Intent data tells you that a company, and sometimes a specific person, has been researching a topic related to what you sell. Intent intelligence is what you get when that raw fact is resolved to an account you can name, checked against your ideal customer profile, corroborated with what else is happening at the company, mapped to the people involved, and delivered to a rep with a reason to act this week. Data infrastructure for intent intelligence is the machinery that performs that transformation every day, because intent that sits in a dashboard for a fortnight is intent that has already been acted on by someone else.
The term is having a moment because intent sources have matured from a single account-level score into topic-level, person-level and time-stamped feeds from licensed sources, and because the teams that bought raw intent learned the hard way that a list of surging accounts is a research assignment rather than a pipeline. The gap between intent data and intent intelligence is the resolution, fit, corroboration and delivery work in between, which is exactly the kind of multi-step, judgment-laden loop autonomous AI agents are built to carry. The product page for this job is Data Infrastructure for Intent Intelligence; the foundations are in the buying intent data guide.
What data infrastructure for intent intelligence means
Intent arrives as events: a topic, an account identifier, a strength, a date, and sometimes a person. On its own an event says that someone at a company read about something. Infrastructure for intent intelligence answers the questions a rep would ask before acting. Which company, exactly, resolved to a canonical domain rather than an inferred name? Do we want them, evaluated against the ICP on filled fields? Is anything else happening there that makes this a buying cycle rather than a curiosity? Who at the account is involved, with a verified way to reach them? And what should the first message say, given what they were reading?
The infrastructure also handles time. Intent decays in days, so the loop has to run daily, the delivery has to reach the rep the same day, and the record of what was surfaced and what happened next has to be kept so the topics and thresholds can be tuned. The buying intent and business signals guide covers the signal landscape; this guide covers the pipeline that turns it into action.
The data jobs inside intent intelligence
Identify. Define the topic set - your category, competitor names, adjacent problems, the specific pains you solve - and pull the accounts and people surging on those topics from licensed sources daily, with strength and recency. Enrich. Resolve every surging account to a canonical company record, fill the firmographic and technographic fields the ICP needs, attach the corroborating signals - open roles, funding, technology change, news - and find the buying committee, prioritizing anyone who appears in the person-level intent. Verify. Verify the contacts, confirm roles are current, and remove customers, open opportunities and competitors, whose research is interesting but is not pipeline. Score. Combine fit, intent strength and recency, and the corroboration count into a single priority, and label the reason so the rep sees "surging on migration topics, hiring a platform lead, tier one fit" rather than a number. Deliver. Land the ranked accounts in the CRM with the intent topics and dates stored as fields, load the mapped people into the sequencer with a draft grounded in the topic, and post the daily digest where the team talks.
The data layers and the fields that make intent actionable
| Layer | Fields that matter | Role in the loop |
|---|---|---|
| Company data | Canonical domain and ID, industry, employee count, revenue band, HQ, technologies in use, funding stage, parent and subsidiaries, ICP fit tier and reason | Resolution and fit; deciding whether the surge is worth a rep |
| Person data | People at the account by department and seniority, person-level intent flag, title, tenure, verified email and phone, committee role | Who is researching and who to reach |
| Signals | Intent topics, strength, recency and trend; corroborating hiring, funding, technology and news events with dates; competitor-topic flags | The surge itself and the evidence that it is a buying cycle |
| Verification | Email and phone status with dates, role-current check, customer, opportunity and competitor flags, duplicate flag | Reachable people at accounts that are actually prospects |
| Delivery | Priority and reason, intent topics and dates as CRM fields, sequencer batch ID, digest posted, first-touch date, outcome for attribution | Action inside the window and the record that tunes the loop |
The trend field earns its place. A topic that surged once may be one person's afternoon; a topic rising over ten days across several people at the account is a project. Storing the trend alongside the strength lets the priority favor sustained interest, and storing the outcome of each surfaced account lets the thresholds learn.
