Managed data products for AI systems
Datificial turns fragmented files, databases, APIs, documents, and public or licensed datasets into linked, enriched, query-ready data products, delivered through files, SQL, search, APIs, feeds, or MCP.
From source inventory and schema design to quality gates, versioned releases, and ongoing refresh.
You bring
Datificial program
The product delivers
Data product
Public, licensed, and customer-provided sources · Batch and continuously refreshed products · Human and agent consumption
One complete program
You define
The application, decision, or agent behavior the data must support, the sources available, the errors that matter, and the constraints on access, freshness, deployment, and privacy.
Datificial builds
Connectors, raw snapshots, canonical schemas, normalization, record linkage, derived features, indexes, quality gates, release packaging, and the delivery interface.
The program delivers
A versioned data release or service together with the technical assets required to reproduce, inspect, operate, and safely update it.
Data-product engineering
Acquire authorized data from files, databases, APIs, document collections, public datasets, licensed feeds, or event streams while preserving source identity and extraction state.
Convert source-specific structures into a stable canonical schema with explicit types, units, identifiers, categories, and missing-data behavior.
Deduplicate records, reconcile aliases, assign stable identities, and connect observations across sources without hiding ambiguity.
Compute the classifications, embeddings, relationships, aggregates, trends, similarities, or extracted attributes the application should not recreate at inference time.
Build retrieval and query interfaces around the actual access patterns: exact lookup, filtering, semantic search, graph traversal, comparison, or change queries.
Gate each release against the requirements of the intended use and keep source, recipe, model, and release versions traceable.
Compute once. Reuse repeatedly.
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authorized observations and snapshots
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parse, normalize, resolve, enrich, validate
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immutable version, schema, quality, lineage
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files, SQL, search, API, feed, MCP
Do not ask the model to rediscover on every request what the pipeline can compute once and verify.
The source can change. The application contract should not change silently with it.
Delivery is part of the product
Versioned Parquet, JSONL, CSV, Arrow, database tables, or warehouse models for batch analytics, training, and internal workflows.
Exact, filtered, semantic, hybrid, or graph-oriented retrieval with documented index versions and evaluation.
Bounded query endpoints, scheduled extracts, webhooks, or change feeds with stable schemas, pagination, error semantics, and access controls.
Read-only, task-specific tools that expose records, evidence, comparisons, and changes without giving an agent unrestricted source access.
Where Datificial fits
Turn internal and external sources into the records, relationships, search, and evidence an application needs before model integration.
Replace repeated browsing and prompt-time reconciliation with stable query contracts, provenance, permissions, and predictable failure behavior.
Replace manual spreadsheets and notebooks with reproducible releases, change detection, and a documented update process.
Resolve overlapping records and derive a canonical view while preserving where each observation came from and how each output was computed.
Datificial is less useful for simple format conversion, generic warehouse migration, or projects where data rights and quality ownership are undefined.
Representative patterns
Illustrative architectures, not client case studies.
Reproducible by design
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Founder-led data-product engineering
Datificial is led by Igor Bogdanov, an AI systems researcher and builder with more than 15 years of production software and systems experience. His work spans data-intensive applications, experimental AI infrastructure, compound LLM agents, evaluation, retrieval, and reliable system design. Datificial applies that combination to the layer AI projects often underestimate: the data product the model actually receives.
Selected publications, code, and reproducibility artifacts are available on the founder's research website.
Common questions
A versioned data product: canonical records and relationships, the transformation and enrichment system that produced them, quality and provenance evidence, the required delivery interface, and the documentation and refresh process needed to operate it.
Yes. Customer files, databases, document collections, and APIs are common inputs. Access begins read-only where possible, and the engagement defines where data may be processed, retained, and delivered.
ETL is one layer. Datificial also handles canonical modeling, entity resolution, semantic enrichment, derived features, retrieval, quality evaluation, provenance, release versioning, and consumer-facing delivery.
Most projects start with a short inquiry followed by a technical call. Datificial then recommends a paid assessment, a bounded pilot, or a production build based on source and product uncertainty.
Start with the data and the use case