100% Salesforce Native

Stop Wasting Time on Leads That Won't Convert.

Your reps chase dead-end leads. Your pipeline is bloated with junk. GPTfy Predict fixes both - combining ML-powered scoring with agentic AI to prioritize, route, and recommend actions automatically.

~73%
of leads are unqualified
or low-intent
Gartner, 2024
~47%
of MQLs never receive
sales follow-up
Demand Gen Report, 2023
~79%
of marketing leads
fail to convert
MarketingSherpa, 2024

You've Been Promised Quality Leads Before

The Challenge

Lead scoring tools promised to surface your best prospects. Instead, they delivered generic models that didn't understand your business. Your reps still cherry-pick the easy leads while hot prospects go cold waiting in the queue.

Lead Scoring Was
One-Size-Fits-None

Until now, lead scoring used generic models that couldn't account for your unique sales motion. Rule-based scoring couldn't adapt as your business evolved. Your reps learned to ignore the scores.

"What I spend the most time on is pipeline health and forecasting. Anything that helps improve data hygiene to improve pipeline accuracy - those are quite time-consuming." - Sales Ops Leader, Enterprise Retail

Routing Rules Couldn't
Keep Up

Static territory assignments didn't account for rep capacity, expertise, or historical performance. Hot leads sat in queues while reps wasted time on poor fits. No system connected lead quality to rep skill.

"Our reps looked at the lead queue like it was a chore list. They'd pick the top 3 and ignore the rest." - Sales Ops Manager, Manufacturing

Marketing Blamed Sales.
Sales Blamed Marketing.

Marketing spent budget generating leads that sales never touched. Sales complained about lead quality while ignoring the gems buried in the pile. Meanwhile, pipeline suffered and targets got missed.

"We were bleeding leads at every stage. Marketing qualified them, sales qualified them again, then they went into a black hole." - CMO, Professional Services

ML-Backed Scoring Meets Intelligent Routing

Before GPTfy Predict

Generic lead scores reps ignore
Static routing rules
Hot prospects go cold waiting

→
With GPTfy Predict

Custom ML scores every lead
Smart routing by fit & capacity
Reps get high-intent leads instantly

→
Your Future

Reps focus on winners
Response times drop
Pipeline quality improves

How It Works

1
Scan All Sources

Aggregate web forms, campaigns, emails, and enrichment data into unified lead profiles

2
Calculate Probability

ML models predict conversion using your historical win/loss patterns - not generic benchmarks

3
Optimize Routing

Smart assignment by territory, workload, expertise, and past performance - right lead, right rep

4
Recommend Actions

AI suggests next-best-action tailored to each lead's profile and engagement history

5
Engage with Insight

Reps enter every conversation armed with propensity scores and contextual talking points

Lead: Rachel Martinez, GreenTech Innovations

PRIORITY: HIGH
Score: 62/100 | Band: High | Conversion: 37% | Lift: +65%
Top Positive Factors
✓ recency: 32 days (+41%) - Recent activity indicates strong engagement
✓ source: Referral (+39%) - Referrals are a top-performing source
✓ budget_range: $50K-$100K (+28%) - High-converting budget range
Key Risk Factors
✗ demo_requested: FALSE (-25%) - Weaker buying signal
✗ company_size: 51-200 (-21%) - Smaller size underperforms
Hidden Pattern Discovered
→ company_size × budget_range: +82% signal alignment
While 51-200 typically underperforms, this budget range significantly
outperforms expectations - making this a "hidden gem" lead.
RECOMMENDED ACTION: Contact immediately. Focus on scheduling a demo to address the lack of request, and emphasize alignment with their ESG/Sustainability goals.

What Your Reps See for Every Lead

Score every prospect. Route with precision. Convert more deals.

Score Every Lead

ML-powered scoring with full transparency. Every factor explained with lift values.

  • ML-Powered Scoring (40%) - Trains on your historical win/loss data. Predicts conversion probability, not generic benchmarks
  • Behavioral Signals (25%) - Email engagement, web activity, content consumption, and recency of interactions
  • Firmographic Fit (20%) - Company size, industry, technographics, and budget range aligned to your ICP
  • Intent Signals (15%) - Demo requests, trial signups, webinar attendance, pricing page visits
  • Hidden Pattern Detection - Interaction effects reveal combinations that outperform expectations - your "hidden gem" leads

Route with Intelligence

Smart assignment that connects lead quality to rep expertise and capacity.

  • Territory + Rules - Geographic, industry, or account-based assignment logic with override controls
  • Load Balancing - Distribute leads based on current queue depth and rep capacity in real-time
  • Skill Matching - Route technical leads to technical reps, enterprise leads to senior AEs
  • Performance-Based - Historical close rates by lead type inform optimal rep-lead matching
  • SLA Enforcement - Auto-escalates leads when reps miss defined contact windows. No lead falls through cracks

"We take 10 to 15 minutes to do research on a prospect, and if you multiply that to 5 leads in a day, and then multiply that with the number of hunters - there is something here."

- Sales Leader, Global Financial Data Provider

How We Build Your Scoring Model

Two-week analysis with your business unit
1
Historical Analysis

Analyze converted vs non-converted leads by source, industry, size, and behavior patterns

2
Feature Engineering

Identify which fields predict outcomes: recency, source, budget, engagement metrics

3
Model Validation

Test across multiple ML algorithms. Validate against holdout data for accuracy

4
Interaction Discovery

Find hidden patterns where combinations outperform - your "hidden gem" segments

~500+ leads for initial training Monthly recalculation Self-improving accuracy over time BYOM - OpenAI, Claude, Gemini, or your preferred model
Questions From Sales Leaders and RevOps Teams
Data
How much historical data do we need?
Typically 500+ closed leads (converted and not converted). We train across multiple ML classification models to validate which fields predict outcomes in your specific business.
Accuracy
How do scores stay accurate over time?
Monthly batch recalculation learns from new conversion outcomes. Models update automatically as your business evolves - no manual retraining required.
Requirements
What infrastructure does Predict require?
A Salesforce org with historical lead data and a BYOM AI provider (OpenAI, Claude, Gemini). All processing stays in your environment. No external servers or data copies.
Setup
How long to deploy?
Analysis completes in two weeks. Includes coordination with your team to understand lead sources, qualification criteria, and routing rules. Deploys as native Salesforce components.
Security
Where does our data go?
Your data stays in your infrastructure. Lead records remain in Salesforce. GPTfy applies data masking before any scoring features leave your org - PII and sensitive fields are replaced with tokens before reaching your AI provider. A Security Audit Record is written for every scoring interaction, giving you a complete log of what data was accessed and when. The entire flow follows a zero-trust architecture: no implicit trust, no external servers, no data copies.
Platform
What is GPTfy?
GPTfy is a 100% Salesforce-native managed package. No external servers, no data copies. Over 100 enterprise customers on AppExchange.
3 problems · 3 weeks · 3 wins
Get GPTfy Prove →
Paid engagement · Under five figures
Don't take our word for it. Make us prove it.
Our forward-deployed engineers work alongside your Salesforce admin and business team, in your org, with your data, on your real leads. No presentations. No guesswork. A working AI solution your team actually uses.
"I'm a firm believer of deploying pilots and seeing the value than just looking at presentations." - CTO, Financial Services
Fixed per-user/month. Unlimited prompts. No consumption surprises. See pricing →
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