SELINA LABS
SURFACE 03IN DEVELOPMENT LIVE

CIFER

Business intelligence for risk, shrink and crime. CIFER reads an entire enterprise dataset and returns trend, cause and forecast, with a live AI analyst sitting on top of all of it. Powered by Selina.ai.

SHE NARRATES THIS SURFACE
DATASET COVERAGEFULL RECORD
INFERENCE LAYERBAYESIAN
ANALYSIS INTERFACECONVERSATIONAL
PUBLIC FRONT ENDNOT RELEASED
STRATUM 00PREMISE ACTIVE

Most organizations already have the data. Almost none can interrogate it.

Case management systems accumulate enormous, well-structured records and then hand back static reports. The reports answer the questions somebody anticipated at implementation time. The questions that actually matter are the ones nobody built a report for, and they usually only become obvious after the loss has already been recognized.

The gap is not storage and it is not dashboards. It is that interrogating the record requires either a query language, an analyst with the time to write it, or a schema change. All three are slow enough that the window in which the answer would have been useful closes before the answer arrives.

CIFER puts inference and live analysis directly over the full record. Trend estimation runs continuously rather than on request. Exceptions surface against a learned baseline rather than a fixed threshold. And the dataset can be asked a question in plain language instead of queried in a syntax.

RISK CONTOURS

Exposure resolving out of the field. Illustrative.

STRATUM 01EXCEPTION ENGINE ACTIVE

DEVIATION VS CONTROL LIMIT

Bars are observed deviation, the dashed line is the current control limit. The limit moves with the baseline, so a quiet period tightens it and a volatile one widens it. Excursions escalate.

ANALYSIS LOGSYNTHETIC

FORECAST POSTERIOR

STRATUM 02CAPABILITY MATRIX ACTIVE

01

Risk trend resolution

Surfaces where exposure is concentrating across locations, periods and categories before it registers as a loss on any report.

02

Shrink intelligence

Separates the signal of organized activity from ordinary operational variance, attributing movement to cause rather than to noise.

03

Crime pattern correlation

Correlates incident data across sites and time to expose coordination that is structurally invisible when each case is read alone.

04

Exception reporting

Probabilistic control limits replace static rules, so what reaches a human is what actually deviates rather than what merely crossed a fixed number.

05

Conversational analysis

Live AI over the entire dataset. Ask in language, receive structured analysis with its reasoning attached, not a filtered dashboard view.

06

Forecasting with intervals

Bayesian models project trend forward and state their own uncertainty, widening honestly when the evidence thins instead of guessing confidently.

07

Entity resolution

Records referring to the same person, location or event are unified across systems, turning a pile of cases into a coherent timeline.

08

Causal attribution

Distinguishes correlation from mechanism where the data supports it, and says plainly when it does not.

09

Narrative generation

Findings arrive as structured written analysis suitable for an investigator or an executive, not as a chart requiring interpretation.

9 CAPABILITIESBAYESIAN COREFULL-RECORD SCOPENL INTERFACE
STRATUM 03ANALYSIS PATH ACTIVE

From raw case record to escalated finding.

01NORMALIZEHeterogeneous incident records reconciled to a common shape at the boundary, so schema drift never propagates downstream.
02RESOLVEEntity resolution and deduplication unify records referring to the same subject, location or event across systems.
03BASELINEA probabilistic model of normal operation is learned per site and per category rather than assumed globally.
04DETECTDeviation is measured against that baseline, which makes novel patterns visible without a prior rule describing them.
05CORRELATEDeviations are tested for coordination across locations and time windows to distinguish a campaign from coincidence.
06FORECASTTrend projected forward with explicit credible intervals that widen honestly when evidence is thin.
07NARRATEFindings rendered as structured written analysis with the supporting evidence attached.
STRATUM 04SHAPE ACTIVE
01ANALYSIS LAYERLive AI chat, insight generation and structured narrative over the complete dataset.
02MODELINGBayesian trend estimation, anomaly detection and forecasting with explicit uncertainty.
03INTEGRATIONBuilt to sit on top of existing enterprise case management infrastructure.
04DOMAINLoss prevention, organized retail crime, incident correlation, exception reporting and risk exposure.
05PRIVACYInherits the Selina.ai encryption and isolation model. Client data is never pooled or used for training.
06STATUSIn active development. Public front end not yet released.
STRATUM 05DOMAIN ACTIVE
/ risk trend analysis/ shrink analytics/ crime pattern correlation/ exception reporting/ incident intelligence/ bayesian forecasting/ anomaly detection/ entity resolution/ multi-site aggregation/ causal attribution/ control limit modeling/ natural language querying/ structured narrative generation/ enterprise data pipelines/ temporal alignment
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