Lead enrichment is the process of adding structured context to a contact you have already identified — job title, company size, technologies in use, growth signals, buying intent — before you send the first message. Inside Chattie, that process draws from two distinct quotas: AI credits (for AI-generated messages and ICP Filter verdicts) and Deep Leads (for LinkedIn searches and profile enrichment). Knowing which action consumes which quota prevents waste and ensures you deploy the right volume in the right place.
Executive summary:
- AI credits are consumed by AI-generated messages and ICP Filter verdicts; manual actions do not consume them.
- Deep Leads are a separate quota used by AI People Search, list enrichment, and LinkedIn profile lookup.
- Enrichment built on incomplete data produces superficial personalization — the problem is data quality, not volume.
- Chattie combines both quotas to qualify and prospect within fixed daily safety caps, without a Chrome extension.
Why lead enrichment matters for B2B LinkedIn prospecting
Lead enrichment crosses an initial data point — a name, company, or LinkedIn URL — with external sources to complete the contact record with information relevant to qualification. The practical outcome is knowing, before the first touchpoint, whether the lead holds the right title, whether the company fits the size you serve, and whether there are signals that suggest purchase intent.
The distinction between enrichment and basic data collection is context. Collection is capturing a name and email from a form submission. Enrichment is discovering that the same person manages a team of 18, that the company grew headcount significantly over the past year, and that they still run a point solution your platform replaces. Without that context, the outreach message is generic. With it, you can write something that speaks to that specific profile's situation.
For B2B founders and consultants prospecting on LinkedIn, the absence of enrichment shows up as low reply rates despite high send volumes. The bottleneck is not the number of leads — it is that the data available at send time does not support real personalization. According to McKinsey's research on generative AI in B2B sales, AI-assisted personalization at scale is one of the most significant levers for improving outbound conversion, precisely because it closes the gap between high-volume sending and contextually relevant messaging.
The implication for LinkedIn outreach is direct: enrichment is the prerequisite for personalization, and personalization is what separates a reply from an ignored request.
AI credits vs. Deep Leads: what each quota does
These are two quotas with separate functions. Treating them as interchangeable is the most common mistake new Chattie users make.
AI credits are consumed in two situations:
- 1 credit per AI-generated message — each time Chattie's AI writes or suggests a message within an active conversation.
- 1 credit per ICP Filter verdict — each time the AI analyzes a lead and decides whether they match your ideal customer profile.
Actions that do not consume AI credits: manually typed messages, connection invitations, profile visits, and withdrawal of pending invitations. If you approve and send a message that the AI wrote, the credit was consumed when the AI generated it — not again at the moment of sending.
Deep Leads are a separate monthly quota consumed in three situations:
- AI People Search — you describe the audience in natural language and Chattie's AI finds the leads. Each lead delivered costs 1 Deep Lead, plus 10 AI credits per search initiated.
- List enrichment — you upload a list with partial data (names, emails, company names) and Chattie fills in the missing LinkedIn profile information.
- LinkedIn profile lookup — locating a specific profile from partial identifiers.
The two quotas do not overlap. A message generated for a lead who arrived via AI People Search consumes 1 AI credit (for the message) and already consumed 1 Deep Lead (when the lead was delivered). They are charged at different stages of the workflow.
For a deeper look at how the three sending modes — Autopilot, Semi-pilot, and Copilot — interact with AI credit consumption, see Autopilot, Semi-pilot and Copilot: The 3 AI SDR Modes on LinkedIn (2026).
How AI People Search works — and what it costs per lead
AI People Search is a beta feature that changes the starting point of the prospecting workflow. Instead of building a search manually in LinkedIn or Sales Navigator and exporting a list, you describe the audience you want in plain language — "VP of Operations at logistics companies with 50 to 500 employees in the DACH region" — and Chattie's AI returns a list of matching profiles.
The key operational detail: AI People Search does not use your LinkedIn account to run the search. It operates on Chattie's own infrastructure, which means it does not touch your daily caps for profile visits, connection requests, or messages. Your LinkedIn account's safety limits remain intact.
The cost per use:
- 10 AI credits per search initiated (regardless of how many leads it returns).
- 1 Deep Lead per lead delivered by the search.
If a search returns 40 leads, you spend 10 AI credits plus 40 Deep Leads. That quota consumption happens before you send a single message. The AI credits for messages come later, as the AI generates outreach for each lead in the campaign.
This matters for quota planning. On the Solo plan, you have 500 Deep Leads per month. If you run five AI People Search campaigns that return 100 leads each, you exhaust the Deep Leads quota before writing a single message. The right sequencing is to define your target segment precisely before running the search, so each lead returned is worth the Deep Lead it costs.
What list enrichment does and when to use it
List enrichment addresses a different starting point: you already have a list — from a CRM export, a purchased database, a conference attendee roster, or a Sales Navigator export — but the data is incomplete. Fields are missing: some contacts have LinkedIn URLs but no job title; others have company names but no headcount; many have emails but nothing else.
Chattie's list enrichment fills those gaps by locating the corresponding LinkedIn profile and pulling available structured data into the record. Each profile successfully located and enriched consumes 1 Deep Lead.
