Lead Scoring and Anomaly Detection
Models that rank incoming leads by how likely they are to convert, using your own closed-deal history, and flag transactions or records that deviate from your normal pattern, so your team checks the right ones first.
Two Related Capabilities
Both live under this page: scoring incoming leads by conversion likelihood, trained against your own closed-deal history, and flagging records or transactions that break from your normal pattern for someone to review.
How Lead Scoring Works
Signals Used
Source, engagement level, company profile and fit against past deals feed the score.
Trained on Your Own Deal History
The score is built from your own closed-won and closed-lost record, not a generic industry model.
Score Shown With Its Reasoning
The score comes with the signals behind it, not just a number to trust blindly.
How Anomaly Detection Works
Baseline From Your Normal Pattern
A baseline built from what your normal transaction or record pattern actually looks like.
Flags Raised for Review, Not Auto-Blocked
Anomalies are surfaced for a person to check, not automatically rejected.
Used for Billing, Inventory or Claims Checking
Applied to whichever record type you want deviations flagged in.
What Affects Reliability
- The volume of past won and lost deal history available for scoring.
- How well-defined "normal" is for the specific pattern you want flagged.
- How often your business genuinely changes shape — new products or markets shift what normal looks like.
Why Deal History Matters First
With very little closed-deal history to train against, scoring has nothing solid to learn from yet. That data needs to accumulate before this is worth building.
Frequently asked questions
Does lead scoring replace a salesperson's judgement?
No, it ranks the queue so reps work the strongest leads first — the decision to pursue or drop a lead stays with the rep.
What drives the cost of building these models?
Whether you need lead scoring, anomaly detection, or both, and how many data sources feed into each.
What drives the build timeline for scoring or detection?
How much closed-deal history or defined-normal transaction data is available to train against.
Does this work with our existing CRM and ERP?
Yes, lead scoring reads from your CRM and anomaly detection reads from your ERP, billing or inventory system.
Who owns the scoring model and flagged records?
You do — the model, its training data and flagged results stay yours.
What data do we need from you?
Closed-deal history for scoring, and a clear sense of what "normal" looks like for whatever pattern you want flagged.
Tell us what you need.
Send a short brief and one of our engineers will come back to you — usually the same day.
- No obligation
- We reply the same working day
- Your details stay private