An AI agent can enrich Airtable records automatically: it reads new or incomplete records, looks up missing field values from external sources, writes the enriched data back into the correct fields, flags potential duplicates against existing rows, and tags records that need a human decision before the data is treated as final. Your team describes the target state; the agent does the lookup and fill work.
This post focuses specifically on enrichment, not on general data cleanup. Enrichment means adding net-new information that was never in the record: a company's industry, employee count, or headquarters city; a contact's job title or LinkedIn URL; a categorization label your team uses internally. The distinction matters because enrichment requires an external lookup, while cleanup only requires normalizing what is already there. Both are valuable; they just need different agents configured differently.
Key takeaways
- Record enrichment adds missing fields by looking up external data; it is distinct from data cleanup, which fixes existing values.
- An AI agent identifies records with empty target fields, performs the external lookup, and writes the values back without manual effort.
- Deduplication runs on each new record before enrichment so you do not double-enrich the same company or contact under two slightly different names.
- The agent tags every enriched field for human review. Source-of-truth decisions stay with the team.
- On Gravity, you describe the fields you want filled and the lookup logic; an expert-built agent runs the enrichment in about 60 seconds per batch.
What Record Enrichment Means in Practice
A typical Airtable enrichment scenario: your sales or operations team adds a new company to a CRM-style base. They fill in the company name and website. Everything else, the industry, company size, headquarters location, key contact names, primary use case for your product, is left blank because the person entering the record does not have time to look it all up. Over days and weeks those blanks accumulate, and the base becomes less useful for filtering, segmentation, or reporting.
An enrichment agent closes that gap. It monitors the base for records where target fields are empty, performs the external lookups, and writes the values back. The team member who entered the record gets a useful, complete entry without doing any additional work. The base stays clean and searchable as it grows.
Enrichment versus cleanup: the practical difference
Cleanup agents normalize what is already in the base: fixing "new york" to "New York," stripping extra spaces, correcting abbreviations, or standardizing phone number formats. Enrichment agents add what was never there in the first place. A cleanup pass on a record with a blank Industry field does nothing because there is nothing to fix. An enrichment pass queries an external source and fills it. The Airtable data entry cleanup agent covers the normalization side; this guide covers the enrichment side.
Filling Missing Fields from External Sources
The enrichment agent's core loop is: find records with empty target fields, determine what lookup is needed to fill each field, perform the lookup, write the result back, and mark the field as enriched. The lookup step is where the external data comes in.
Company enrichment
For a company record, the most commonly requested enrichment fields are industry vertical, employee count range, headquarters location, founding year, and a brief description of what the company does. The agent can pull these from public company information sources, domain lookup tools, or APIs you authorize. Given a company name and website domain, most of these fields can be filled reliably without any manual research.
A practical example: you run a B2B outreach base in Airtable. Each week your team adds fifty new target companies from a list. Without enrichment, each entry is a name and a URL. With an enrichment agent, by the next morning each record also has the industry, company size bucket, and headquarters city. Your team can filter by "Series B SaaS companies in North America under 200 employees" immediately, rather than manually researching each account before deciding whether to prioritize it.
Contact enrichment
For contact records the target fields are typically job title, department, LinkedIn profile URL, and direct contact information. The agent starts with whatever identifying information is already on the record (name, company, work email) and uses it to locate the matching profile or record in connected sources. If a job title is stale or missing, the agent fills it and flags it for review. If a LinkedIn URL is not present, the agent populates one where it can be found confidently.
Contact enrichment matters most for bases that feed outreach workflows. An enriched contact record means the sales rep or recruiter who opens it has the context they need to send a relevant, personalized message rather than a generic one. The enrichment happens before the human touches the record, not while they are looking at it under pressure.
Internal categorization fields
Some enrichment fields are not drawn from external databases but from internal logic: assigning a record to the right tier, owner, or use-case category based on values already on the record. A company with 500 to 2,000 employees and an industry of "Financial Services" might automatically receive a tier-1 designation. A contact with the title "Engineering Manager" might be routed to a technical sales track. The agent applies this categorization consistently across every new record rather than relying on whoever entered the data to remember the rules.
Standardizing New Records on Intake
Enrichment and standardization often run together. Before filling missing fields, the agent normalizes the data that is already there: company names get a canonical form (removing "Inc.", "LLC", "Ltd" suffixes so deduplication works reliably), country and region fields get consistent abbreviations, and phone numbers get a standardized format. This preprocessing step prevents enrichment from creating inconsistencies on top of inconsistencies.
Canonical name resolution
Company name variations are the most common source of duplicate records. "Acme Corp", "Acme Corporation", and "ACME Corp." might be three records for the same company if nobody normalizes on intake. The agent applies a canonical form to the company name field before running deduplication, so all three variants match to the same existing record rather than creating three entries that will need to be merged later.
