Blueprint Data Engine™
AI-driven data integration for technology and data leaders. The same pipeline handles one-time migration, continuous harmonization across sources, and deduplication within or across systems.
An AI-driven data integration platform. Source data from any system, format, or age is analyzed by AI to infer schema and meaning, propose mappings, generate production-ready transformation code, and execute at scale. Humans review the results, and the system learns from every correction.
/ MODE 01
Migrate
One-time movement from a source system to a target. Pipeline runs once; source can typically retire.
/ MODE 02
Harmonize
Continuous reconciliation across multiple sources into a unified canonical view that downstream tools consume.
/ MODE 03
Deduplicate
Find and consolidate duplicate records, within a source or across many sources, as a standalone phase or as part of a broader migration.
Three real voices. The same broken pattern.
Vendor quoted 18 months and €2M to migrate from our legacy ERP. We're 24 months in, €3.5M spent, still finding data issues.
VP DATA, MANUFACTURING
Three acquired companies. The same customer in five different systems under five different names. Our 'unified view' was a manual reconciliation spreadsheet.
CDO, SERVICES
Every new vendor invoice format means three weeks of engineering. Then they change their template and it breaks.
DATA ENGINEERING LEAD, FINTECH
Migration. Master data. Deduplication. Three problems, one root cause: every project is a custom engineering effort with no leverage and no learning.
Economics, not technology.
The technology to integrate data has existed for thirty years. The economics have not.
Annual integration spend, F500 enterprise
Of integration projects exceed budget
Cost per data field mapped, traditional
Engineering time per new data format
Every integration is treated as a custom engineering project. No leverage. No learning. No accumulating intelligence. Until the AI does the schema-analysis and code-writing work humans did by hand for thirty years.
AI does the work. Humans approve. The system learns.
The same pipeline handles migration, master data harmonization, and deduplication on one architecture, with one pattern library that compounds across all three.
01
Reads anything.
Excel, PDF, CSV, XML, JSON, SQL Server, Oracle, MongoDB, SAP HANA, AS/400, EBCDIC mainframe extracts, COBOL copybooks, REST APIs, S3, Snowflake. Any source, any format, any age.
02
Understands meaning.
AI determines that VND_ C D is a vendor code, INV_DT is an invoice date, this address belongs to that entity in another system. Not just structure, but semantics.
03
Writes the code.
Generates Python, SQL, Spark, dbt. Production-ready, error-handled, fully documented, version-controlled, auditable.
Every integration is treated as a custom engineering project. No leverage. No learning. No accumulating intelligence. Until the AI does the schema-analysis and code-writing work humans did by hand for thirty years.
The same pipeline. Configured for what you need.
/ MODE 01
Migrate.
WHEN
You're moving data from a source system to a target.
WHAT
Pipeline runs once, transforms the data, populates the target. Source can typically retire.
EXAMPLE
5,000+ boutique fitness studios from ZingFit to Mariana Tek.
/ MODE 01
Harmonize.
WHEN
You have data in many systems and need a single canonical view.
WHAT
Pipeline runs continuously, reconciles records across sources, maintains the canonical truth.
EXAMPLE
Customer or vendor identity unified across CRM, ERP, finance, HRIS.
/ MODE 01
Deduplicate.
WHEN
You have duplicate records you want consolidated, within or across sources.
WHAT
Same matching mechanism as Harmonize, applied to find duplicates and consolidate them.
EXAMPLE
Clean up before launching analytics, AI, or further integration work.
Most engagements use more than one mode. Pattern library compounds across all three.
ZINGFIT TO MARIANA TEK
5,000+ studios. One pattern library.
01 / THE SHAPE
Same source schema, repeated at scale.
Migration and onboarding of 5,000+ boutique fitness studios from a legacy BMS being sunset to a modern operations platform. Each studio its own tenant on source and target. Members, contracts, billing history, attendance, access control.
02 / WHAT BLUEPRINT DID
First cohort seeded the patterns.
First cohort ran through the full pipeline with substantial human review. Pattern library accumulated mature exemplars for the recurring transformation shapes. From cohort two onward, retrieval found high-confidence patterns; review collapsed to studio-specific deviations only.
