Every capability built to turn raw data into a decision
Mjperry combines structured risk scoring, live data feeds, and plain-language recommendations into a single workflow — no analyst team required.
What Mjperry actually does
Each feature below addresses a specific point of friction in traditional research workflows — slow data, biased interpretation, or reports that arrive too late to act on.
Predictive Risk Scoring
Incoming data is weighted against historical volatility patterns to generate a single risk score per opportunity, so you can compare options at a glance instead of reading pages of footnotes.
Continuous Data Refresh
Underlying feeds update on a rolling basis rather than a fixed quarterly cycle, which means a recommendation reflects current conditions, not last month's snapshot.
Plain-Language Summaries
Every output is distilled into a short written recommendation with the reasoning behind it, removing the need to interpret raw tables or statistical jargon.
Scenario Comparison
Place two or more opportunities side by side to see how they differ on risk, timing, and projected outcome before committing to either one.
Alert Thresholds
Set the conditions that matter to you and receive a notification only when a tracked opportunity crosses that threshold — no need to check dashboards constantly.
Exportable Reports
Generate a clean, shareable summary of any recommendation for internal review, partner discussions, or your own records.
From raw signal to a recommendation you can act on
These capabilities don't operate in isolation — they feed into a single, repeatable pipeline.
Data Ingestion
Relevant data streams are pulled in continuously and checked for consistency before anything is scored.
Risk Modelling
Each opportunity is passed through the scoring engine, which weighs volatility, timing, and historical pattern alignment.
Recommendation Delivery
The result is translated into a plain-language summary and surfaced through your dashboard or alert of choice.
Built on structured, repeatable analysis
Weighted Historical Patterns
Scoring draws on documented historical behaviour rather than isolated recent events, reducing the influence of short-term noise.
Transparent Reasoning
Every recommendation includes the factors that drove it, so you can judge the logic rather than accept a black-box output.
Consistent Review Cycle
Models are reviewed on a regular schedule against new data, keeping the scoring approach aligned with current conditions.
What each feature changes in practice
Individually, these capabilities are useful. Together, they remove the most common bottlenecks in day-to-day decision-making: waiting for a report, second-guessing a gut call, or missing a shift in conditions because no one was watching.
Mjperry is designed so that the gap between "something changed" and "here's what to do about it" stays as short as possible — without requiring you to build or maintain an analytics team.
- Before Decisions wait on manual research, delayed reports, or a scattered set of spreadsheets.
- After A continuously updated score and recommendation are available whenever you need to decide.
- Before Risk assessment varies depending on who is doing the reviewing.
- After Every opportunity is scored against the same consistent model.
See the full feature set in your own workflow
Set up takes about a minute. No long onboarding calls, no commitment required to start.
Deploy in 60 Seconds