The system of record for how data changes in regulated enterprises
Magellan AI builds the audit‑ready mapping and metadata control layer for regulated data. We replace fragile spreadsheets and opaque pipelines with a governed, ULID‑keyed, hash‑chained record of every schema, mapping, and transformation — so every connection between systems is discoverable, traceable, and defensible to an auditor.
Our Mission
Data teams spend up to 80 % of their time understanding and reconciling schemas across sources. Magellan eliminates that friction by ingesting any schema format into a unified MetaModel, enabling predictive mapping, automated governance, and full data lineage — all from a single platform designed for both domain experts and engineers.
Every schema — Avro, JSON, XML, Excel, JDBC — is transformed into a single, version-controlled MetaModel, eliminating format silos.
Five algorithm classes — exact match, synonyms, abbreviations, hierarchy, domain equivalence — score each candidate pairing 0.0–1.0 with explainable provenance. Reviewers see ACCEPT (≥0.85), REVIEW (≥0.65), or REJECT bands, and every accepted mapping appends to the ULID‑keyed ledger.
Semantic versioning, draft-and-publish workflows, RBAC, and full audit trails keep every change traceable and compliant.
Understand how data flows end-to-end and instantly see the downstream impact of any schema change before it ships.
Founder
Founder · CEO · Principal Investigator
Loren Cahlander is the founder and sole member of Magellan AI Corporation. With over two decades of experience in data architecture, digital transformation, and full-stack application development, he has built a career around solving complex data harmonization challenges for enterprises and government agencies alike.
An abstract and lateral thinker, Loren specialises in helping organisations unlock the true potential of their data through knowledge graphs, NoSQL databases, semantic technologies, and advanced search. His deep expertise in XML, XQuery, SPARQL, and modern cloud architectures underpins the Magellan platform's ability to ingest any schema format and unify it into a single, governed MetaModel.
Loren holds a Master of Science in Software Engineering from the University of Minnesota, Twin Cities, where he graduated with a 3.78 GPA. His graduate coursework in database systems, software architecture, quality assurance, and user interface design provides the academic foundation that complements his decades of industry experience.
Earlier in his career as Chief Software Architect at ZH Computer, Loren built an entity-enrichment pipeline using Apache UIMA with Hidden Markov Models, developed an eXist-db application that leveraged Stanford NLP for named-entity recognition and annotation, and applied the Text Encoding Initiative (TEI) standard to structured document analysis — foundational work that informs Magellan's modern Semantic Match Engine.
Prior to founding Magellan AI, Loren served as a principal consultant delivering large-scale data integration and search solutions across federal, financial-services, and life-sciences sectors. That hands-on experience with real-world data complexity directly informs Magellan's mission: to make metadata the single source of truth and eliminate the manual toil that slows every data initiative.
NLP Heritage
The live Semantic Match Engine runs on Google Gemini 2.5 Flash through a versioned semantic-match edge function. The design choices behind it — typed annotations, decomposable confidence, structured metadata — were shaped by two decades of work in entity enrichment, statistical NLP, and structured-document encoding our founder pioneered while Chief Software Architect at ZH Computer.
Built an entity-enrichment pipeline using Hidden Markov Models for named-entity recognition. The lesson — typed annotations are infrastructure, not free text — informs Magellan's layered attribute annotations: semantic type, domain, lineage, and sensitivity.
Integrated Stanford CoreNLP into an eXist-db application for named-entity recognition over structured XML. The lesson — confidence must be explainable — informs today's response shape: integer confidence 0–100 paired with one of five whitelisted algorithm classes and a rationale.
Applied the Text Encoding Initiative standard to structured document analysis. That discipline directly inspired Magellan's MetaModel — treating metadata as first-class, machine- addressable structure (Metadata as Code™).
What runs in production today
Engine
semantic-match edge function v1.0.0, calling google/gemini-2.5-flash via the Lovable AI Gateway at temperature 0.1 with a strict JSON response schema.
Response shape
Integer confidence (0–100), one algorithm class, and a short rationale for the reviewer.
Algorithm classes
exact_match, synonym_match, abbreviation_match, context_hierarchy, domain_equivalence.
Decision policy & ledger
Healthcare defaults: AUTO-ACCEPT ≥ 85, REVIEW 65–84, REJECT < 65. Every decision is appended to a ULID-keyed, SHA-256–chained audit ledger — a Universally Unique Lexicographically Sortable Identifier whose millisecond timestamp prefix makes the chain chronologically self-ordering and supports point-in-time reconstruction under HIPAA §164.312(b) and 21 CFR Part 11 §11.10(e).
NLP Heritage Brief (PDF)
20-year lineage from Apache UIMA, Stanford CoreNLP, and TEI — and how the live Gemini 2.5 Flash engine implements those lessons.
Algorithm Classes Brief (PDF)
The five whitelisted algorithm classes returned by the semantic-match edge function, with worked examples across FHIR, FinServ, retail, manufacturing, public-sector, and supply-chain.
Decision Policy Brief (PDF)
Versioned threshold profiles applied to the engine's 0–100 confidence — AUTO-ACCEPT / REVIEW / REJECT cutoffs across healthcare, finance, and SOX-governed regimes.
Audit Ledger Brief (PDF)
Ledger schema keyed by a unique identifier (ULID — time-ordered, lexicographically sortable, no central coordination) and SHA-256 chained; how every decision is reproducible months or years later, with model version, threshold profile, and annotations preserved for HIPAA and 21 CFR Part 11 audit.
Federal-First
Magellan is architected with FISMA and FedRAMP readiness in mind, delivering the security posture, audit trails, and role-based access controls that U.S. federal agencies require — while remaining flexible enough for any enterprise data landscape.
The system of record for how data changes in regulated enterprises
Magellan is the system enterprises use to guarantee data integrity and auditability across all data movement.
Pricing, market focus, regulatory posture, and current traction — the questions that matter when evaluating a governance infrastructure investment.