Company enrichment
Resolve company names, write concise profiles, classify industries, normalize locations and create semantic tags.
Read company guideEnrichment types and controls
AI Data Enrichment Workflows for Every Record
Use the Enrich API to turn partial records and unstructured text into consistent fields, summaries, classifications and semantic metadata.
Company dataDocumentsCRM recordsSupport tickets
Record types
Choose a focused enrichment workflow
Start with a narrow record type and a schema that maps directly to a business decision or application feature.
Person enrichment
Structure a professional profile from roles, organizations, experience, skills and public record context.
View fieldsWebsite enrichment
Identify a site’s purpose, organization, topics, audience, language and important pages.
View fieldsDocument enrichment
Classify document type, detect entities, extract clauses and produce schema-bound summaries.
View fieldsProduct enrichment
Normalize product names, features, categories, use cases, compatibility terms and descriptive attributes.
View fieldsSupport enrichment
Detect intent, topic, urgency, sentiment, product area and recommended routing from ticket text.
View fieldsField design
Ask for fields that have a clear downstream use
A good enrichment field is specific, testable and connected to an action. Avoid broad requests such as “tell me everything.”
| Record | Useful fields | Validation approach | Example use |
|---|---|---|---|
| Company | Canonical name, industry, profile, location, keywords | Required strings, controlled taxonomy, source notes | CRM segmentation and account research |
| Person | Role, organization, seniority, expertise, profile summary | Nullable fields, role taxonomy, confidence per field | Contact routing and research context |
| Website | Site type, topics, audience, language, entities | URL normalization, enum values, evidence snippets | Content discovery and lead qualification |
| Document | Document type, parties, topics, dates, structured summary | Page or section references, required arrays, null policy | Knowledge base and review queues |
| Product | Category, features, use cases, compatibility, tags | Category taxonomy, unit normalization, deduplication | Catalog search and comparison |
| Support ticket | Intent, topic, priority, sentiment, routing hint | Priority enum, confidence threshold, human override | Triage and agent-assist workflows |
Quality controls
Separate enrichment from unsupported invention
The pipeline should be allowed to return null, unknown or needs_review. A blank field is safer than confident fabrication.
Schema constraints
Define types, required fields, allowed values, maximum lengths and nested object rules before execution.
Evidence handling
Store source references or evidence snippets when a field depends on retrieved or supplied material.
Null policy
Tell the pipeline when to return null instead of guessing. Treat unknown as a valid outcome.
Field confidence
Score uncertain fields independently so one weak attribute does not invalidate an otherwise useful record.
Human review
Route high-impact or low-confidence results into a review queue rather than auto-publishing them.
Evaluation sets
Maintain representative records with expected outputs and test every prompt, model or schema change.
Workflow composition
Build an enrichment pipeline from small, observable steps
Each step can use deterministic code, retrieval, a model or a validation rule. The best pipelines do not use an LLM for every transformation.
1. Normalize before interpretation
Trim whitespace, detect language, standardize URLs and map known aliases first. These deterministic steps reduce ambiguity and lower the amount of work sent to a model.
2. Extract before summarizing
Identify entities and key facts before asking for a summary. Structured facts are easier to validate, and the summary can be generated from the accepted fields.
3. Validate before storage
Do not write directly to a CRM, database or agent memory. Validate the object, attach the schema version and retain enough metadata to audit the transformation.
Example composition
Input: a support ticket and account tier.
Deterministic step: normalize error codes.
Model step: classify intent and sentiment.
Rule step: raise priority for enterprise authentication failures.
Output: a validated triage object.
Measurement
Evaluate field-level quality instead of relying on one score
Enrichment quality varies by field, record type and source. Build a dashboard that makes those differences visible.
AccuracyHow often a populated field is correct.
CoverageHow often the pipeline can return a useful value.
ValidityHow often the output matches the required schema.
Review rateHow often a result needs human intervention.
AI data enrichment FAQ
Plan a useful and measurable enrichment workflow
These answers cover the most important design choices before implementation.
What is AI data enrichment?
AI data enrichment uses models and deterministic transforms to add fields, labels, summaries or relationships to an existing record. The result should be validated before downstream use.
Which records are good candidates for enrichment?
Incomplete CRM rows, company names, professional profiles, websites, documents, product records, support tickets and research notes are common candidates.
How do I prevent the model from inventing fields?
Use narrow field definitions, source-aware prompts, null values for unknown facts, explicit confidence rules and schema validation. Do not force a value when the evidence is weak.
Should every field use an LLM?
No. Normalize dates, identifiers, casing and deterministic mappings without a model. Use a language model where interpretation, classification or summarization adds value.
How should enrichment quality be measured?
Create a labeled evaluation set and score field accuracy, coverage, invalid output, unsupported claims, latency and cost. Review quality by record type and field rather than one aggregate score.
Create a schema for one high-value record
Choose a record your team already handles manually. Define the desired fields, accepted unknowns and review rules before selecting a model.