AI scoring built on top of your CRM. Enriched firmographic signals, LLM inference, score explanations SDRs can read. Routed to the right rep in under five minutes.
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monthly retainer
Who this is for
You run RevOps or marketing at a B2B company where inbound volume has outpaced SDR capacity. The HubSpot or Salesforce lead score is still based on rules someone set up three years ago. SDRs ignore it because it doesn't match their gut — and nobody has time to retune it. I build AI lead scoring automation that trains on your actual conversion history, enriches each lead with firmographic and intent signals, and routes automatically so the right rep gets the right lead before the window closes.
The pain today
- Inbound volume exceeds what SDRs can manually qualify
- CRM lead score built on stale point rules nobody tunes
- SDRs ignoring the score because it doesn't explain itself
- Enterprise and SMB leads landing in the same queue
- No visibility into why a lead ranked high or got ignored
The outcome you get
- ML model trained on your historical closed-won and closed-lost data
- Enrichment layer (Clearbit, Apollo, or custom) feeding each score
- 2-3 sentence explanations SDRs can read and act on
- Automatic routing — enterprise to AEs, mid-market to SDRs, cold to nurture
- Weekly recalibration as new conversion data accumulates
Why rule-based scoring stops working
Rule-based lead scoring works fine when your ICP is stable and someone with domain knowledge maintains the weights. Neither condition holds at most B2B companies past 50 inbound leads per week. The rules were written for last year's buyer, they don't account for new product lines, and nobody's job description includes tuning them. So SDRs develop their own gut filter and the score becomes decoration.
AI lead scoring automation replaces the maintenance problem with a training problem. Instead of asking 'which rules should a lead satisfy?', the model asks 'which historical leads that looked like this one actually converted?' That question has a measurable answer, and it recalibrates automatically as new conversion data comes in.
I've seen this pattern at companies of every size. The scoring system isn't wrong because the team is lazy — it's wrong because static rules can't keep pace with a live market.
ML model trained on your historical closed-won and closed-lost data
Signal inventory: what you already have vs. what's worth adding
Most B2B companies have more scoring signal than they realize. Already captured: firmographic data (company size, industry, revenue range from CRM), behavioral data (pages viewed, time on site, content downloaded), and engagement signals (email opens, clicks, event attendance). These alone are enough to outperform a rule-based score.
Missing but accessible: tech stack (BuiltWith, Wappalyzer), funding signals (Crunchbase, PitchBook), and intent data from providers like G2 or Bombora. Enrichment typically costs $0.10–$1.00 per lead through Clearbit, Apollo, or ZoomInfo.
My first step with any customer is a signal audit — what you capture, what quality it's actually in, and which gaps are worth the enrichment cost. Buying every data source available before you know which signals predict conversion in your specific market is a fast way to spend $2,999/mo on noise.
+500%: Lead base growth.
How the hybrid scoring model works
The scoring architecture I build combines classical ML with an LLM layer. Classical ML — logistic regression or gradient boosting — handles numeric features well: firmographics, behavioral counts, engagement frequency. It produces the baseline score and the 'of similar leads in your history, X% converted' logic. That part is fast, cheap to run, and fully interpretable.
The LLM layer handles unstructured signal: website copy, LinkedIn bio, email body, job title nuance. It infers qualitative fit that numeric features miss — for example, a company that mentions SOC 2 compliance on its homepage is a different ICP signal than one that mentions cost savings, even if both are 200-person SaaS companies.
Combined, the two layers produce a score plus a plain-language explanation. Each model contribution is auditable. This hybrid consistently outperforms pure-LLM scoring on cost per lead and outperforms pure-ML on unstructured signal coverage. I built a similar pattern for Norte Web Digital's custom CRM, which processed 250 new leads per day and grew their pipeline by over 500%.
CRM integration: HubSpot and Salesforce
Scoring that doesn't land in the tool SDRs open every morning is scoring nobody acts on. In HubSpot, I write scores back to a custom contact property, put the explanation in a second custom field, and trigger routing via HubSpot Workflows based on score thresholds plus firmographic conditions. In Salesforce, scoring lands on the lead object and routes through assignment rules or Salesforce Routing.
Both integrations use webhooks so scoring happens in seconds, not overnight batch jobs. New form submissions score immediately. Existing contacts rescore on a daily sweep when new behavioral data comes in.
One thing that trips up most HubSpot-Salesforce hybrid shops: if deals close in Salesforce but the outcome never syncs back to HubSpot, the model trains on a partial picture. I audit the sync before building — a scoring model trained on incomplete closed-won data is worse than no model at all.
Score explanations that build SDR trust
A number without reasoning is a number people ignore. Every score I ship includes a short explanation generated by the LLM layer and cached until the next rescore: what signals pushed it up, what's missing that would raise confidence, what the rep might want to probe on in discovery.
