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Build a Custom Predictive Analytics Dashboard Using AI in Minutes

Describe your Predictive Analytics Dashboard in plain English and create a production-ready build, from design and development to deployment, in minutes. No code needed.

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Build a custom predictive analytics dashboard in 4 simple steps

Show what is likely to happen next rather than only what already did.

Put Your Idea Into Words

Describe what you want predicted and what you would do differently if you knew it.

From Prompt to Working Predictive Analytics Dashboard

Your historical data connects, so forecasts are built on your own outcomes rather than a generic model.

Refine by Prompting

Change it: a new prediction, a different confidence threshold or results by segment.

Launch and Share

Open one view where forecast and actual sit together, so the model is judged rather than trusted.

Emergent Key Features for Building a Predictive Analytics Dashboard

Scores delivered as a ranked worklist with the reasoning shown and accuracy monitored over time.

Predictions as an Action List


Scored records arrive ranked with owners assigned, so a model produces work rather than a report somebody reads occasionally.

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Every Prediction Stored with the Outcome


Forecasts are retained against what actually happened, which is what makes accuracy measurable rather than asserted.

Drift Detection


Input distributions are compared with the training period, so a model degrading because the world changed is caught before decisions suffer.

Segment Level Accuracy


Performance is reported by segment, revealing where the model is unreliable rather than presenting one overall figure.

Explanations per Record


The factors driving each individual score are shown, which is what allows the person acting on it to trust and use the prediction.

Threshold Effects Modelled


The trade off from moving the action threshold is calculated, so the cutoff is an operational decision rather than a default.

Predictive Analytics Dashboard Use Cases You Can Build in Minutes

Prediction Worklist

Deliver Predictions as Work, Not as a Chart

Current records scored by likelihood of the predicted outcome with the top contributing factors for each, the recommended action, cases already actioned with outcome recorded, high scoring cases not yet reviewed and the expected value of acting.

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window.awbMockup = { predictionWorklist: "Build a predictive analytics dashboard showing current records scored by likelihood of the predicted outcome, ranked with the top factors contributing to each score, records above the action threshold assigned to an owner, actions taken and their result, and outcomes for records that were and were not acted on.", modelAccuracy: "Build a model performance dashboard comparing predictions against actual outcomes over time, showing accuracy, precision and recall at your chosen threshold, performance by segment to reveal where the model is weakest, the effect of moving the threshold, and accuracy trend since the model was last retrained.", modelDrift: "Build a model monitoring dashboard showing how the distribution of input data has shifted since training, features whose distribution moved most, prediction distribution against the training period, accuracy decline over recent periods, records the model is least confident about, and a signal when retraining is warranted.", featureInsight: "Build a model explanation dashboard showing which inputs most influence predictions overall, how each feature affects the outcome across its range, features that have become more or less important since training, correlated inputs adding little independent value, and a plain language explanation for any individual prediction."};

Model Accuracy

Check the Model Beats the Obvious Rule

Predictions against actual outcomes over time with accuracy, precision and recall at your chosen threshold, performance across score bands, comparison against a simple baseline rule and how accuracy changed since the last retrain.

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Model Drift

Catch the Degradation Nothing Else Reports

How the distribution of input data has shifted since training per feature, features drifted beyond tolerance, the share of predictions falling in unfamiliar ranges, accuracy trend since the last retrain and a recommendation on retraining.

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Feature Insight

Explain a Score to the Person Acting on It

Which inputs most influence predictions overall, the direction of each relationship, features whose importance shifted since training, cases where the model and human judgement disagreed and features with missing data affecting reliability.

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Conversations from chat, WhatsApp and your helpdesk attach to the right contact, so sales and support see the same history.

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Invoices, subscriptions and payment status flow back into the deal, so revenue and pipeline stay in one place.

If it has an API, your CRM can connect to it.

Emergent builds the connection against the API itself, including internal tools your team made. No closed ecosystem, no approved-partner list, no waiting for a connector to ship.

Why choose Emergent?

Most tools give you a demo you have to rebuild. Emergent gives you a product that is ready to run.

ComparisonOther Tools
Built forDemos and MVPsProducts you keep growing
What you getFront end shell onlyFull stack, wired end to end
Backend and databaseSet it up yourselfBuilt and connected for you
CustomizationSurface level stylingDeep workflow control
Integrations and APIsManual glue workConnected from a prompt
Code ownershipLocked to the platformClean code you can export
Time to launchWeeks of patching and setupLive the same day

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Choose the plan that fits your building ambitions. From weekend projects to enterprise applications, we've got you covered.

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Frequently Asked Questions

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