Technical capabilities
The difficult work between raw data and useful inference.
Datificial combines conventional data engineering, semantic modeling, machine learning, retrieval, and controlled LLM enrichment to produce stable analytical state rather than prompt-time improvisation.
01
Source ingestion and preservation
Obtain authorized source state reproducibly.
- file and object-store ingestion
- database snapshots and incremental extraction
- API pagination, cursors, rate handling, and retries
- document and archive collection
- event and change streams
- source checksums and extraction manifests
- immutable raw zones
- schema and availability monitoring
- Outputs
- Source snapshots, extraction logs, raw manifests, replayable batches.
- Failure modes addressed
- Missing pages, duplicate extraction, mutable inputs, partial runs, silent source schema changes, and non-idempotent refresh.
02
Document and semi-structured parsing
Convert human-oriented formats into inspectable records.
- HTML, XML, and JSON parsing
- PDF and office-document text and table extraction
- template-aware section parsing
- OCR only where necessary and quality-controlled
- nested API normalization
- table and key-value extraction
- chunking based on document structure rather than arbitrary token windows
- parse confidence and quarantine
- Outputs
- Structured document objects, sections, tables, fields, source spans, parse diagnostics.
- Failure modes addressed
- Layout loss, duplicated headers, malformed nested data, mixed encodings, broken tables, and unsupported documents.
03
Canonical schema and normalization
Make records comparable and predictable across sources.
- typed canonical schemas
- source-to-canonical mappings
- units, currencies, dates, time zones, and locale normalization
- controlled vocabularies and taxonomy mapping
- identifier formatting and validation
- address and name normalization
- explicit null, unknown, not-applicable, and redacted states
- schema versioning and compatibility rules
- Outputs
- Canonical records, validation errors, source mapping documentation, schema registry.
- Failure modes addressed
- Overloaded fields, silent coercion, ambiguous nulls, incompatible units, and breaking schema drift.
04
Deduplication and entity resolution
Establish stable identity without erasing ambiguity.
- exact and normalized identifier matching
- blocking and candidate generation
- string, address, numeric, temporal, and semantic features
- probabilistic or learned match scoring
- graph-based alias representation
- merge, split, and no-decision policies
- human review queues
- precision and coverage evaluation
- Outputs
- Stable entity IDs, aliases, matched-record groups, conflict records, confidence and review state.
- Failure modes addressed
- False merges, duplicate entities, identity drift, source-specific IDs, and irreversible overwrite.
05
Classification and semantic enrichment
Add reusable interpretation required by the application.
- rule-based classification
- supervised and zero- or few-shot classifiers
- embedding representations
- LLM-assisted extraction and categorization
- ontology mapping
- multi-label and hierarchical taxonomies
- calibrated confidence and abstention
- human adjudication and active-learning loops
- Outputs
- Derived attributes, labels, embeddings, taxonomy links, method and evidence metadata.
- Failure modes addressed
- Category drift, forced labels, inconsistent prompts, unsupported generated fields, and no visibility into method version.
06
Relationships, similarity, and analytical features
Precompute the structures applications repeatedly derive.
- pairwise and neighborhood similarity
- rule- or evidence-based relationships
- graph construction
- clustering and segmentation
- aggregates and rolling windows
- trend, anomaly, and change features
- cross-source joins
- application-specific scores
- Outputs
- Relationship edges, feature tables, clusters, similarity indexes, event streams, score documentation.
- Failure modes addressed
- Uncalibrated scores, unstable clusters, hidden leakage, transitive assumptions, and relationships presented as facts without method labels.
07
Search, retrieval, and query interfaces
Expose the product through the access patterns the consumer requires.
- exact lookup and filtering
- relational and columnar queries
- full-text search
- vector and hybrid retrieval
- metadata filters
- graph queries
- reranking where justified
- pagination, caching, and query budgets
- retrieval evaluation with representative questions
- Outputs
- Indexes, query endpoints, evaluation sets, latency and cost reports, error contracts.
- Failure modes addressed
- Retrieval over unnormalized records, stale index state, source leakage, irrelevant semantic matches, and unbounded agent queries.
08
Temporal data and change detection
Preserve how records and relationships evolve.
- source snapshot comparison
- slowly changing dimensions
- effective-from and effective-to intervals
- field-level diffs
- entity merge and split history
- significance thresholds
- change-event classification
- backfill and correction handling
- Outputs
- Historical records, change events, release diffs, alerts, temporal query support.
- Failure modes addressed
- Overwriting history, false alerts, repeated events, and inability to explain when a field changed.
09
Quality, evaluation, and provenance
Determine whether a release is fit for its intended use.
- schema and type checks
- completeness, uniqueness, referential integrity, and consistency
- distribution and drift testing
- labeled samples for resolution and classification
- retrieval relevance and coverage fixtures
- source-span and field-lineage checks
- cost, latency, and throughput tests
- release comparison and regression suites
- Outputs
- Quality report, failed-record quarantine, evaluation metrics, limitation register, release approval record.
- Failure modes addressed
- Data that is valid but does not answer the needed questions, silent semantic regression, and quality claims without measurement.
10
Orchestration and maintained operation
Turn a successful notebook or script into a reliable release process.
- containerized stages
- dependency and run graphs
- idempotent tasks
- checkpoints and resumability
- resource and cost accounting
- retries, dead-letter handling, and incident state
- secret management
- scheduled and event-driven runs
- monitored release publication
- Outputs
- Production pipeline, run history, deployment definitions, monitoring, and runbooks.
- Failure modes addressed
- Manual reruns, partial outputs, unknown versions, hidden credentials, and no rollback.
The method is selected by the transformation.
Not by the marketing category. A regex may be better than an LLM. An LLM may be better than a brittle parser. An embedding may support retrieval but not factual extraction. Datificial evaluates the simplest method that can meet the product's quality and operating requirements.