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Tape cracking is the process of harmonizing a raw loan or lease tape against a Common Data Model (CDM).

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Start harmonization

Attach the loan tape (CSV or XML) and tell the Agent to begin. For a new tape, you can enter let's crack this tape. Click Get Started. Prophecy creates a new project and identifies the schema of the source file.
Prophecy organizes work into projects. You can return to this project later by clicking the Projects icon in the side bar.

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Review the source data

Before mapping, you should review row profiles and distributions to understand the tape’s data. Click Data Profile to see detailed column-level information, including value distributions (such as the percentage breakdown across vehicle types).
See Data Profiling for a full reference on distributions and profile metrics.
Click Continue once you’ve reviewed the profile.

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Select a CDM

Choose a Common Data Model (CDM), such as auto_ABS_CDM. You can click a CDM to view its full definition. The CDM you select is part of a domain pack. A domain pack bundles the CDM together with the domain-specific knowledge needed to interpret it correctly. auto_ABS_CDM is bundled with US auto loan structures, which is why fields like obligorEmploymentVerificationCode or vehicleModelYear map correctly out of the box. Click Map Data to continue.
At this point the file is uploaded to a fabric. A fabric is a Prophecy entity that contains the connection information needed to connect to external compute and data storage. You don’t need to configure one here; it’s used automatically as part of this step.

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Agent-driven mapping

Prophecy begins harmonization, starting with deterministic mapping. The agent narrates its reasoning as it works through the tape:
Each line is a discrete action stated in plain terms. When the Agent hits something ambiguous — like an unexpected date format — it flags the item for review rather than guessing silently. agent reasoning during harmonization

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View summary of harmonization

When the Agent finishes, it returns a summary of how columns were mapped: Deterministic — historical mappings match a pattern the system has mapped before. Deterministic — direct passthrough mappings are unambiguous one-to-one field matches. AI-mapped mappings required inference. NULL mappings are target fields with no corresponding source data. The agent also returns DQ check results, such as:
  • 37 checks passed (including 1 fixed)
  • 0 checks failing
  • 0 SQL errors
Where a check fails and the Agent can resolve it automatically, it does so — for example, normalizing obligor_credit_score_type from 'FICO Score 8 Auto' to 'FICO Auto'. Finally, the Agent summarizes key mappings applied: how source fields were mapped to the standardized target schema, grouped by category (Identifiers, Dates, etc.).
  • Source — the original field name from the input data
  • Target — the corresponding field name in the harmonized schema
Where a mapping includes a format change (such as reformatting a date) the Agent displays the transformation alongside the field names, so you can verify both the mapping and the conversion in one place. Once harmonization completes, review each mapping before accepting the tape. This step is foundational — a poorly reviewed tape undermines every downstream analysis built on it. The goal is to get every mapping to an approved status.

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View review panel

The right panel shows mapping review status. Click any mapping to see:
  • The transformation applied.
  • An AI explanation of the confidence level.
  • Any DQ tests passed.
  • AI Memory — previously approved mappings inform future mapping accuracy. The more mappings you approve, the more deterministic future mappings on similar tapes become
Mapping detail panel with Source Column picker and Sample Values You can change a mapping by clicking the Source Column/Expression drop-down menu, which offers the following options:
  • No value (null) — leave the target column unmapped.
  • Function Expression — write a custom expression manually
  • Use AI to generate expression — have AI generate a transformation instead of a direct field mapping
  • Columns — map directly from an available source field
A Sample Values panel shows real data from the selected source column, so you can confirm the mapping is correct before accepting it.

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Review mapping results

Review the overall breakdown, for example:
  • 62 deterministic (historical)
  • 15 deterministic (direct pass-through)
  • 17 AI-mapped
  • 78 null
  • 72 high-confidence
Click View More for the full count table. For each mapping, Accept, override, or reject it, then use Accept & Next to move through the list.

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Filter and sort mappings

Use the Filter icon (top right) to isolate mappings that need review. You can filter by:
  • AI confidence — High, Medium, Low, None, Overridden
  • Data quality — Passed, Failed, No DQ checks
  • Error status — Has error, No error
You can sort by:
  • Default order
  • Name (A → Z)
  • Name (Z → A)
  • Confidence (low → high) — surface what needs the most review
  • Data quality (worst first)
  • Status (unmapped → accepted)
Filter mappings Sorting by confidence (low → high) or data quality (worst first) prioritizes manual verification on the mappings most likely to need it.

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Correct a mapping via chat

To fix an incorrect mapping, describe the issue and the correct value directly in chat — for example, correcting a deal_id mapping.
The agent overwrites the mapping; you then accept it.

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Meta-questions during review

You can ask the Agent questions about the review state at any point — for example, “how many source columns were there in total and how many were mapped?”

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Add DQ tests

The agent can suggest data quality tests to add. Adding these improves the Agent’s mapping performance on future tapes, not just the current one.

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Add a new CDM

To add a new CDM:
  1. Click the + at the top of the review panel, then select CDMs.
  2. In the dialog, enter a name for the common data model and click Create.
  3. In the new CDM, choose Add Table or Upload Schema.
  4. If you choose Add Table, enter names, types, descriptions, and DQ checks for each column.

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Complete the tape

  1. Continue reviewing remaining confidence tiers (medium, low, etc.). If you’re certain of a mapping, you can specify it directly and check sample values in the window.
  2. Once every mapping is accepted, click Output Preview to preview the harmonized result.
  3. Once satisfied, click Done.
  4. The tape is marked ready. Download it as Excel or CSV, or add it as a table to your SQL Warehouse.
Continue to Stratifications & Pool Selection to run analyses on the harmonized tape.