When list enrichment is worth the quota cost:
- The list has genuine targeting value (you know why these companies are on it) but is missing data that blocks ICP qualification.
- The ICP Filter will be used to qualify the enriched list — which means the AI credit cost of filtering is only worthwhile if the input data is complete enough for the filter to make a meaningful decision.
- The list is large enough that manual enrichment one profile at a time would take longer than the campaign cycle.
When it is not worth the cost: enriching a list that was not sourced with any targeting logic. If the original list is random, enriching it produces a detailed record of the wrong people. The problem is upstream, not in the enrichment step.
For a practical breakdown of how to build lists that are worth enriching, B2B List Building and Data Enrichment: 7 Tips for Smarter Prospecting (2026) covers the sequencing in detail.
How the ICP Filter uses AI credits for qualification
After leads are sourced — whether through AI People Search, list import, or standard LinkedIn search — the ICP Filter is the qualification layer. You define your ideal customer profile: target industries, company size ranges, seniority levels, geographies, and any exclusion criteria. The AI then evaluates each lead against that definition and returns a verdict: match, partial match, or disqualified.
Each verdict consumes 1 AI credit. If you run the ICP Filter across 300 leads, you spend 300 AI credits before any outreach begins.
The practical implication: use the ICP Filter on lists that are already reasonably targeted. Running it on a list with no prior filtering to discover that most leads are disqualified is an expensive way to do what better upstream sourcing would have prevented. The filter is most efficient when it is confirming borderline cases and catching the exceptions in a list you have already pre-qualified by other means.
The ICP Filter output also feeds message personalization. When the AI knows a lead matched on specific criteria — say, company size and technology stack — it can reference those criteria in the generated message. That connection between qualification signal and message content is where enrichment data has its highest return on quota cost.
Chattie's daily caps and how enrichment fits within them
Chattie enforces fixed daily limits by default on every LinkedIn account connected to the platform:
- 25 connection invitations per day (up to 200 per week)
- 40 conversations per day
- 200 actions per day
- 80 profile visits per day
For accounts connected via Sales Navigator: 40 invitations, 60 messages, and 100 visits per day.
These caps apply to prospecting actions — sending messages, visiting profiles, withdrawing pending invitations. They do not apply to enrichment operations. AI People Search runs on Chattie's own infrastructure and does not count against your LinkedIn account's daily limits. List enrichment and profile lookup reference LinkedIn data without simulating account actions.
This separation matters operationally. You can run a large AI People Search or enrich a 500-row list on the same day that your account sends its full daily volume of outreach messages. The two workflows do not compete for the same resource.
Accounts go through a 15-day warm-up period when first connected. During warm-up, outreach volume is ramped gradually. Enrichment operations are not affected by warm-up status — you can build and qualify your lead list during the warm-up window so the account is ready to prospect as soon as it reaches full capacity.
The platform does not use a Chrome extension. All activity runs through Chattie's cloud infrastructure, which keeps the browser-based behavioral fingerprint that LinkedIn monitors clean.
The three sending modes and how they interact with enriched leads
Once leads are sourced, enriched, and qualified, they enter a campaign that runs in one of three sending modes. The mode determines how much human review happens before a message goes out:
- Autopilot — the AI sends messages autonomously, without requiring approval at each step. The scheduling link goes into the conversation when the AI determines the timing is right.
- Semi-pilot — the AI sends messages until a lead replies. After the first reply, a human reviews and approves each subsequent response before it is sent. The scheduling link goes out with that human approval.
- Copilot — the AI drafts every message, but a human approves it before it is sent at every stage of the conversation. The scheduling link is included in an approved message.
Invitations, campaign execution, and withdrawal of pending invitations after 15 days are automated in all three modes.
Enrichment data influences message quality across all three modes, but its effect is most visible in Autopilot, where the AI relies entirely on the data it has to write relevant outreach without human editing. Richer input — seniority confirmed, company size verified, technology stack known — produces more specific first messages. Thin data produces generic ones regardless of which mode runs the campaign.
Choosing between modes is partly a question of how much your team needs to control the conversation after the first reply. For founders prospecting solo with a small, well-defined ICP, Autopilot on a list of well-enriched leads can run with minimal oversight. For higher-value accounts where the sales cycle is longer and the conversation needs nuance, Semi-pilot or Copilot keeps a human in the loop at the moments that matter most.
For more on how to qualify the leads that enter these campaigns, How to Qualify LinkedIn Leads with AI: Signals, Tiers, and Timing covers the decision logic in detail.
Quota planning by plan: how to allocate AI credits and Deep Leads
The three Chattie plans carry different quota allocations. Understanding what each supports in practice helps you match the plan to the actual prospecting workflow.
Solo — US$ 75/month
- 1 LinkedIn account included (up to 2 with extra seats)
- 3,000 AI credits per month
- 2,500 leads per month: 2,000 standard + 500 Deep Leads
A solo founder running one LinkedIn account can initiate several AI People Search runs per month within the 500 Deep Lead cap, depending on how many leads each search returns. At an average of 50 leads per search, that is roughly 10 searches per month before Deep Leads are exhausted. The 3,000 AI credits support both ICP Filter verdicts and message generation across those leads — if 500 leads each receive one AI-generated message and one ICP verdict, that is 1,000 credits consumed on two operations, leaving 2,000 for follow-up messages in active conversations.