03 / WHERE THE LEVERAGE WAS
Compounding at scale.
The pattern library compounded aggressively. By the long tail, individual studio onboardings ran with minutes of human attention each. Most AI-ETL pitches don't actually demonstrate this scale of compounding.
04 / WHAT HUMANS ACTUALLY DID
Cohort review and edge cases.
Initial cohort review with high effort per studio established the patterns. Edge-case adjudication on studio-specific custom data. Operational orchestration of cutovers, workflow management around the migration rather than the migration itself.
20 YEARS SAP ERP TO MODERN ERP
The hardest case in the category.
01 / THE SHAPE
Two decades of accumulated drift.
Enterprise-scale manufacturer running SAP for ~20 years. Extensive Z-table customization, deep module customizations, integrations layered by successive teams over two decades. Original developers gone. The canonical hardest case in enterprise data migration.
02 / WHAT BLUEPRINT DID
Undocumented schema evolution.
Schema has drifted from SAP standard in undocumented ways. Two decades of business-rule evolution inside the data, a status field with seventeen values where only nine still mean what they originally meant. Reconciling source customization to modern target requires explicit policy decisions.
03 / WHERE THE LEVERAGE WAS
Direct extraction and documentation.
Direct extraction from SAP. Analyze step produced complete schema documentation, the customer's first complete picture of their own 20-year installation. Standard SAP modules ran through with little review. Z-tables and customized standard tables required more attention but benefited from cross-table pattern compounding.
04 / WHAT HUMANS ACTUALLY DID
If it works here, it works anywhere.
20-year SAP migrations anchor the SI firms' practices, justify €5M+ project budgets, and fail or overrun more often than any other migration shape. Demonstrating Blueprint on this engagement establishes that the product handles the hardest case in the category. Trust on every less-hard case follows.
Why year three is more valuable than year one. Generic AI products say "gets smarter over time" as marketing. We can describe the mechanism operationally.
02
Compressed timeline.
Migration cycles that ran 12 to 18 months in traditional engagements compress to weeks-to-months. The labor that used to be senior data engineers reading legacy schemas and writing transformation code by hand is replaced with AI generation plus human review.
02
Integrated architecture, not three vendors.
A pure-MDM vendor still leaves you needing migration tooling and deduplication tooling. Three vendors, three pattern libraries, three contracts, three integrations. Blueprint Data Engine is one product, one engagement, one team.
STAGE 03
Compounding value over time.
Every correction the customer's reviewers make feeds the pattern library. The first migration is fastest-of-its-kind on the market; the tenth migration to a similar source format runs almost without human review. Year-three customers are structurally hard to displace.
Per-tenant pattern library. No model fine-tuning on customer data. The library is the customer's asset, not portable to another vendor.
Eight steps. One human checkpoint.
STEP 01
Ingest
Connect to any source, modern or legacy.
STEP 02
Analyze
AI infers structure, semantics, quality.
STEP 03
Map
Connect, ingest history, validate quality.
STEP 04
Generate
Connect, ingest history, validate quality.
STEP 05
Validate
Connect, ingest history, validate quality.
STEP 06
Review
Connect, ingest history, validate quality.
STEP 07
Execute
Connect, ingest history, validate quality.
STEP 08
Learn
Connect, ingest history, validate quality.
Same eight steps power migration, MDM, and deduplication. The mode determines configuration; the architecture is one.
Real code. Not a hypothetical.
01
Real python blueprint data engine
A competent data engineer reading this believes someone competent wrote it.
02
The right call
Currency goes to integer cents, not floats. The AI understands the domain.
03
Provenance in code
Confidence and pattern reference in the docstring. Audit trail is automatic.
Rules first. LLM where rules can't reach. Schema validation always.
/ STAGE 01
Blocking
Reduce the comparison space. Partition records into groups where matches are plausible. Rules use high-cardinality identifiers plus normalized location and name fields. LLM tunes the blocking when fields are noisy.