Format normalization for key fields
Website domains get stripped to their root form (removing "www." and trailing slashes) so they can serve as reliable lookup keys. Email addresses get lowercased. State and country names get mapped to a consistent code or full-name format depending on what your base uses for filtering. These small normalizations are invisible to the end user but critical for making the base queryable and reliable over time.
Deduplication on Intake
Enrichment without deduplication creates a more detailed mess. If the same company is in your base twice under slightly different names, enriching both records does not help: you now have two enriched duplicates instead of one complete record. The deduplication step runs before enrichment so you catch the duplicate before it gets embedded further into the base.
How the agent identifies likely duplicates
The agent uses a combination of field values to detect potential duplicates: company name similarity (after canonical normalization), website domain match, and, for contacts, email address or name-plus-company combination. When a new record matches an existing one above a confidence threshold, the agent tags the new record as a likely duplicate and links it to the existing record, rather than merging silently or ignoring the match.
Fuzzy matching handles the cases that exact matching misses: "Stripe Inc" versus "Stripe", or a contact entered as "J. Smith" versus "John Smith" at the same company. The agent flags the match for human review rather than making the merge decision itself, because merging records is a destructive action that should require explicit confirmation.
The review queue for potential duplicates
Flagged potential duplicates appear in a dedicated view or with a specific tag in the base. The human reviewer sees both the new record and the existing one side by side and decides: merge them, keep them as separate records (because they are actually different entities), or discard the new entry. This review step takes seconds per record once the agent has surfaced the match. Without the agent, the duplicate would likely go unnoticed until someone noticed the base had two entries for the same company during a manual audit.
Tagging Records for Human Review
The agent never treats its own enrichment output as final without a human check. Every field it fills gets a corresponding tag or status value that indicates the value came from an external lookup rather than direct human entry. A dedicated "Enrichment Status" field on each record shows whether enrichment is pending, complete and awaiting review, or reviewed and accepted.
Why the review layer matters
External data sources are not always accurate or current. A company's industry classification might be outdated. A contact's job title might have changed since the last time a public source was updated. The agent fills the field with the best available information and marks it for review; the human confirms or corrects it. Once confirmed, the field is marked as human-verified and the agent will not overwrite it on future enrichment runs.
This design keeps the agent in its correct role: reducing manual lookup work, not making authoritative decisions about your data. The team's judgment remains the source of truth. The agent's output is a well-informed starting point, not a concluded answer.
Batch review workflow
Rather than reviewing each record individually as it is enriched, most teams configure a weekly or daily review session. The agent enriches new records throughout the week; a team member opens the "Pending Review" filtered view once or twice a week and steps through the flagged fields. For a well-configured enrichment agent on a typical intake volume, this review session takes far less time than manually looking up the same information would have taken in the first place.
Categorization and Auto-Classification
Beyond filling specific lookup fields, the agent can apply categorical labels that your team uses for segmentation, routing, or prioritization. These labels may not exist in any external database; they are internal classifications your team has defined. The agent derives them from the combination of fields already on the record, including those it just enriched.
Use-case and persona tagging
A SaaS company might classify accounts by buyer persona: technical buyer, business buyer, or executive sponsor. The persona label is not in any external database, but it can be inferred from the contact's job title and department. After enriching the job title field, the agent applies the persona tag based on rules your team defines. A "Head of Engineering" contact gets tagged as a technical buyer. A "CFO" gets tagged as an executive sponsor. The tag appears on the record so whoever handles the account knows which communication track to use.
Priority and tier assignment
Similarly, account tier or lead priority can be assigned automatically based on enriched fields. A company with a certain employee count range in a high-value industry vertical gets assigned to tier 1 and routed to a senior account executive. A smaller company outside the primary verticals gets assigned to tier 3 and handled through a self-serve track. The logic lives in the agent's configuration; the agent applies it consistently to every new record without requiring a manual scoring step.
This kind of rule-based classification is closely related to what a lead scoring agent does inside a CRM. The difference is that here the base of record is Airtable rather than a dedicated CRM, and the classification is applied at intake rather than as a separate scoring pass. For teams using Airtable as a lightweight CRM, the enrichment and classification steps together replace the manual research and prioritization that would otherwise happen before a record reaches a human.
How Gravity Handles This
On Gravity you describe the enrichment target: which base, which table, which fields should be filled, what external sources the agent should use, and what the review workflow looks like. Something like "for every new record in my Contacts table where Industry and Company Size are blank, look up those fields from the company domain, write them back, and tag the record as Pending Review" is enough to configure the run.