Autonomous agents versus working intent by hand
By hand, intent is a weekly export from the intent dashboard, pasted into a spreadsheet, matched to the CRM by company name with mixed success, glanced at by a manager, and forwarded to reps who do not know what to say to an account that read about a topic. By the time the first email goes out the surge is ten days old. An autonomous AI agent runs the loop daily: resolve, fit, corroborate, map the people, verify, draft, deliver, and report what was surfaced and what came of it.
| Step | By hand | Run by an autonomous agent |
|---|---|---|
| Cadence | Weekly export, read days later | Daily pull; same-day delivery to the rep |
| Resolution | Company-name matching in a spreadsheet; subsidiaries and renames lost | Resolved to canonical domains and IDs; hierarchy known |
| Fit and corroboration | Skipped; every surge forwarded | ICP evaluated on filled fields; hiring, funding and technology signals attached; priority with reason |
| People | Rep finds someone later, if the account is worked at all | Buying committee mapped, person-level intent prioritized, contacts verified |
| First touch | Generic template that never mentions the topic | Draft grounded in the topic and the corroborating signal, loaded for review |
| Learning | None; the spreadsheet is overwritten next week | Topics, dates and outcomes stored; thresholds and topic set tuned on results |
The rep still decides whether the account is worth a call and holds the conversation. The agent removes the days between the surge and the decision, and it adds the context that makes the decision easy: fit, corroboration, the people, and what they were reading.
Intent intelligence also improves the rest of the infrastructure. Accounts that surge repeatedly without ever fitting are a reason to revisit the ICP; topics that surge at customers are early churn or expansion signals for the account team; competitor-topic surges at existing customers deserve a different owner entirely. An agent that resolves and routes intent daily can send each of those to the right place, which is a second return on the same feed.
The metrics that show it is working
The figures below are illustrative examples for a mid-market B2B team running a daily intent loop on a curated topic set.
92%of surging accounts resolved to a canonical company record (example)34%of resolved surges inside the ICP and corroborated by a second signal (example)1.3 daysmedian time from surge to first touch loaded (example)2.9xopportunity rate on intent-sourced accounts versus the flat list (example)
Resolution rate is the plumbing number; anything unresolved is invisible to the rest of the loop. In-ICP, corroborated share tells you whether the topic set is precise and whether the market is actually in motion. Surge-to-touch time is the number that intent lives or dies on. Opportunity rate on intent-sourced accounts is the outcome, and splitting it by topic is how the topic set gets tuned each quarter.
How AstroFabric does it
AstroFabric runs intent intelligence as a daily scheduled mission. buyer_intent_topics holds the topic set and shows what is trending; buyer_intent_companies returns the accounts surging on it with strength and recency; buyer_intent_contacts returns the individuals involved; and company_buying_intents gives the full intent picture for any single account. Resolution runs through company_to_domain and company_lookup, fit through list_score on fields filled by list_enrich and tech_stack, and corroboration through hiring_signals, funding_events and company_news, with signals_feed and watch_companies keeping the surging accounts under standing watch.
People come from buying_committee, with find_email, find_phone and email_verify making each reachable, and list_hygiene removes customers, opportunities and competitors while routing customer surges to the account team. outbound_draft writes the first touch from the topic and the corroborating signal, crm_upsert_contacts stores the intent topics, dates and priority on the account, list_push loads the mapped people into the sequencer, and audience_push builds an in-market audience on connected ad accounts, every write parked for one approval. create_schedule runs it daily. Plans start at $49 per month, a verified contact is a few credits, and finder misses are free. The landing page for this job is Data Infrastructure for Intent Intelligence.
Frequently asked questions
What is data infrastructure for intent intelligence?
The daily loop that turns raw intent events into accounts a rep can act on: resolution to a canonical company, ICP fit on filled fields, corroboration with hiring, funding and technology signals, the people involved with verified reach, a grounded first touch, and delivery into the CRM and sequencer inside the window.
What is the difference between intent data and intent intelligence?
Intent data is the event: a topic, an account, a strength, a date. Intelligence is that event resolved, qualified, corroborated, mapped to people and delivered with a reason to act. Teams that stop at the data get a research assignment; teams with the infrastructure get a ranked daily batch.
How should the topic set be chosen?
Include category terms for buyers who know the category, competitor names for switchers, and adjacent problems and specific pains for buyers who do not yet know a category exists. Review quarterly which topics produced opportunities and retire the ones that produced noise; the outcome field on each surfaced account makes that review possible.
Why does corroboration matter?
A single surge can be one person reading an article. A surge alongside an open role in the relevant function, a funding round or a technology change is far more likely to be a buying cycle. The priority weights corroborated surges higher, and the reason on the row tells the rep what was found.
How quickly does intent need to be acted on?
Within days. Intent decays fast and competitors see the same feeds, so the loop runs daily, resolution and enrichment happen the same day, and the first touch is loaded for review within a day or two of the surge. Median surge-to-touch time is the metric that keeps the loop honest.
Sources
- Gartner - The B2B buying journey (how much of the buying cycle happens in independent research)
- U.S. Bureau of Labor Statistics - Job Openings and Labor Turnover Survey (hiring as a corroborating signal)
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