/ STAGE 02
Pairwise matching
Compare record pairs within each block. Field-level scoring: name similarity, address normalization, exact match on identifiers, date proximity. LLM invoked specifically for the ambiguous mid-confidence cases.
/ STAGE 03
Clustering
Group matched pairs into canonical entities. Transitive clustering with thresholds to prevent over-merging. Each input record gets a canonical entity ID; the registry stores the cluster and the match-evidence trail.
≥ 0.95 Auto-approved, audit-trail capture.
0.70 – 0.94 Routed to steward review queue.
< 0.70 Blocked, explicit decision required.
Customer-defined truth, per field
When records are matched into a canonical entity, which value wins for each field? This is policy, not algorithm. The customer decides; the system executes.
Most recent wins.
Take the value from whichever source last updated this field.
Source-of-truth designation.
Address from CRM, tax ID from finance system, name from the longest non-null value across sources.
Quality-weighted.
Take the value from the source with the highest reliability score for that field type.
Manual override.
Steward locks a specific value as canonical regardless of source updates.
Rules captured into the pattern library through the same review interface. Future updates of the same entity type apply the rule automatically.
Why year three is more valuable than year one. Generic AI products say "gets smarter over time" as marketing. We can describe the mechanism operationally.
Generic AI products say "gets smarter over time" as marketing. We can describe the mechanism operationally.
FIRST TIME
Full review
Empty library, every cell reviewed.
Pattern library is empty for this source. Human reviews every mapping. The corrections become the seed exemplars.
TENTH TIME
Auto-approved
Retrieval finds confident exemplars.
System has seen the pattern enough times. Retrieval finds high-confidence exemplars. Human reviews exceptions only.
HUNDRETH TIME
Adapts to variants
Detects drift, flags only what changed.
Source format changes? System detects the variant against its history, adapts the transformation, flags only what genuinely changed.
Per-tenant pattern library. No model fine-tuning on customer data. The library is the customer's asset, not portable to another vendor.
Where modern AI-ETL tools fall down
When a prospect has a legacy source, the evaluation is decided in the first hour. Either the tool extracts correctly, or it produces garbled text and floating-point garbage where there should be integers. Most modern AI-ETL tools fail this test.
EBCDIC encoding
Native code-page detection (CP037, CP500, CP1047, plus Asian variants). Mainframe extracts decode correctly on first connection.
Packed and zoned decimal
Native code-page detection (CP037, CP500, CP1047, plus Asian variants). Mainframe extracts decode correctly on first connection.
COBOL copybooks
Native code-page detection (CP037, CP500, CP1047, plus Asian variants). Mainframe extracts decode correctly on first connection.
AS/400 (IBM i)
Native code-page detection (CP037, CP500, CP1047, plus Asian variants). Mainframe extracts decode correctly on first connection.
Fixed-width records
Native code-page detection (CP037, CP500, CP1047, plus Asian variants). Mainframe extracts decode correctly on first connection.
Legacy date formats
Native code-page detection (CP037, CP500, CP1047, plus Asian variants). Mainframe extracts decode correctly on first connection.
SaFour deep-dives ahead. Intake and handoff, the bookends, covered after.
What the human reviewer actually sees.
/ PANEL 01
Mapping summary
Every target field, source it derives from, transformation class, per-field confidence score. Cells below review threshold highlighted; cells below blocking threshold require explicit action.
/ PANEL 02
Generated code
The actual code artifact with syntax highlighting. Read-only by default; modify-code button switches to editor. Edits feed the pattern library as correction signals.
/ PANEL 03
Sample input/output
Side-by-side preview of representative sample data and the transformed output. Live: modifications re-run against the same sample. Validation-flagged cells highlighted.
/ PANEL 04
Validation and pattern context
Validation report from Step 5 with per-check pass/fail. Below: which prior transformations were retrieved as exemplars, which corrections from history influenced this generation.
Designed so a reviewer can see all the relevant artifacts simultaneously, without switching tabs.
Five architectural commitments.
01
Schema-first extraction.
AI outputs validate against typed schemas before they enter the system. Invalid values go to review, never silently into your data.
02
Hybrid stack.