Growth — US$ 190/month
- 3 LinkedIn accounts included (up to 10 with extra seats)
- 10,000 AI credits per month
- 7,500 leads per month: 6,000 standard + 1,500 Deep Leads
Growth covers small teams running multiple accounts simultaneously. The 1,500 Deep Leads support larger AI People Search campaigns or list enrichment across multiple target segments. The 10,000 AI credits handle higher conversation volume across three accounts, including follow-up sequences that generate multiple messages per lead.
Scale — US$ 285/month
- 5 LinkedIn accounts included (up to 10 with extra seats)
- 18,000 AI credits per month
- 12,500 leads per month: 10,000 standard + 2,500 Deep Leads
Scale supports teams running coordinated outreach across five accounts, with enough Deep Leads to run enrichment-heavy campaigns and enough AI credits to sustain multi-touch sequences at full volume.
Neither AI credits nor Deep Leads roll over month to month. There is no option to purchase additional credits or Deep Leads outside of upgrading to a higher plan. If a campaign requires more Deep Leads than your current plan provides in a single month, the practical solution is to stagger the enrichment across two months or upgrade before the campaign launches.
Extra seats for LinkedIn accounts beyond those included in a plan are available at US$ 75 per seat per month on any plan.
Common mistakes that waste quota
Running AI People Search without a defined ICP. Each lead delivered costs 1 Deep Lead. If the search description is too broad — "marketing professionals at tech companies" — the results will include many leads who fail ICP qualification. You spend Deep Leads on leads the ICP Filter then disqualifies, consuming AI credits in the process. Define the audience precisely before running the search.
Using list enrichment on unsourced lists. If you acquired a list without clear targeting logic — industry focus, company size range, geography — enriching it produces detailed records of the wrong people. The Deep Leads spent on enrichment return no prospecting value. Source the list with intent before enriching it.
Running the ICP Filter on every lead regardless of pre-qualification. The ICP Filter is a precision tool, not a first-pass filter. If you are importing 1,000 leads from a Sales Navigator search that already applies your core filters, running the ICP Filter on all 1,000 spends AI credits to confirm what you already know. Reserve the filter for genuinely ambiguous leads or for adding a secondary qualification layer the search filters cannot apply.
Not accounting for follow-up credits. Many users calculate AI credits based on first-message volume and forget that follow-up messages in active conversations also consume credits. A five-touchpoint sequence for 200 leads that reach conversation stage can consume more AI credits than the initial outreach to 500 leads. Build your credit estimate around the full sequence, not just the first send.
Waiting for warm-up to end before building the lead list. The 15-day account warm-up period is the right window to run AI People Search and list enrichment. By the time the account reaches full sending capacity, the lead list is already built, enriched, and ICP-qualified. The first day at full capacity goes into prospecting, not preparation.
FAQ
What are Deep Leads in Chattie?
Deep Leads are a monthly quota separate from AI credits, consumed in three scenarios: AI People Search (finding leads by natural-language description), list enrichment (filling in missing data from an imported list), and LinkedIn profile lookup from partial data. Each lead delivered by AI People Search consumes 1 Deep Lead. The quota does not roll over between months and cannot be purchased separately.
Does lead enrichment use my LinkedIn account's daily limits?
AI People Search does not use your LinkedIn account at all — it operates independently, which preserves your daily and weekly caps. List enrichment and profile lookup may reference LinkedIn data, but they do not simulate account actions (visits, connections, messages). Chattie's fixed daily caps — 25 invitations, 40 conversations, and 200 actions — apply to prospecting actions, not to enrichment.
Which Chattie plan fits a solo B2B founder who prospects alone?
The Solo plan (US$ 75/month) includes 1 LinkedIn account, 3,000 AI credits, and 2,500 leads per month (2,000 standard + 500 Deep Leads). For founders prospecting without a team and needing up to 500 enriched leads per month, Solo is the entry point. If volume is higher or more than one LinkedIn account is involved, Growth (US$ 190/month) covers 3 accounts, 10,000 AI credits, and 1,500 Deep Leads — enough for most small teams.
Do AI credits roll over if I don't use them all in a month?
No. AI credits and Deep Leads both reset monthly and do not accumulate. There is no option to purchase additional credits or Deep Leads outside of upgrading to a higher plan. Planning your campaign volume against your monthly quota before the period begins prevents waste.
Quota planning is not a constraint to work around — it is the mechanism that keeps your LinkedIn account within safe operating limits while running structured enrichment and outreach in parallel. AI credits and Deep Leads each cover a specific layer of the workflow. Using them in the right sequence — source, enrich, qualify, then prospect — produces better reply rates than high-volume sending on thin data.
If you want to see how both quotas work inside a live campaign, Chattie is available to explore across Solo, Growth, and Scale plans.