Build the data product around the decision, application, or agent.

01

AI application data layer

A team has a working model experience but the application depends on manual data prep, fragile scripts, inconsistent metadata, and repeated prompt-time reconciliation.

Datificial work

• source inventory and canonical schema
• entity and record resolution
• attribute extraction and normalization
• semantic representations
• quality and evidence fields
• search and API layer
• refresh and change process

A stable, versioned application data layer that the model and conventional code can consume through the same contract.

• · fewer prompt-time transformations
• · lower token and latency overhead
• · more predictable retrieval
• · inspectable facts and derivations
• · safer source updates

02

Bounded data tools for agents

An agent currently browses raw sources or receives broad database access. It spends steps finding structure, produces inconsistent queries, and cannot reliably explain provenance.

Datificial work

• define task-level query contracts
• prepare stable entities and relationships
• build permission-aware indexes
• expose evidence separately from summaries
• implement typed errors and limits
• evaluate tool use on representative agent tasks

A read-only API or MCP service that gives the agent bounded access to records, search, comparison, evidence, and changes.

• · smaller action space
• · reduced data leakage risk
• · fewer invalid queries
• · reproducible behavior across agent runs
• · clear audit trail

03

Semantic search and discovery

Keyword search and database filters cannot capture the concepts users need, but a vector index built directly over raw chunks produces noisy or unauditable results.

Datificial work

• document and record modeling
• canonical metadata
• chunking based on source structure
• embeddings and hybrid index
• entity-aware aggregation
• relevance fixtures and failure analysis
• source-span and version links

A search product with stable records, filters, semantic retrieval, evidence, and measured relevance.

• · semantic access without discarding exact filters
• · less duplicate content
• · better result explanations
• · index tied to a release
• · safer re-embedding and rebuilds

04

Entity and relationship product

Several sources describe the same people, organizations, products, assets, publications, transactions, or events without common identifiers.

Datificial work

• canonical entity model
• blocking and match features
• merge and split policy
• conflict preservation
• relationship extraction or inference
• human review and quality sampling
• historical identity changes

Stable entities, source-linked observations, relationships, aliases, and confidence or review state.

• · one identity across sources
• · fewer double counts
• · explicit ambiguity
• · reusable joins
• · reliable historical analysis

05

Recurring research and intelligence

Analysts repeatedly collect, reconcile, classify, and compare the same sources in spreadsheets, documents, and notebooks.

Datificial work

• formalize the recurring questions
• convert sources into canonical records
• resolve identities and categories
• preserve evidence and analyst corrections
• produce releases and change events
• deliver query and export interfaces

A maintained analytical product replacing a one-time research artifact.

• · repeatable updates
• · shared definitions
• · lower manual effort
• · source traceability
• · historical comparisons

06

Document-to-structured-data product

Important facts are trapped in PDFs, reports, forms, manuals, filings, contracts, tickets, or pages whose structure varies.

Datificial work

• source-aware parsing
• section and table extraction
• schema-constrained field extraction
• source-span evidence
• normalization and entity linkage
• confidence, abstention, and review queues
• change-aware reprocessing

Structured records with document lineage, extraction method, evidence spans, and validation state.

• · queryable facts without losing source context
• · repeatable extraction
• · human review focused on uncertain cases
• · controlled model upgrades

07

Training and evaluation corpus

A model or agent program needs a clean, versioned corpus, but examples are duplicated, weakly labeled, contaminated, unbalanced, or disconnected from source provenance.

Datificial work

• source and rights inventory
• deduplication and contamination checks
• canonical example schema
• taxonomy and label normalization
• filtering and mixture design support
• train, validation, and test separation
• provenance and release manifests
• export to the training stack

A versioned corpus with stable examples, labels, provenance, quality checks, and split policy.

• · reproducible training inputs
• · documented contamination controls
• · auditable label provenance

Datificial prepares the corpus. Model post-training itself is separately scoped work.

08

Change and monitoring product

The team does not merely need the latest record. It needs to know what changed, when, why, and whether the change matters.

Datificial work

• source snapshots and temporal model
• field and relationship diffs
• entity continuity
• significance and suppression rules
• change classification
• release notes, feeds, and evidence
• false-alert evaluation

A versioned historical product with typed change events and monitored delivery.

• · changes tied to releases
• · fewer noisy alerts
• · evidence behind every event