An expert-built agent handles the Airtable API interactions, the external lookups, the field writes, and the tagging. You pay per run rather than per record or per seat. A batch enrichment run over a hundred new records costs the same structure as a run over ten: a small number of credits for the work the agent actually does. If the batch is large, the per-record cost is lower than any manual research alternative; if it is small, you are not overpaying for an idle subscription.
For a broader introduction to how agents handle structured data tasks, see our guide on what an AI agent is.
What Stays with the Human
The enrichment agent handles the mechanical work: identifying gaps, performing lookups, writing values, flagging duplicates. Several things remain with the human by design.
First, the decision about which external sources to trust. The agent uses whatever you authorize; you decide which data sources are acceptable for your use case. Second, the merge decision for potential duplicates. The agent flags likely matches; the human confirms or rejects the merge. Third, any source-of-truth corrections. If the agent fills an industry field as "Technology" and your team classifies that company as "FinTech" based on their product, the human corrects it and marks it as reviewed. The correction sticks; the agent will not overwrite it.
This division of labor is the point of the review tagging system. The agent compresses the time spent on lookup and fill from hours to minutes. The human's contribution shifts from tedious research to fast, high-quality review and correction. The result is a more complete base with less effort and more reliable data than either the agent or the human could produce alone.
Getting Started
Start with one table and one category of missing fields. Do not configure enrichment for every field at once. The most common starting point is company-level data on a contacts or accounts table: industry, company size, and headquarters location. These three fields cover most segmentation use cases and can typically be filled from reliable public sources.
Define your target fields and acceptance criteria
Before setting up the agent, list the fields you want enriched, what a correct value looks like for each, and which fields require human confirmation before they are used in reporting or outreach. For some fields (like a company's public description) you may accept the agent's output without review. For others (like a contact's direct email) you will want a confirmation step. Being explicit about this upfront prevents confusion about what the agent is and is not allowed to treat as final.
Run a test batch on existing records
Pick twenty or thirty existing records with known correct values and let the agent enrich them. Compare the agent's output against what you know to be true. Check the accuracy on each target field. Where the agent is consistently right, you can reduce the review frequency. Where it is sometimes wrong or where the data source is inconsistent, keep the mandatory review tag in place. This calibration step takes a short session and tells you exactly how much to trust the agent's output before applying it to new records at scale.
Connect to additional workflows
Once enrichment is running reliably on your main table, you can connect its output to adjacent workflows. Enriched records with a tier-1 designation can trigger a notification to a sales rep. Enriched contacts with a verified email can trigger an outreach sequence. Records that fail deduplication and get flagged as duplicates can go into a review queue that someone handles weekly.
For teams that also use Google Sheets alongside Airtable, the Google Sheets data validation agent handles a similar enrichment-adjacent problem on the Sheets side. For Notion-based workflows, the Notion project tracking agent can consume the enriched Airtable data as part of a broader operations system. For an overview of how agents handle multi-step data tasks, see our post on how to set up your first AI agent.
Frequently Asked Questions
What does an AI agent for Airtable record enrichment actually do?
It reads records with empty or incomplete fields, looks up the missing information from external sources (company databases, public registries, your other connected tools), writes the values back into the correct Airtable fields, and tags the record so a human can review the additions before treating them as final. The human retains control of every source-of-truth decision; the agent handles the lookup and fill work.
How is record enrichment different from data cleanup in Airtable?
Data cleanup corrects existing values: fixing typos, standardizing capitalization, removing duplicates. Record enrichment adds net-new information that was never in the record in the first place, such as a company's industry vertical, employee count, or contact's LinkedIn URL. An enrichment agent fills the gaps; a cleanup agent fixes what is already there. Both are valuable, but they solve different problems.
Can an AI agent deduplicate Airtable records on intake?
Yes. On each new record the agent checks the base for existing records with matching or similar values on your chosen key fields, such as company name and domain, or contact name and email. If a likely duplicate is found, the agent tags the new record for human review rather than merging silently. This prevents the most common intake problem: the same company or contact entered twice under slightly different names.
What external data sources can the agent use to fill missing fields?
The agent can draw from any source it has access to: public company information registries, domain lookup tools, your CRM, connected spreadsheets, or APIs you authorize. For contact enrichment it might use LinkedIn data or a business directory. For company enrichment it might use a company data API or your own internal knowledge base. The specific sources depend on what you connect; the agent handles the lookup and field-writing logic.
Does the AI agent overwrite existing Airtable field values?
By default the agent only writes to empty fields or fields explicitly marked as enrichment targets. It does not overwrite values a human has already entered. You can configure it to flag conflicting values for review rather than replacing them, so the human decides which version to keep. Source-of-truth decisions stay with the team, not the agent.