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    PROVENTrusted by 200+ founders · 42 verified reviews · over $10M generated for clients

    Case Studies

    Real revenue moves,
    shown before and after.

    Every card below shows the constrained KPI, the job that was not being done, the system that changed it, and the verified outcome. Revenue and growth proof first, efficiency proof after.

    Current AGL workers are built from jobs, processes, and systems AGL has historically performed manually or through previous technology. The results below were produced by those engagements. They are not claims that a current AI worker produced these outcomes.

    $43B
    M&A tracked
    300+
    Clients scaled
    1M+
    Leads generated
    $10M+
    Tracked revenue
    3M+
    BH downloads
    Expert Vetted, top 1% on Upwork
    100% Job Success Score
    $10M+ tracked client revenue generated
    1M+ leads captured across client stacks
    3M+ Business Hangouts downloads
    Ex PE CMO and CRO, $43B+ in M&A experience
    B2B SaaS
    90 days
    $40K
    Baseline MRR
    $65K
    MRR at 90 days
    +62.5% MRR

    Product-market fit turned into a repeatable growth motion.

    Constraint

    Demand existed, but no one owned qualification and follow-up on a fixed cadence, so revenue moved only when the founder pushed it.

    System

    Rebuilt attribution across inbound, outbound, and lifecycle, then ran a 90 day cadence on the top three revenue levers with no added headcount.

    KPI

    Monthly recurring revenue, with sales cycle length as the supporting measure.

    Verified outcome

    MRR moved from $40K to $65K in 90 days and the sales cycle shortened by roughly 60 days, with no added headcount.

    Related AI workers

    Built from the same jobs and processes. Not a claim that these workers produced the result above.

    Attribution rebuiltOffer sharpenedSales cycle -60d
    Fractional CMO · B2B
    6 months
    $0
    Prior pipeline
    $1.2M
    Qualified pipeline
    + $1.2M pipeline

    Zero pipeline to $1.2M qualified in a single quarter.

    Constraint

    No one was consistently identifying the right accounts, running outreach off one message, or recording where meetings came from.

    System

    Locked the ICP, ran outbound and inbound off the same message, and rebuilt HubSpot so every booked meeting had a source of truth.

    KPI

    Qualified pipeline created, and booked meetings by source.

    Verified outcome

    $1.2M in qualified pipeline created from a zero baseline, with every booked meeting attributable to a source.

    Related AI workers

    Built from the same jobs and processes. Not a claim that these workers produced the result above.

    ICP lockOutbound + inboundHubSpot rebuild
    DTC Ecom
    19 days
    $0
    Baseline revenue
    $89,871
    Revenue, 19 days
    56x ROAS

    $89,871 booked in 19 days at 56x ROAS.

    Constraint

    The buying cohort was never defined, so creative and landing pages spoke to everyone and converted almost no one.

    System

    Locked the buying cohort, matched the offer trigger, and rebuilt the landing around a single winning creative pattern with retention flows behind it.

    KPI

    Revenue booked in the launch window, and return on ad spend.

    Verified outcome

    $89,871 in booked revenue across 19 days at 56x return on ad spend.

    Related AI workers

    Built from the same jobs and processes. Not a claim that these workers produced the result above.

    Cohort lockedOffer trigger matchLanding rebuilt
    B2B Events
    90 days
    0
    Prior registrants
    13,600
    Registrants at launch
    + 13,600 registrants

    13,600 registrants for a single category-defining event.

    Constraint

    Audience building was episodic, with no single narrative or repeatable promotion motion across owned, earned, and paid.

    System

    Ran a partner amplified launch across owned, earned, and paid, with a single narrative and a single call to action.

    KPI

    Registrants captured for the event.

    Verified outcome

    13,600 registrants captured from a zero baseline in 90 days.

    Related AI workers

    Built from the same jobs and processes. Not a claim that these workers produced the result above.

    Webinar SaaSPartner activationLadder built
    PE Portfolio · ROAS
    60 days
    Baseline
    Prior spend efficiency
    $56 ROAS
    Return per $1 in 60 days
    56x return on ad spend

    Media spend rebuilt around the buyer, not the platform.

    Constraint

    Creative was being judged by platform metrics instead of buyer intent, and nothing was killed or scaled on a fixed cadence.

    System

    Set buyer altitude, calibrated channel by channel, and ran a weekly kill and scale cadence on the top creatives.

    KPI

    Return per dollar of media spend.

    Verified outcome

    $56 returned per $1 of media spend within 60 days of the rebuild.

    Related AI workers

    Built from the same jobs and processes. Not a claim that these workers produced the result above.

    Buyer altitudeChannel calibrationWeekly kill scale
    Fractional CMO · Ecom
    12 weeks
    $500
    Prior CAC
    $50
    CAC at 12 weeks
    CAC divided by 10

    Cost to acquire a customer dropped 10x in a single quarter.

    Constraint

    Spend was pointed at a blended audience, and reporting did not show which cohort actually produced customers.

    System

    Segmented cohorts, refreshed creative around the highest intent buyer, and rebuilt attribution so the reporting matched reality.

    KPI

    Cost to acquire one customer.

    Verified outcome

    CAC fell from $500 to $50 over 12 weeks while acquisition volume held.

    Related AI workers

    Built from the same jobs and processes. Not a claim that these workers produced the result above.

    Cohort segmentationCreative refreshAttribution truth
    PE Portfolio · Multiple
    24 months
    1.5x
    Prior multiple
    7.5x
    Exit multiple
    5x lift on enterprise value

    Exit multiple moved from 1.5x to 7.5x on enterprise value.

    Constraint

    Revenue depended on founder effort, and nothing about the growth motion was documented well enough for a buyer to underwrite.

    System

    Installed a durable revenue engine, then documented the system into diligence-ready reporting the buyer could underwrite.

    KPI

    Enterprise value multiple at exit.

    Verified outcome

    The exit multiple moved from 1.5x to 7.5x on the same underlying business.

    Related AI workers

    Built from the same jobs and processes. Not a claim that these workers produced the result above.

    Revenue engineDiligence-readyExit-optimized
    M&A Advisory
    6 months to close
    24 mo.
    Prior exit window
    8 mo.
    Compressed window
    3x acceleration

    Exit window compressed from 24 months to 8.

    Constraint

    Buyer conversations stalled because the revenue story and the reporting behind it were not ready for diligence.

    System

    Cleaned the revenue story, installed diligence-ready attribution, and paired it with curated buyer outreach at board-grade cadence.

    KPI

    Months from start of process to a saleable, diligence-ready business.

    Verified outcome

    The window to a saleable business compressed from 24 months to 8 months.

    Related AI workers

    Built from the same jobs and processes. Not a claim that these workers produced the result above.

    Diligence-readyAttributionBoard-grade reporting

    Two ways to start from here.

    Start with one worker on one KPI and measure the economics, or bring us the whole revenue engine. Neither path requires the other.

    Start with one job

    Pick the KPI you need to move, put one worker on it, then decide whether to add more.

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