summary | String | Plain-language explanation of the score reasoning |
evaluated_at | ISO 8601 timestamp | When the AI evaluation was completed |
criteria_version | String | Version identifier of the qualification criteria used |
Why server-side processing matters
AgentFlow performs all AI qualification calls on the server. Your OpenAI API key and qualification prompts are never sent to the client or exposed in the browser. This keeps your credentials protected and prevents your qualification logic from being inspected or replicated by third parties.