A real example looks like this: 'Score 82 (high). SaaS company, 150 employees, US-based — matches ICP on all three dimensions. Two decision-makers visited the pricing page in the last 10 days. Missing: direct budget signal. Suggest a discovery call to confirm timeline and authority.'
SDR skepticism about AI scoring is healthy and usually fades after four to six weeks of visible accuracy. I share weekly calibration reports — which high-scored leads converted, which low-scored leads correctly sat in nurture — so the team sees the model working rather than taking it on faith.
Routing logic and threshold design
Score alone doesn't produce speed-to-lead. Routing decisions are where score value actually converts to pipeline. The threshold structure I design with most RevOps teams looks something like this: leads scoring 80 or above with enterprise firmographic fit route to AE queue within five minutes of submission. Scores 60-79 go to SDR round-robin. Below 60 enter automated nurture with weekly rescoring. Explicit wrong-fit leads get marked as such so your pipeline forecast stays honest.
Threshold design is collaborative — the AI provides the score, the RevOps team provides the routing logic that fits the actual sales motion. What counted as SDR-worthy six months ago may now be AE-worthy as the market shifts. Routing rules update quarterly as conversion data accumulates.
The goal isn't to automate the SDR out of existence. It's to give them a queue they can trust and a reason to act on it fast.
Timeline, pricing, and what the retainer covers
AI lead scoring fits the AI Automation retainer at $3,999/mo. First-version timeline is four to six weeks: signal audit and data quality assessment in week one, model training and enrichment integration in weeks two and three, CRM integration and routing setup in weeks four and five, and calibration with the SDR team in week six.
The retainer continues past launch. Weekly recalibration incorporates new conversion outcomes. Quarterly model retraining refreshes the baseline as your ICP evolves. SDR feedback on score accuracy is a continuous input — I want to hear when the model gets it wrong, not just when it gets it right.
14-day money-back, cancel anytime, Work Made for Hire. Enrichment provider costs (Clearbit, Apollo, ZoomInfo) and LLM API costs run directly on your accounts — typical range is $200–$2,999/mo depending on lead volume and data source selection.
Recent proof
A comparable engagement, delivered and documented.
A custom CRM that turned Google Maps into a lead machine
Built a purpose-built CRM for a digital agency that captures leads from Google Maps, reaches them via official WhatsApp (Twilio + Meta API), and uses AI to suggest replies and standardize templates. The system scaled the lead base by over 500%, with 250 new leads entering the pipeline every day.
Read the case studyKeep reading
Frequently asked questions
The questions prospects ask before they book.
A minimum of 500 leads with known outcomes — closed-won, closed-lost, or no-response — gives enough signal for a baseline model. Five thousand or more leads produces a noticeably more confident result. Below 500 leads, I start with a rules-based score and transition to ML as data accumulates. I'll tell you honestly what's achievable with your current data before any work begins.
That depends mostly on explanation quality and early accuracy. I run a four-to-six-week calibration phase with SDR leadership: weekly accuracy reports showing which high-scored leads converted and which low-scored leads didn't. Most skepticism fades once the team sees the model calibrate against real outcomes. The explanation field does more work than the number itself — a score of 82 with a reason is actionable; a score of 82 with no context is just a number.
Yes. A weekly sweep rescores the full database as new behavioral data accumulates. Dormant leads that now match ICP better — a new hire appeared on LinkedIn, the company raised a round, they revisited the pricing page — resurface in the right queue. Reactivating right-fit leads from the existing pipeline is often where the highest immediate ROI appears, because those leads already know who you are.
HubSpot's native predictive scoring works from behavioral engagement data it already captures. It doesn't incorporate external enrichment (tech stack, funding, intent data), and it can't reason over unstructured signals like website copy or job title nuance. A custom model trained on your specific conversion history with enriched firmographic data tends to outperform the out-of-the-box model — especially for companies with a narrow ICP where generic patterns don't apply.
Lead data is processed under the same privacy rules you already apply to your CRM. Enrichment providers' DPAs cover their data sources. LLM processing routes through Anthropic or OpenAI Enterprise, both of which offer data processing agreements. EU-only data residency is achievable with Azure OpenAI in EU regions or a self-hosted model. I document the full data flow before any integration goes to production.
Yes. Most major MAPs — Marketo, Pardot, HubSpot Marketing Hub, Mailchimp — support the same webhook-based score writeback pattern as the CRM integrations. Score changes trigger workflow updates in the MAP the same way they do in HubSpot or Salesforce. A MAP integration typically adds about one week to the initial timeline and is scoped case by case depending on which platform and which workflow triggers matter for your team.