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Case Studies
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Real agents. Real outcomes.

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Names anonymized at the customer's request. Numbers verified. We don't run agents in slide decks.

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Marketing Agency · Amsterdam
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From 4 hours of inbox triage to 12 minutes
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94%
inbox time saved
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A 12-person creative agency was losing the founder's first 4 hours every morning to email — client requests, internal status, vendor invoices, prospect outreach all mixed together.

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What we built (48 hours): An agent that triages every inbound email into Slack channels (#clients, #internal, #vendors, #leads), drafts replies in the founder's voice for the top 20% that need a human, auto-replies to FAQs, and surfaces only the messages that genuinely need attention.

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What changed: Founder now spends 12 minutes on email mornings. Saved 19 hours/week. Two clients said the agency feels "more responsive than ever" — they're getting acknowledgements within 5 minutes of sending, even at 2 AM.

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69|69| GmailSlackNotion 70|70| HubspotDrafts approval flow 71|71|
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Law Firm · Rotterdam
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Case-status updates that draft themselves
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€68K
annual hours saved
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A 4-partner matrimonial law firm. Every Friday, each partner sent a case-status email to every client — average 35 clients per partner = 140 emails total, ~6 hours of partner time, often pushed into Saturday.

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What we built (5 days): An agent that reads each open case (matter notes, recent court filings, billable activity log), drafts the Friday update in the partner's voice, sends it to the partner for one-click approval, and dispatches via the firm's email system. Updates reference exact case events, recent filings, and next steps.

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What changed: Partner Friday time on status updates dropped from 6 hours to 35 minutes (review-and-approve). Reclaimed time = ~22 partner hours/week firm-wide. At €600/hr blended rate, that's €68K/year in opportunity cost recovered. Three clients commented unprompted that the firm's communication felt "more thorough."

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88|88| CaseLodgeOutlookApproval gatesVoice modeling 89|89|
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Insurance Broker · Antwerp
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Renewal-reminder funnel + policy comparison summaries
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+27%
renewal close rate
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A 6-person broker was hand-running renewal reminders 60 days out from policy expiry — chasing them via the CRM, then often forgetting until the policy lapsed. Industry benchmark close rate on renewal touches: ~42%. Theirs was 38%.

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What we built (3 days): Agent pulls upcoming renewals from their CRM (Brio Toolbox), drafts a personalized renewal-reminder email comparing the customer's current policy to two updated quotes, schedules send-dates (60d / 30d / 7d), and tracks responses. When a customer replies, the agent classifies the intent (renew / negotiate / shop around) and routes to the right broker with context.

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What changed: Close rate jumped from 38% to 65% in the first quarter. Two brokers reclaimed ~10 hours/week each. Bonus: the policy-comparison summaries the agent writes are now used in the initial sales process too — they were that good.

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106|106| Brio ToolboxOutlookComparison engineReply classification 107|107|
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Real Estate · Eindhoven
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Lead follow-up across 3 agents, 4 listing portals
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2.4×
viewings booked
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A small RE agency with 3 agents was getting leads from 4 portals (Funda, Pararius, Jaap, their own site) — manually copying each into the CRM, then chasing follow-ups, often missing leads entirely on busy days.

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What we built (2 days): Agent monitors all 4 inbound lead channels, dedupes by phone+email, immediately drafts a personalized first-touch message referencing the specific property the lead asked about, routes the response to whichever agent is on duty, and schedules viewings via Calendly without back-and-forth.

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What changed: Viewings booked jumped from ~18/week to ~43/week. Average time-from-lead-to-first-contact dropped from 4 hours to 6 minutes. The agents now have time to actually do viewings instead of chasing them.

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124|124| Funda APIPararius scraperCalendlyCRM sync 125|125|
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Want to be the next case study?

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First customer in your vertical pays half-price for the first 3 months in exchange for being a case study (anonymized to your preference).

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