Workflow

You have the names. You need the rest of the row

A list of 400 company names is not a pipeline. Somebody has to find the industry, the size, the location, the tech stack, and whether they are even worth a call. SheetAI researches each row on the live web, writes what it finds into the columns you name, and cites the page it came from, so the list you hand your reps is one they will actually work.

Try it on your sheet See real workflows

Audit-ready trailNo data trainingReversible per action

22.5%

Contact data decay per year

HubSpot estimates that email marketing databases degrade by about 22.5% a year, based on MarketingSherpa research putting B2B decay at 2.1% a month.

Source: HubSpot: Database Decay Simulation

76%

Say under half their CRM data is accurate

In Validity's survey of 602 CRM users, 76% said less than half of their organisation's CRM data is accurate and complete. 37% said they lose revenue as a direct result.

Source: Validity: The State of CRM Data Management in 2025

4.1 yrs

Median time with current employer (US)

US wage and salary workers had been with their current employer for a median of 4.1 years in January 2026. Each move leaves a contact record pointing at the wrong company.

Source: US Bureau of Labor Statistics: Employee Tenure Summary (2026)

Lead Enrichment: illustration for SheetAI's lead enrichment use case

Sales reps spend roughly 70% of their week not selling.

Salesforce's State of Sales research has repeatedly found that only about 30% of a rep's time goes to actual selling; the rest is administration, research, and data entry. Enrichment is the single largest research slice, and it is the one that looks most like a machine's job.

Source: Salesforce: State of Sales

This page is about filling in the columns you do not have. You start with something thin: a conference attendee list, a webinar signup export, a scrape of an association directory, a CRM segment where half the fields are blank, and you need it to become a list somebody can prioritise: industry, headcount band, headquarters, funding stage, relevant technology, a plausible reason to reach out. SheetAI does that research row by row on the live web, writes each finding into the column you specify with a source link, and marks the ones it could not confirm rather than filling them with something that reads well. It is not a contact database, it will not produce personal email addresses or phone numbers, and the boundaries section says exactly why.

The economics of a cold list

Four numbers that explain why enrichment eats the week it does, and why buying your way out of it is not always the answer.

Rep time spent on non-selling activity

~70%

Research, data entry, CRM hygiene, and internal admin. Enrichment is the largest automatable slice, the part that requires reading rather than judgement.

Salesforce State of Sales

B2B contact data going stale each year

~22.5%

Roughly one field in five is wrong within a year of collection. Enrichment is therefore not a one-off project but a maintenance task, which is precisely what makes manual effort so expensive.

HubSpot, widely cited

Organisations reporting inaccurate contact data

~30%

Experian's data quality research has repeatedly found businesses believe roughly a quarter to a third of their customer and prospect data is inaccurate, and act on it anyway.

Experian Data Quality

Annual cost of a mid-tier enrichment subscription

$15k+

Per-seat, per-credit pricing on the major data vendors adds up quickly, and the coverage gap on smaller, non-US, and non-tech companies is exactly where most lists live.

Published vendor pricing

The pattern: the expensive part is not the data itself; most of it is publicly on a company's own website. The expensive part is a human opening 400 tabs. Doing the research inside the sheet collapses that into a loop, and keeps the citation next to the cell so the answer is checkable.

Anatomy of an enrichment pass

What building a workable list from a raw export actually involves, before anything is automated.

Phase 1: Normalise what you already have

›Fix company names: trailing Ltd, Inc, duplicated whitespace
›Deduplicate the obvious repeats
›Split the one column that contains three fields

⚠ Every list arrives dirty, and enrichment against a dirty key means researching the same company three times under three spellings.

Phase 2: Find the company

›Search for the official website
›Discard the directory aggregators and the LinkedIn mirror
›Confirm it is the right entity, not a similarly named one

⚠ This is the step that takes the time and the step that is never budgeted. Company names are not unique and search results are mostly noise.

Phase 3: Read and record

›Industry, headcount band, headquarters, ownership
›Recent funding, acquisition, or leadership change
›Whatever qualifying signal matters to your motion

⚠ Four minutes of reading per row, and the finding lives in your short-term memory until you type it, which is where it gets subtly wrong.

