Teenva AI & Digital Ventures builds AI lead scoring for B2B SaaS, inside sales teams, enterprise SDR orgs, and CRM product vendors from Bangalore, India. Every inbound lead gets a predictive score—probability to convert to opportunity, ICP fit, and priority rank—with explainable drivers reps actually trust, synced back to Salesforce, HubSpot, or Zoho CRM for routing, SLA alerts, and daily call lists.
Manual point rules decay when your ICP shifts. Teenva trains ML models on your won/lost history, behavioral signals, and optional enriched firmographics—not generic LLM guesses.
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AI lead scoring—predictive models, explainability, and CRM sync for faster qualified pipeline.
Hub: AI solutions · Domain: CRM & Sales
What the model scores
| Score type | Question answered | Typical output |
|---|---|---|
| Conversion probability | Will this lead become SQL/opportunity? | 0–100 or 0–1000 rank |
| ICP fit | Does firm/profile match your ideal customer? | Fit tier: A / B / C |
| Velocity (scoped) | How fast will they move if engaged? | Days-to-SQL estimate band |
| Deal size proxy (scoped) | Expected ACV bucket | SMB / mid / enterprise |
| Churn-risk overlap (scoped) | Existing account expansion vs new logo | Route to AE vs SDR |
Scores update on new activity (form fill, demo request, product trial event)—not once-a-quarter spreadsheet rules.
Signals we use in models
| Signal category | Examples |
|---|---|
| Firmographics (B2B) | Industry, employee band, revenue band, geography |
| Contact profile | Title, department, seniority, email domain type |
| Marketing engagement | Email opens/clicks, webinar attendance, content downloads |
| Product / trial (scoped) | Feature usage, activation milestones, PQL signals |
| Website intent (scoped) | Pricing page visits, return frequency via your analytics |
| Form & source | Campaign, channel, partner referral vs cold inbound |
| Enriched data (scoped) | From CRM data enrichment |
| Historical outcomes | Won/lost labels from your CRM—ground truth |
Lead scoring uses structured ML—gradient boosting, logistic regression, calibrated probability—not chat LLMs for rank order.
Features we implement
- Scoring API — Real-time score on form submit or CRM webhook
- Batch refresh — Nightly rescore of open leads as activity accumulates
- Explainability — Top 3–5 drivers per lead (VP title + pricing page + SaaS industry)
- Tier rules — Hot / warm / nurture thresholds you control
- Routing hooks — Assign to SDR pod, territory, or round-robin by score + geo
- Rep UI embed — Score badge + drivers inside CRM sidebar or custom SaaS
- SDR worklist — Sorted call queue exported to CRM view
- Model registry — Versioned models, training snapshot, approval workflow
- Champion/challenger — Shadow-test new model before replacing production
- Monitoring — Conversion rate by score decile, calibration drift, segment bias checks
- Backtesting — Historical leads scored as-if before go-live
Architecture
Lead created / updated (form, CRM, marketing automation)
Feature assembly (firmographics, engagement, product events)
Optional enrichment API → CRM data enrichment
Lead scoring model → probability + ICP tier + drivers
Policy rules (geo, product line, partner exclusivity)
CRM write-back (score field, tier, routing owner)
Closed-won / lost feedback → retrain labels
- Deployment — Your cloud/VPC; CRM tokens scoped least-privilege via API integration
- Labels — SQL created, opportunity won, or your agreed conversion definition
Use cases by team type
| Team | How scoring helps |
|---|---|
| B2B SaaS SDR | Focus on PQLs and high-intent inbound before outbound spray |
| Enterprise sales | Route enterprise-fit accounts to senior AEs fast |
| Inside sales (India/global) | Time-zone aware priority lists each morning |
| Partner / channel (scoped) | Score partner-sourced leads separately |
| CRM / RevOps platform | Embed scoring as feature in your product |
| Marketing ops | Feed campaign manager with scored segments |
Integrations
CRM & marketing
Reps ignore black-box scores. We ship driver explanations and decile conversion reports so RevOps can tune thresholds with evidence—not gut feel.
AI lead scoring vs sibling CRM AI
| Product | Focus |
|---|---|
| AI lead scoring | Predict who to call first |
| Sales copilot AI | Assist rep on email, call prep, CRM updates |
| CRM data enrichment | Fill missing firmographic/contact fields |
| Customer churn prediction | Retain existing accounts |
| AI campaign manager | Plan and optimize outbound campaigns |
Different from credit risk analysis—sales pipeline conversion, not loan default.
Why Teenva AI
RevOps-aware delivery
We align on label definitions with your CRM admin, not only data scienceExplainability default
Reps adopt scores when they see whyIndia + global
Teams selling into India, US, EU from Bangalore HQProduct + services
Embed in your SaaS CRM or score inside existing stackSibling synergy
Pair with enrichment and sales copilot without duplicate vendors
Delivery process
Discovery
CRM objects, conversion definition, rep workflow
Data audit
Volume of labeled wins/losses, field completeness
Label design
SQL vs opportunity vs won; window (30/60/90 days)
Feature pipeline
CRM + marketing + product event joins
Baseline model
Beat current rules-based score on holdout set
Explainability review
RevOps validates driver narratives
CRM integration
Fields, flows, SDR list views
Pilot pod
One SDR team; measure connect-to-SQL lift
Production + monitor
Decile reports, quarterly retrain cadence
Explore
Frequently asked questions
Often yes on accuracy—we train on your outcomes; native scores are generic. Coexist during pilot (scoped).
Rough guide: 500+ converted leads for stable B2B models; smaller volumes need simpler models or enrichment-heavy features.
Both—instant score on create; nightly refresh as engagement accumulates.
Top drivers per lead in CRM sidebar—e.g. industry match, recent pricing visit, title band.
Yes via API integration—custom fields and automation flows (scoped).
First-class via Zoho integration—score fields and assignment rules.
No for rank order—LLMs are unreliable for calibrated probability; we use ML with audit-friendly metrics.
We report conversion by segment in monitoring; thresholds adjustable per geo (scoped).
Yes—CRM data enrichment feeds firmographics into the model.
8–12 weeks to production MVP with CRM sync—depends on data cleanliness and label clarity.
Related CRM & Sales AI solutions
Build your AI lead scoring model
Predictive MQL/SQL scores, ICP fit, explainable drivers, and CRM-native routing for your sales team.
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