SKALV
Evidence for operational decisions
SKALV helps organisations collect evidence, identify meaningful signals, understand possible consequences, and verify what remains uncertain. Teams can then work from one shared picture they can follow.
Illustrative sample
Illustrative sample
Bridge Infrastructure
Impact reported
Illustrative sample
Power Disruption
Multiple regions
Illustrative sample
Relief Convoy
En route
The problem
Reports, feeds and observations arrive from different places. Some are incomplete. Some conflict. Some go quiet. Without structure, it is hard to know what matters — or what is missing.
The SKALV approach
SKALV organises incoming information so people can weigh the support behind it, see where it came from, and notice where judgement is still needed.
How SKALV works
The main workflow helps teams move from raw information to reviewed understanding. Other workspaces help with sources, nodes, blind spots and previous findings.
01
Information enters the system and is captured clearly before anything is treated as an operational signal.
02
Observations that may matter can be identified, prioritised and assessed — with their origin kept visible.
03
Teams can model what a signal may affect across linked nodes and functions — and why it matters.
04
Uncertainty can be tested through human review tasks linked back to the evidence and assessments that support them.
The result
SKALV does not remove uncertainty. It makes evidence, gaps and review status easier to see — so decisions rest on clearer ground.
Where information came from, source visibility, structured findings and human verification stay part of the picture — not hidden behind a score.
Why SKALV
See the support behind a claim, not only its severity. Conflicting sources stay visible instead of being averaged away.
Signals are related to the nodes and dependencies you have modelled, so exposure is specific and inspectable.
Human verification is recorded as evidence with origin and time, and can leave an uncertainty open.
Context layers
Walkthrough
An illustrative sequence showing how the same event moves through the platform. The data is synthetic.
01
A public warning feed reports disruption in a district. A field report arrives twenty minutes later describing something narrower. Both are recorded with their origins.
02
The two items are grouped into one signal. They disagree about extent, and the disagreement is retained rather than resolved by the system.
03
Two nodes are possibly exposed through a declared dependency. Neither is marked as affected. A verification task goes to the operator who can answer it, and their reply is recorded as evidence.
Trust
Supporting capabilities
Alongside the main workflow, SKALV includes workspaces for sources, nodes, the map, blind spots, findings, archive and foresight. Explore a few of them here.
Figures below are illustrative seed samples, not live customer metrics.
Archive
Browse finished records kept for later reference — not a live incident feed.
Illustrative sample
Flooding reported
Northern District
Road closure confirmed
Highway 27
Heavy rainfall detected
Weather sensor grid
Live Map
After sign-in, see your organisation’s monitored nodes on a map, with related status when available.
Foresight
Describe possible developments with clear evidence links — not deterministic prediction.
Illustrative sample
Trace
18,392
Events ingested today
Resonance
76%
Signal coherence
Dissent
12%
Contradicting reports
Veil
Moderate
Information opacity
Verification Queue
24
Items awaiting review
3 Critical