Phase 4: Score and prioritise

›Apply the ICP criteria to each row
›Rank by fit and by timing signal
›Cut the bottom half so reps do not work it

⚠ Scoring is easy once the fields exist. It never gets done because phase 3 consumed the budget and the list ships unscored.

Phase 5: Hand it over and defend it

›Push to the CRM or hand to the reps
›Answer 'where did this come from?' for the disputed rows
›Re-run the whole thing next quarter

⚠ Without a source per field, the list is unfalsifiable, which means the first rep who finds one wrong row discounts the entire file.

Cautionary tale

Why the source link matters more than the field

In 2018 the Federal Trade Commission brought an action against a set of data brokers over lists sold to marketers that had been assembled and represented in ways the buyers could not verify; the recurring pattern across the FTC's data-broker work has been that purchased lists arrive with confident-looking fields and no way to check any of them. Buyers discovered the problem the way buyers always do, after the campaign, from the bounce rate and the complaints. The same failure mode appears whenever a field is separated from its provenance: the value survives, the uncertainty does not.

The lesson: An enriched cell without a source is a rumour with formatting. The practical defence is boring and effective: make every researched field carry the URL it came from, and make the tool say 'not found' out loud. SheetAI writes a citation column alongside the values and leaves unconfirmed fields blank and flagged, so a rep who challenges a row can settle it in one click rather than distrusting the file.

Source: FTC: Data Broker Enforcement and Research

Who it's for

If your pipeline starts as a spreadsheet with too few columns, this one is for you.

SDRs & BDRs

Given a raw list and a quota, and expected to research each account before the first touch.

Sales Operations

Maintain a CRM where a third of the account fields are empty and the rest are eighteen months old.

Founders Doing Their Own Sales

Have no data budget, no ops team, and a list of 300 companies that needs qualifying by Friday.

Demand Generation

Turn event and webinar exports into something segmentable before routing it to sales.

Partnerships & BD

Research long-tail targets that the major data vendors simply do not cover well.

Real enrichment workflows

The prompt, what the agent actually does, and what the sheet looks like at the end.

lead-enrichment_workbook.xlsx - SheetAI

You ask

“Column A is company names. For each row, research the company and fill in: official website (B), industry (C), employee count band (D), headquarters country (E), and a one-line description (F). Put the source URL in G. If you cannot confirm a field from a real page, leave it blank and write NOT FOUND in G.”

SheetAI does

✓Searches for each company and identifies the official site rather than a directory mirror.
✓Reads the site to extract industry, size band, and location.
✓Writes one row per company with the source URL alongside.
✓Leaves unconfirmed fields blank and marks the row NOT FOUND rather than guessing.

Result

312 of 400 rows enriched with a citation each, 88 marked NOT FOUND, mostly duplicates under alternate spellings and three companies that no longer trade. Roughly seven hours of tab-switching, done while the sheet was open.

Everything you need to make a thin list workable

Live web research per row, written into the columns you choose, with a citation beside every finding and an explicit not-found rather than a confident invention.

✓Live web search and page reading per row

✓Source URL written beside every field

✓Explicit NOT FOUND instead of plausible guesses

✓Fill-blanks-only mode for CRM re-imports

✓ICP scoring with written justifications

✓Title and function normalisation at scale

Plays well with your stack

CRM exports (Salesforce, HubSpot, Pipedrive)
Event and webinar attendee CSVs
LinkedIn Sales Navigator saved-list exports
Marketing automation contact exports
Company registry and directory downloads
Any CSV with a company or domain column

What enriching 400 accounts looks like

A representative outbound team preparing a quarter's target list, before and after the research step moves into the sheet.

Before SheetAI

~26 hours

Mon
Clean the export; deduplicate; fix name variants
~4h
Tue
Research the first 130 accounts by hand
~7h
Wed
Research the next 130; lose the thread twice
~7h
Thu
Finish the tail; chase the ambiguous company names
~5h
Fri
Score what got finished; ship the list unranked
~3h

✓

With SheetAI

~5 hours

Mon
Clean and dedupe in one prompt; enrichment runs on the full list
~1h
Tue
Review the NOT FOUND rows; correct the ambiguous keys
~2h
Wed
Score against ICP with written justifications; tier the list
~1h
Thu
Recent-news pass on Tier 1 only; hand to reps
~1h

~80% reduction in list-preparation hours on a representative 400-account quarterly build.

