The network is already in the data. Read it out.
A free-standing canvas for link analysis. Import call records, bank statements and travel data, map the columns once, and get a typed entity graph you can pivot, annotate and defend — then fuse several of them into one case.
It already knows what a call-detail record looks like.
Point Notebook at a CDR dump, a bank statement or a travel manifest. It detects the columns, you confirm the mapping once, and the file becomes a typed graph rather than another spreadsheet.
Sixteen node types, seventeen edge types — and your own on top.
The default taxonomy covers most investigations. When it does not, an organisation defines its own entity and relationship types, with its own uploaded icons, and every board resolves against that catalogue.
Measured, not eyeballed.
Who matters in a network is a calculation, not an impression. Each measure below answers a different question, and every result can be reproduced and defended.
- Degree
- Who talks to the most people.
- Betweenness
- Who sits on the paths between otherwise separate groups — the broker.
- Closeness
- Who can reach the whole network fastest.
- Influence
- Who is important because important people connect to them.
- Clustering coefficient
- How tightly knit a subject's immediate circle is.
- Community detection
- The groups the data actually forms, rather than the ones you expected.
Eighteen typologies, found before any model runs.
These are deterministic detectors with published thresholds, not an AI opinion. Each finding carries the node ids behind it, so clicking one highlights the exact records that triggered it — and if the language model is unreachable, the findings are still there.
- Structuring
- Three or more transactions deliberately sized just under a reporting threshold.
- Threshold avoidance
- Amounts clustering below a declared limit across accounts.
- Smurfing
- Many small deposits from many parties converging on one destination.
- Layering
- Funds in and straight out again through intermediate accounts to break the trail.
- Rapid pass-through
- Money arriving and leaving within hours, leaving no balance behind.
- Funnel account
- One account collecting from many and paying out to few.
- Dormant reactivation
- An account or handset silent for months resuming activity.
- One-way broadcast
- A number that only ever calls out, never receives — a tasking pattern.
- Activity burst
- Traffic far above a subject's own established baseline.
- Odd hours
- Communication concentrated in the hours a subject is normally silent.
- Signal calls
- Repeated calls under five seconds — contact made without conversation.
- Shared contact
- Two subjects who should not know each other calling the same third party.
- Device sharing
- One handset carrying several SIMs.
- Handset rotation
- One SIM moving between several handsets.
- Cross-modal convergence
- The same pair appearing in both the call data and the money trail.
- Unresolved duplicates
- The same person or number entered twice under different spellings.
- Fragmented graph
- Disconnected islands that suggest a dataset is incomplete.
Thresholds are published rather than hidden: a signal call is a called edge totalling under five seconds with weight three or more; structuring is three or more transactions in the 8,000 to 10,000 band.
Questions only a fused case can answer.
Attach several notebooks to a project and every real-world entity appears once. Then the cross-dataset analytics become possible — the ones that need the phone records and the bank statements in the same frame.
- Temporal coupling
- Events in two datasets that keep happening close together in time.
- Co-location
- Subjects placed at the same location within the same window.
- Convergence
- Separate threads arriving at the same entity from different directions.
- Unified timeline
- Calls, transactions and movements on one axis.
- Money and comms overlay
- The transfer that follows the call, made visible.
- Entity resolution
- Automatic merge on exact phone, email, wallet, IMEI, IMSI or account number; name similarity goes to a review queue you can confirm, dismiss or reverse.
A missing precondition is reported as such. “No coupled events” and “your financial data has no timestamps” are different answers, and Notebook does not conflate them.
The case leaves in one file, analysis included.
Notebook never ships a model key. Point it at the provider your organisation already has under contract; credentials are sealed at rest.
Platform names and logos are trademarks of their respective owners. Their presence here identifies the sources CLERINT can lawfully collect from, and does not imply any partnership, endorsement or affiliation.
The things people ask before a briefing.
Does it read call-detail records?
Yes, and it detects the columns itself — A-party and B-party, timestamp, duration, call type, cell ID, tower, LAC, CGI, IMEI and IMSI. You confirm the mapping once and the file becomes a graph of phones, devices and locations.
What file formats can I import?
CSV, XLS, XLSX and JSON, several files at a time, up to 64 MB and 500,000 rows per file. The limits are published and enforced before upload rather than after.
Do I need a CLERINT subscription to use it?
No. Notebook stands on its own. It reads a CLERINT case when one is connected, but it does not require one.
Can I define my own entity types?
Yes. The sixteen node types and seventeen edge types are a starting point; an organisation can define its own types with its own uploaded icons, and every board resolves against that catalogue.
Can I take a case out of the system?
Yes — as PNG, JPG, HTML or CSV, or as a portable .clnb archive containing the graph, the analysis, the type definitions and the raw records with checksums. Model credentials and identity keys are deliberately excluded.
Bring one dataset.
A single CDR dump or bank statement is enough for a walkthrough — we will import it with you and show you what the graph says about it.