Rules and structured parsing handle the bulk. LLM only handles cases rules can't reach. Asked validating questions, never open-ended ones.
03
Provenance everywhere.
Every transformed value traces back to its source: file, page, cell, clause. Built for audit, not retrofitted.
01
Three-tier confidence.
0.95 and above auto-approved. 0.70 to 0.94 reviewed. Below 0.70 blocked. Customer configures thresholds; regulated industries tighten them.
05
Per-tenant learning.
Pattern library scoped per customer. No model fine-tuning on customer data. The library is your asset and your moat.
An honest map of the competitive surface.
COMPETITOR CLASS
WHERE BLUEPRINT WINS
WHERE BLUEPRINT WINS OR PARTNERS
Modern ELT
Fivetran, Hevo, Airbyte
WHERE BLUEPRINT WINSLegacy sources, significant transformation logic.
WHERE BLUEPRINT WINS OR PARTNERSModern SaaS sources, mostly EL pipelines.
Enterprise integration
Informatica, Talend
WHERE BLUEPRINT WINSSpeed, cost, code-first auditability.
WHERE BLUEPRINT WINS OR PARTNERSCustomer requires the incumbent as standard.
AI-pipeline natives
Nexla, Rivery, Ascend
WHERE BLUEPRINT WINSLegacy depth, schema-first hybrid, pattern library.
WHERE BLUEPRINT WINS OR PARTNERSPure breadth of features and connectors.
Pure MDM
Reltio, Informatica MDM, Stibo
WHERE BLUEPRINT WINSCustomer wants integrated migration, MDM, and dedup.
WHERE BLUEPRINT WINS OR PARTNERSStandalone deep MDM, no migration component.
Pure dedup
Tamr, Senzing
WHERE BLUEPRINT WINSDedup is part of a broader migration or MDM need.
WHERE BLUEPRINT WINS OR PARTNERSSpecialized identity resolution (fraud, KYC).
SI engagements
Accenture, Deloitte
WHERE BLUEPRINT WINSCost and timeline are the deciding factor.
WHERE BLUEPRINT WINS OR PARTNERSCustomer needs consulting, not just labor.
Five named boundaries
When records are matched into a canonical entity, which value wins for each field? This is policy, not algorithm. The customer decides; the system executes.
Not an orchestrator
Use Airflow, Dagster, Prefect. We expose hooks for them to call.
Not a CDC replication tool
For high-volume change-data-capture, use Debezium or specialized CDC vendors. We consume CDC streams; we don't produce them.
Not a data catalog
For enterprise metadata management, use Atlan, Alation, Collibra, DataHub. We produce lineage as a byproduct.
Not a data warehouse
Snowflake, Databricks, BigQuery: these are targets we write to, not products we replace.
Not a data-quality rules engine
We apply customer-defined validation rules during transformation. We don't host the dedicated DQ rule library Informatica DQ or Ataccama maintains.
Three things changed in the last 18 months.
01
LLMs got reliable for schema-bound code generation
Until late 2024, generating transformation code reliably was impossible. Error rates negated the time savings. With constrained-output decoding and current frontier models, generated transformations validate above 95% and run correctly above 90% on first execution.
02
Integration cost dropped to near-zero
Six-month integration projects to pull data from SAP are now six weeks. Connector tooling matured. Customers approve integration on first ask rather than treating it as a major procurement event.
03
Enterprises crossed the AI willingness threshold
Two years ago, an industrial CIO would not let AI touch production data. Today, board mandates exist to adopt AI. The buyer is now actively looking for the product we're building.
≥ 0.95 Auto-approved, audit-trail capture.
0.70 – 0.94 Routed to steward review queue.
< 0.70 Blocked, explicit decision required.
Tell us what you’re integrating.
We’ll tell you what’s possible.
Three ways to start. Pick the on that fits where you are. Each has a defined scope and a defined outcome.
30-min discovery“Their ability to combine strong engineering with clear product thinking made a real difference for our teams. Communication was smooth, delivery was consistent, and the results exceeded expectations.”
— Head of Engineering, Global Industrial Client
1/3 What are you building?