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Your business. Your decision model.

Teach a model
how your
business works.

Turn past outcomes into a model for your next decision. Train it on your data. Keep it learning as your business changes.

Get started See it in action

From first dataset to your next model version.

The learning loop
From past examples to your next decisionCall notes, deal history and known outcomes train your model. It predicts an outcome for a new case. New outcomes return for the next training run. This is an illustrative workflow; you review model versions before deployment.PAST EXAMPLESNEXT DECISIONCall notesDeal historyOutcomeYour modelNew sales callNew case readyProcessing…Likely to closeIllustrative output Train, decide, learnCall notes, deal history and known outcomes feed your model. It predicts an outcome for a new case. New outcomes return for the next training run. This is an illustrative example; you review model versions before deployment.PAST EXAMPLESCall notesDeal historyOutcomeYour modelNew sales callNew case readyProcessing…Likely to closeIllustrative output
New outcomes feed the next training run.
You review each version before it goes live.

Start with the data
in the tools you use.

Explore integrations

Start with a question you already ask.

Explore a sample decision

Will this deal close in the next 7 days?

Your past examples

CRM records + call notes

  1. Budget confirmed. Legal review complete.Closed
  2. Champion engaged. Budget still pending.Didn’t close
  3. Contract sent. Start date agreed.Closed
Your modelTrained for this question

A new opportunity

Budget approved. Two decision makers attended the demo. Contract review is scheduled.

78%Yes / no probability

Estimated chance of closing

Within the next 7 days

Illustrative examples and processing animation. Sample outputs are not predictions from a live model.

From business data
to a model of your own.

Bring a question and examples of what happened. PreciseDecisions brings the data preparation, training, and model versions together.

Connect. Review. Train.

Pull in records from Attio, Pylon, or Fireflies. Define the outcome you care about, then review the examples before training.

A sales call is an input.
Whether the deal closed is its label.

Keep the answer key out of training.

Separate the data used to teach your model from the examples reserved for testing. Related records stay together to reduce leakage.

Reviewed examples branch into training data to learn patterns, validation data to tune the model, and a holdout set kept outside training for independent evaluation.
Reviewed examples
  • TrainingLearn patterns
  • ValidationTune the model
  • HoldoutKept out of training
Training shows validation metrics. Export the holdout set for your own independent evaluation.

Your business changes.
Keep training.

Schedule data refreshes and retraining as new outcomes arrive. Each run creates a candidate model for you to review.

New outcomes flow through refresh and retraining into a candidate. Human review separates the candidate from the live model. Predictions produce future outcomes, completing the loop.
New outcomesRefresh data + retrainCandidate modelReview before deploymentLive model

Future outcomes feed the next training run.

Retraining can run on a schedule. Deploying a new version stays a deliberate decision.

Know which model made the call.

Every prediction has a trace. Follow it back to the model version, source data, and inputs behind the result.

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Built around your question

A different job.
The same learning loop.

01 / Sales

Which deals will close?

Learn from your pipeline history and the outcomes of past calls.

02 / Support

Who had a good experience?

Connect support conversations to the ratings customers leave.

03 / Operations

How severe is this incident?

Turn past incidents and their severity labels into a triage model.

Explore all use cases

A few useful answers.

Do I need to be an ML engineer?

No ML background is required to understand the workflow. Start with a business question and historical examples. You’ll still need to review your data and decide whether a model is good enough for the job. Training and deployment currently need setup support.

What does “post-training” mean?

It means adapting an existing model using your examples. PreciseDecisions currently trains Kev 4B for decision tasks: yes/no probabilities, categories, and scores.

What data should I bring?

Past examples that pair what was known at the time with what happened afterward. For sales, that might be call notes and deal history paired with whether the deal closed. Available connectors are Attio, Pylon, and Fireflies.

Will a new model replace my live version automatically?

No. Scheduled retraining creates a new candidate. You review its results before deployment. Automatic holdout evaluation and automatic promotion are planned, not available today.

How do I use a trained model?

Once a version is deployed, ask supported questions in the app, use it in a workflow, or call it through the API. Questions need to match the outcome the model was trained to predict.

Start with one question.
Make the model yours.

Get started