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AIM · Adaptive & Intelligent Migration

AIM profiles your legacy data exports, proposes mappings with confidence scores, writes the transformation code from your business-rule documents and reconciles every table — with a person approving each step that matters.

  • Human-approved at every step that matters
Book an AIM demoSee it in action

Built in

  • A confidence score on every source-to-target mapping
  • Below 80% confidence, a person always reviews
  • Two-person approval for high-risk fields
  • Every decision in a tamper-evident audit log

What it migrates

Mix formats in one migration — even when headers come in a separate file.

  • CSV & TSV

    Delimited exports, with headers in the file or supplied separately.

  • Excel workbooks

    XLSX sheets read as tables, ready for profiling.

  • Flat files (.dat)

    Delimited and fixed-width records, with layouts from a data dictionary.

  • SQL dumps

    Table exports with INSERT rows, in SQL Server and MySQL dump styles.

  • Target schema

    Upload the target's schema (XML, JSON, CSV, Excel or DDL) and AIM builds the target tables.

  • Exports

    Reconciled data out as a SQL dump, a CSV bundle or Excel.

  • Live database connectors (Roadmap)

    Direct connections to Oracle, SQL Server, MySQL, PostgreSQL and mainframe sources.

  • Cloud data platforms (Roadmap)

    Load straight into Snowflake, BigQuery and cloud object storage.

Product walkthrough

A simplified run-through of the AIM workflow, step by step.

AIM

Illustrative demo

Sample data · not a client resultAgents profile legacy export files, map them to a target schema and reconcile every row, with a person approving low-confidence mappings.Illustrative AIM demo with sample data. Rows flow from three uploaded legacy export files (a CSV export of members, an Excel workbook of accounts and a fixed-width .dat file of loans) through four agent stations: automated discovery, intelligent transformation, agentic orchestration and continuous learning, into a target schema. Discovery profiles every column and reads business rules from documents; transformation code drafted from the approved rules runs in a sandbox only after a person approves it. A schema-mapping panel fills in five proposed mappings from the loans file to the target loan table, each with a confidence score. One mapping, BORR_NO to customer_id, scores 0.72, below the 0.80 threshold, so it is sent to human review; a person approves it and it is saved for reuse on the next migration. A reconciliation counter reaches 48,210 of 48,210 rows reconciled, with row counts, primary keys, nulls and monetary precision checked against the source, and every decision written to the audit trail.

How it works

  1. 01 AIM Profiler

    Discover

    Profiles every column — types, nulls, cardinality, samples — and reads business rules from your documents.

  2. 02 AIM Mapper

    Map

    Proposes source-to-target mappings with confidence scores; low-confidence ones wait for a person.

  3. 03 AIM Rule Writer

    Transform

    Turns approved rules into transformation code that runs in a locked-down sandbox.

  4. 04 Workflow

    Validate

    Checks row counts, hashes, type compatibility and schema drift; critical drift goes to a person.

  5. 05 Workflow

    Reconcile

    Compares each target table with its source, runs acceptance tests and writes the post-migration report.

  6. 06 AIM Mapper

    Learn

    Saves every approved mapping, so the next migration starts from what this one learned.

Capabilities

AI-assisted data migration from legacy exports — AIM profiles your data, proposes mappings, turns business-rule documents into approved transformation code and reconciles the result.

  • 01 Automated Discovery

    Profile every column of your legacy exports — CSV, Excel, delimited and fixed-width .dat, text and SQL dumps — and read business rules from your own documents.

  • 02 Intelligent Data Transformation

    AI drafts the transformation code from your business rules; it runs in a locked-down sandbox only after a person approves it.

  • 03 Agentic Migration Orchestration

    A durable, checkpointed workflow runs mapping, transformation, validation and reconciliation, pausing for human review and rolling back phases on failure.

  • 04 Continuous Learning

    Approved mappings become reusable knowledge, found again by semantic search on every new migration.

  • 05 Confidence-Scored Mapping

    Every source-to-target mapping carries a confidence score, and low-confidence mappings always go to a person.

  • 06 Validation & Reconciliation

    Row counts, primary-key uniqueness, null drift, monetary precision and schema drift are checked, with a post-migration report.

  • 07 Privacy & Governance

    Sensitive values are redacted before AI, a self-hosted model runs by default, and a tamper-evident audit log, field-level lineage and two-person approval cover high-risk fields.

Use cases

  • A core-system conversion from flat-file exports

    A legacy core system can only hand over fixed-width exports, and the target schema is completely different.

    1. Discover
    2. Map
    3. Transform
    4. Reconcile

    Every field is mapped with a confidence score, financial rules pass penny-precision checks, and every table reconciles with its source.

  • Consolidate data from several legacy vendors

    Members, enrollments and claims sit in different vendor formats that never agreed on one model.

    1. Survey
    2. Discover
    3. Map
    4. Transform
    5. Validate

    One target model, with a structured intake survey for client staff and one audit trail across every source.

  • Migrate regulated data with sensitive fields

    The data includes financial or health identifiers that must never reach a public AI model.

    1. Redact
    2. Map
    3. Approve
    4. Reconcile

    Sensitive values are redacted before AI, cloud models can be switched off, and high-risk fields need two approvals.

Bring a sample of your legacy exports and a target schema. We'll show you AIM profiling the data, proposing mappings and reconciling every table.

Book an AIM demo

AI agents

Each agent drafts; people decide. Validation and reconciliation are deterministic code, not AI.

  • AIM Profiler

    Understands your exports

    Classifies each file, tags columns, turns vendor notes into hints, and proposes tables for files the parsers can't read.

  • AIM Mapper

    Maps source to target

    Maps source columns to the target model with a confidence score, learning from every mapping a person approved.

  • AIM Rule Writer

    Writes the transformations

    Reads your business-rule documents, writes each rule in plain English, then drafts the code that applies it.

  • AIM Survey Assistant

    Runs the intake

    Summarizes intake surveys from client staff and answers their questions, so the migration starts with the right facts.

Security & governance

  • Redaction before AI

    Financial, healthcare, government and retail identifiers, plus names and addresses, are redacted before any text reaches a model.

  • Your model, your boundary

    A self-hosted model runs by default. Cloud models can be switched off, and are blocked automatically under a HIPAA policy.

  • Tamper-evident audit

    A hash-chained audit log, field-level lineage from source to target, and a timeline of every phase and decision.

  • Access and approvals

    Single sign-on, role-based access, tenant isolation and two-person approval for high-risk fields.

Integrations

Sources
CSV Excel Fixed-width .dat SQL dumps
Target & exports
PostgreSQL SQL dump CSV bundle Excel
AI models
Your private model OpenAI Gemini
Platform
Kubernetes Docker Single sign-on (OIDC)

On the roadmap

Part of the AIM architecture and in development.

  • Live Source Connectors (Roadmap)

    Connect directly to Oracle, SQL Server, MySQL, PostgreSQL and mainframe sources.

  • Cloud Data Platform Targets (Roadmap)

    Load straight into Snowflake, BigQuery and cloud object storage.

  • Incremental Sync & Cutover (Roadmap)

    Change-data-capture sync for near-zero-downtime cutovers.

  • Large-Estate Processing (Roadmap)

    Distributed processing for very large data estates.

Also see InsightMesh, CortexIQ and QAI

Get started with AIM

Book a demo with a sample of your exports. We'll show you the mappings, the rules waiting for approval and the reconciliation report.

Book an AIM demo

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