Know who to call, before they pick up.
Score every lead by real buying intent — not self-reported form data, not 10-year-old BANT checklists. Swiftex blends transformer-based conversation analysis with 40+ enrichment signals, so your reps spend their day on deals that will actually close.
Rules miss signal. LLMs read the room.
AI lead qualification is the use of machine learning — typically a transformer model — to infer a prospect’s buying intent, budget, timeline and fit from conversation data and enrichment signals, rather than relying on self-reported form fields. Unlike rule-based lead scoring, AI qualification updates in real time as the conversation evolves.
Swiftex runs a 7B-parameter dialogue model fine-tuned on 40M+ Indian sales conversations. Scores come with reason codes — not just a number. Your reps see “budget confirmed, urgency high, objection = delivery timeline” instead of a black-box 78/100.
40+ signals. Not 5 form fields.
Every inference combines what the buyer says, what they do, who they are, and what they did last time. The four pillars:
How a real WhatsApp conversation escalates from cold to hot — in 4 turns.
Why form-field scoring is obsolete.
| Dimension | Swiftex AI qualification | Rule-based lead scoring | BANT checklist |
|---|---|---|---|
| Inputs | Conversation + enrichment + behaviour | Form fields + simple events | Manual notes |
| Refresh rate | Every turn | On form submit / event | Manual, rarely updated |
| Explainability | Reason codes per signal | Opaque point totals | Qualitative |
| Handles code-switching | Yes, natively | No | Agent-dependent |
| Detects objections | 40+ categories | No | Manual log |
| Next-best-action | Automated | No | No |
| Effort to maintain | Self-learning | Ops team tunes rules | Rep discipline |
What changes when qualification gets smarter.
Lead qualification, answered.
What is AI lead qualification? +
AI lead qualification uses machine learning — typically a transformer model — to infer a prospect’s buying intent, budget, timeline and fit from conversation data and enrichment signals, rather than self-reported form fields. Unlike rule-based scoring, AI qualification updates in real time as the conversation evolves.
How does Swiftex score lead intent? +
Swiftex runs a transformer fine-tuned on 40M+ Indian sales dialogues. Inputs include source, enrichment data, conversation sentiment, objection type, stated timeline, budget signals and past behaviour. Scores refresh every turn with explainable reason codes.
What signals does Swiftex use? +
40+ signals: phone/email validation, location, vehicle or property history, CIBIL/KYC hooks (BFSI), intent phrases, urgency markers, sentiment, objection type, competitor mentions, demographic proxies, device & channel, time-of-day behaviour, past interaction history and more.
How is this different from BANT or MQL scoring? +
BANT is a static framework filled in manually. MQL scoring is typically rule-based points on form-field matches. Swiftex uses both conversational and enrichment signals, updated live, with per-turn intent scores and explainable output.
Can Swiftex integrate qualification with my CRM? +
Yes. Score, stage, reason codes and next-best-action are written back to Salesforce, HubSpot, Zoho, LeadSquared, Freshsales and Kylas in real time. Custom CRMs via webhook/API.
Stop calling dead leads. Start closing live ones.
30-minute walkthrough of Swiftex qualification on your actual lead data.