Platform

An agentic operating system for GTM.

Custom agents, wired into every data layer, configured to your KPIs, running continuous experiments across your whole stack. One connected system rather than a pile of point tools.

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Layer 01 / 06

Every layer, joined

Agents read a single joined view of your analytics, ad accounts, CRM, product events, support, and warehouse. No decision runs on a siloed dashboard.

AnalyticsAd accountsCRMProduct eventsSupportWarehouse

The stack, layer by layer

01

Every layer, joined

Agents read a single joined view of your analytics, ad accounts, CRM, product events, support, and warehouse. No decision runs on a siloed dashboard.

AnalyticsAd accountsCRMProduct eventsSupportWarehouse
02

Agents built for the job

A fleet of agents that already exist, each scoped to a GTM function and configured to your workflows, rather than one generic assistant bolted on the side.

ResearchCreativeLifecycleCROPipelineAttribution
03

Tuned to your numbers

We configure each agent against your objectives, guardrails, and brand so it optimises for conversion, revenue, and margin, not clicks.

ObjectivesGuardrailsBrand + value propMargin targets
04

The loop that compounds

Hypothesise from evidence, ship the variant, measure against revenue, log the result. Every pass pulls from the last, so the system sharpens over time.

HypothesiseShipMeasureLogSharpen
05

The whole stack, one loop

The same experiment loop runs across every surface at once, so tests share a data layer and wins in one channel inform the next.

Paid mediaWebsite + CROOrganic + paid socialLifecycle + emailSEO + AEO
06

Human-in-the-loop, with a guard

Hypothesis selection, copy approval, and ship-to-production stay human gates. Every variant is checked against your value prop and audience before it goes live.

Human gatesApprovalsValue-prop checksAudit log

What the agents run

Seven surfaces, one loop.

Website CRO

Agents read the analytics, pick the next test, ship it to your site and verdict it against a baseline written before anything changed.

What it reads, how it decides, example experiments

Deep analytics

One joined view across analytics, ad accounts, CRM and product events, with the tracking audited before any of it is trusted.

What it reads, how it decides, example experiments

Creative

Ad creative and landing pages produced as variants to test, not as deliverables to sign off and hope for.

What it reads, how it decides, example experiments

SEO and AEO

Programmatic pages, technical diagnostics that find what is actually blocking indexing, and writing for answer engines as well as search.

What it reads, how it decides, example experiments

Organic social

A posting cadence that learns. What settles gets verdicted, and the playbook rewrites itself for the next cycle.

What it reads, how it decides, example experiments

Lifecycle and email

Segmented campaigns reviewed against last month, drafted and QA'd by an agent, held at an approval gate until you send.

What it reads, how it decides, example experiments

Every one of them runs on the same data layer and the same experiment loop, so a result in one is evidence for the next rather than a separate report.

How a deployment works

From brief to a system that compounds.

  1. Can the agents reach the siterepo access
    Is there enough traffic to readvolume floor
    Which channels can we getaccess granted

    we say no when the answer is no

    01

    Check the fit

    Before anything is signed: can the agents reach your site, is there enough traffic for a result to mean anything, and which channels can we actually get access to. We say no when the answer is no.

  2. 02

    Audit the tracking

    Access to analytics, search, ads, CRM and email, then we check what they are telling you. Most accounts are measuring at least one thing wrong, and every decision after this depends on it.

  3. metricbefore
    Sessions / wk1,240
    Enquiries / wk9
    Enquiry rate0.7%
    Indexed pages38 / 61

    written down before anything changes

    03

    Set the baseline

    Numbers written down before anything changes, because that is what a result is measured against. Meanwhile we fix what is simply broken, which needs no baseline and shows up in week one.

  4. Researchagent
    Creativeagent
    Lifecycleagent
    CROagent

    4 agents pointed at your properties

    04

    Plug the agents in

    Each agent is pointed at your properties, your voice and your guardrails, and each one starts behind a human approval gate. You decide which ones earn the right to run on their own.

  5. Pricing hero A/BWon
    Meta set 3Running
    Lifecycle step 2Shipped
    trial to paid3.1% to 9.4%
    05

    Run the experiments

    One at a time, hypothesis and review date logged before the change ships. A verdict takes weeks rather than days, and anything faster is either bought traffic or a guess.

  6. each cycle sharpens the next

    06

    Report and compound

    A monthly read on what changed, what it moved and what needs a decision, plus one call. What replicates gets promoted into the playbook the agents follow next cycle.

Governance

Human-in-the-loop, with a guard.

Agents read the data, draft variants, and measure outcomes. People decide. Hypothesis selection, copy approval, and ship-to-production are human gates, and every variant is checked against your current value proposition and target audience before it goes live. Wins and losses are both logged, so a loss tells you which model was wrong and sharpens the next call.

Human gates

Selection, approval, and ship are human decisions, not autopilot.

Brand-safe

Every variant checked against your value prop and audience first.

Fully logged

An audit trail of every hypothesis, test, and outcome.