What SheetAI will not do

Enrichment is one field away from surveillance, so the line matters. Here is where it sits.

Find personal email addresses or phone numbers

SheetAI does not generate, guess, or pattern-match personal contact details, and it is not a contact database. Beyond the legal exposure under GDPR, PECR, and similar regimes, guessed addresses are the fastest way to destroy a sending domain's reputation. If you need verified contact data, license it from a vendor whose lawful basis and opt-out handling you can inspect.

Bypass a paywall, login, or robots.txt

It reads pages that are publicly available. It does not authenticate as you, work around access controls, or fetch what a site has asked automated clients not to fetch. If the information is behind a subscription you hold, the licence terms are between you and the publisher; read them before you point a tool at it.

Guarantee a field is current

It reports what the source said at the time it read it, which is why the citation and the date matter. Company data decays at roughly a fifth per year; a field from six months ago is a hypothesis, not a fact. Re-run rather than trust.

Replace consent and suppression checks

Knowing a company exists is not permission to contact anyone at it. Suppression lists, consent records, and regional marketing rules stay your responsibility, and no enrichment output should be treated as a lawful basis for outreach.

“

We were paying for a data tool that covered maybe half our market: everything US and tech, almost nothing in our actual segment. Now the research runs against the companies' own websites and the answer arrives with a link. My reps stopped arguing with the list, which I did not expect to be the main benefit.

Head of Sales Development, B2B services company (14 reps)

Frequently asked

The questions that come up before anyone points this at a real list.

Does this replace ZoomInfo or Clearbit?

›

Not for verified contact data; those vendors license, verify, and stand behind individual-level records, and SheetAI does not produce personal contact details at all. Where it does compete is firmographic and contextual research: industry, size, location, ownership, recent events, qualifying signals. That is the layer where the coverage gaps on smaller, non-US, and non-tech companies are widest, and where a live web read often beats a stale database record.

Will it find email addresses?

›

No, deliberately. It will not produce, guess, or pattern-match personal email addresses or phone numbers. Guessed addresses damage your sending reputation and carry real regulatory exposure under GDPR and similar regimes. A generic, published company address on a contact page is a different thing and you can ask for it, but that is publicly listed corporate contact information, not a person's details.

How accurate is the enrichment?

›

It is as accurate as the page it read, which is why the citation column is not optional. Company-published information about itself is generally reliable; inferred fields like employee band are usually a reasonable band rather than an exact figure. The behaviour that matters is what happens on failure: unconfirmed fields come back blank and marked NOT FOUND, so the error mode is a gap you can see rather than a plausible invention you cannot.

How many rows can it handle?

›

Hundreds in a pass is routine; thousands is a matter of running it in batches, which is also better practice because each batch stays reviewable. Research is genuinely slower than a formula; it is reading pages, not filling a series, so a 400-row enrichment is minutes rather than seconds.

Is this legal under GDPR?

›

Researching publicly available company information is ordinarily fine; the obligations attach to personal data and to what you do next. SheetAI stays on the corporate side of that line by not producing personal contact details. Your outreach, consent records, suppression lists, and lawful basis remain your responsibility, and nothing this tool outputs constitutes one.

Can it work from domains instead of company names?

›

Yes, and it is more reliable that way. Company names are ambiguous; three businesses share the name and the search picks the wrong one. A domain is a unique key. If you have both, give it both and ask it to confirm they match.

What happens when it cannot find a company?

›

It writes NOT FOUND rather than filling the row with something that reads plausibly. This is the single most important design decision in the workflow: a blank you can see is recoverable, a confident wrong industry classification is not, because nobody will ever check it.

Can I re-run it to refresh a list?

›

Yes, and you should, roughly a fifth of B2B fields go stale each year. The fill-blanks-only pattern is useful here too: re-run against the rows whose citation date is oldest rather than the whole file, and keep the existing values that a human has already verified.

Sources & further reading

Every benchmark and statistic on this page is drawn from publicly available research. We cite our sources because we read theirs.

Get started

Stop opening 400 tabs.
Start working the list.

Paste your company names into a sheet, name the columns you want filled, and get a researched list back with a source link on every finding.

All use cases

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