Anomaly detection
Model-based detection across metrics, segments and regions, tuned to your benchmarks.
Insight engine
Insights you cannot see today.
STRATIS builds each insight from the campaign's own data, the objective, the strategy, the market and your decision history, then shows it with the path it took. It finds what no single view or general model can.
How an insight is made
A general model answers a question. An insight here draws on the whole picture.
Every insight follows the same path through your operation, and STRATIS keeps that path. Open the audit trail to see the data it touched, the benchmark it checked and the strategy it read.
01
A metric moves
Spend, cost, reach or conversion shifts on one placement, one audience or across a region. That is the starting point, and on its own it means very little.
02
The campaign's own data
STRATIS reads the movement against the campaign's internals: pacing, creative, audience, channel mix and how each has behaved before.
03
The objective and the strategy
Then it checks the brief: what the campaign was meant to do, the strategy behind the plan and the benchmarks that define good.
04
The market outside
It weighs category news, audience conversation and platform shifts against your own performance rather than in isolation.
05
Your decision history
What your team approved, declined and overrode before, and the reasons recorded at the time.
06
One insight, with its path
STRATIS ranks the insight by value to the business and shows every step above in its audit trail, so anyone in the room can follow how it was reached.
01
Detect
Detectors for anomaly, pacing and burn rate, segment pattern, creative fatigue and channel saturation run continuously across every channel and region. An insight fires only after a pattern has held for about three days, so your team acts on signal rather than noise.
02
Surface
Insights arrive ranked by value to the business, each with the signal, the recommended action, a plain-language rationale, projected impact and an audit trail. STRATIS holds back any insight it cannot verify against its own evidence, or hands it to a person.
03
Approve
Each insight moves through a workflow your team controls: proposed, under review, approved, executed, completed, rejected or rolled back. Escalation by dollar value, assignment and tagging are built in, and STRATIS can generate a brief from any insight.
What is in the insight engine
Detection across the whole marketing operation.
Pacing and burn rate
Spend against plan at every level, with the projected end-of-period position.
Creative fatigue
Decay in creative performance, flagged before the audience tells you.
Channel-mix reallocation
Cross-platform efficiency and where the next dollar should move, with the reasoning shown.
External signal
Custom keyword clusters read public news, forums and audience conversation against your own performance.
Brand memory
Ask plain-language questions of everything the platform knows and see the sources behind each answer.
Trust
Built for the institutions that cannot tolerate ungoverned intelligence.
Governance is part of the architecture here, built in from the first line, so the teams with the strictest oversight can use the platform without compromise.
Your data, your tenant
Every client runs in a logically isolated tenant. Data, memory and learnings never leave it, and cross-tenant movement is excluded by architecture.
No training on your data
Client data never trains any STRATIS or third-party model. We build platform improvements from internal and synthetic data.
Read-only by default
STRATIS informs. Your team decides. The platform reads from your channels and warehouse and writes nothing back.
An append-only audit trail
STRATIS records every insight, model call and operator action with its provenance, model version and data lineage.
SOC 2 Type 1 attested
An independent auditor has examined the design of our security controls.
Enterprise access controls
SSO, SAML and MFA, role-based access by region and line of business, and segregation of duties enforced at the system level.
From fragmented data to better decisions.
See the insight engine on one live decision from your own operation: what it detected, what it read and what it flagged with its evidence attached.