In financial services, the question isn’t whether your marketing team will use AI. It’s whether they’ll use it through a system compliance can see, or through personal accounts and pasted API keys nobody can. Mad Fish Elements, built by Mad Fish Digital, connects Claude, ChatGPT, Microsoft Copilot, or Gemini to 33 marketing, analytics, and ops platforms through one governed gateway: per-user OAuth, per-seat read/write/delete permissions enforced server-side on every call, and a full audit trail of every AI-initiated action. built around documented security controls.
Give the team a governed gateway instead of a ban they’ll route around. Every assistant session works through per-user OAuth - each marketer clicks Connect and signs in with their own credentials, no API keys, no shared service accounts - and every operation on every platform is logged with the individual’s identity. Shadow AI dries up because the sanctioned path is faster than the workaround, and compliance gets a single view over all of it.
Every read, write, and delete that flows through the gateway - across Google Ads, LinkedIn, Meta, GA4, BigQuery, HubSpot, Salesforce, and the rest - lands in a full audit trail: who, what, when, on which platform. When a campaign change is questioned months later, the answer is a lookup, not an investigation. Automated error monitoring catches failed operations, so silent breakage doesn’t turn into a reporting discrepancy.
Provision analysts, agency partners, and reviewers with read-only seats where the restriction is enforced server-side on every call, per platform - not by platform-native roles of varying quality, and not by trusting anyone’s prompt hygiene. A read-only seat can’t execute a write or delete on any connected platform under any circumstances. Access reviews get simple: one gateway, per-seat scopes, one list.
Your assistant queries BigQuery, pulls GA4 behavior, and reads LinkedIn and Google Ads performance in a single thread, with every call permission-checked and logged. The cost-per-qualified-lead analysis that took an analyst three days of exports happens in one conversation, from live sources, the same way every time.
Data Visualization publishes governed, shareable reports - charts, KPIs, tables, datasets - from any connected data. Scheduled refreshes update the same report at the same link, so the board sees the same artifact every quarter with current, traceable numbers. Teams review and annotate in the report itself, which keeps the commentary next to the figures it explains.
Your marketers are pasting campaign data into consumer chatbots and wiring up unofficial API keys, because the sanctioned path is too slow. Every one of those workarounds is an unmonitored channel between your ad accounts, your analytics, and a model nobody vetted - with no record of what was read, changed, or shared. Compliance can’t review what it can’t see. And the current answer, banning the tools and hoping, mostly guarantees the activity continues without a paper trail.
When marketing operates in a regulated business, “who changed this, when, and under whose authority” isn’t a curiosity. It’s a question you have to answer precisely, months later. Native ad-platform change histories are inconsistent, scattered across nine logins, and silent about why a change happened. The moment AI starts touching campaigns, that gap becomes an audit finding waiting to be written: an automated action with no attributable human identity behind it.
Your analysts and agency partners need read access to Google Ads, LinkedIn, GA4 (Google’s web analytics platform), and BigQuery to do their jobs. The platforms’ native role systems are inconsistent - some barely distinguish viewer from editor - so access reviews turn into spreadsheet archaeology, and the safest available answer becomes “no access.” That sends everyone back to emailed CSV exports, which are themselves an ungoverned data channel.
Your growth story runs through LinkedIn ABM programs (account-based marketing - campaigns aimed at named target companies), Google Ads on high-intent terms, GA4 behavioral data, and a BigQuery warehouse where the funnel actually gets modeled. Getting one coherent cost-per-funded-account or cost-per-qualified-lead view means manual exports and a spreadsheet only one analyst understands. By the time it reaches leadership, the quarter has moved on.
Numbers that reach the board, examiners, or investor materials have to reconcile, repeat, and trace to source. Ad-hoc spreadsheet pipelines produce figures that shift depending on who pulled them and when - and “the number changed because the export changed” is not a sentence anyone wants to say in front of an audit committee.
If you’re wondering what the first week looks like, start with these five - typed exactly as written.
“Show cost per lead this quarter by product line - checking, mortgage, wealth, commercial lending - across Google Ads and LinkedIn, next to last quarter’s numbers.”
A CPL scorecard by product line from live data, ready before the pipeline meeting instead of after it.
“Pull every change made to the mortgage campaigns in Google Ads and LinkedIn between June 1 and June 30, with the user and timestamp on each, formatted for compliance review.”
A defensible change log in minutes - the question that used to be an investigation becomes a lookup.
“Which of our 100 named target accounts engaged with the LinkedIn ABM campaigns in the last 60 days, and which went quiet after the first touch?”
An account-level engagement read your sales team will actually use, pulled straight from the platform.
“Draft a Monday morning briefing in Google Docs summarizing last week’s spend, leads, and CPL by program from Google Ads, LinkedIn, and GA4 - using reads only.”
A ready-to-send exec briefing built entirely from read-only pulls - nothing in any ad platform gets touched.
“Publish an account-opening funnel dashboard with Data Visualization - clicks to applications started to funded accounts, from GA4 and BigQuery - refreshed weekly at a link the executive team keeps.”
One governed funnel view with the same definitions every week, so the number never shifts depending on who pulled it.
In a business where every number gets questioned, the chart’s job is to make the answer obvious and defensible. Here’s what works for financial services:
This is governance in infrastructure, not policy documents. Each user authenticates with their own OAuth identity - no API keys, no shared credentials - so every action is attributable to a person. Read, write, and delete permissions are assigned per seat, per platform, and enforced server-side on every single call; a read-only seat can’t be talked into a write by any prompt. The full audit trail records every operation across all 33 platforms, giving compliance a reviewable, exportable history of everything AI touched. Automated error monitoring flags anomalies early. And the whole system is built around documented security controls, so your vendor risk review starts with concrete permission, identity, and audit-trail evidence.
A higher base buys a lower rate. Pay more up front and keep more of your spend. Every Ad Ops tier includes governed platform access, per-user grants, and administrator controls.
See what's wrong across every account.
Find issues and fix them — full read/write automation.
Everything in Reporting, plus:
Proactive optimization across every brand you own.
Everything in Standard, plus:
Your playbooks, your rules, at scale.
Everything in Premium, plus:
How managed spend is measured: total spend on connected ad accounts during the billing month, measured from the platforms' own metrics APIs at billing time, regardless of where changes originated. Exact accounts and queries are visible in the audit trail. Disconnected accounts are excluded.
Data Visualization is tenant-wide: Viz Starter /mo for up to 50 saved reports, Viz Team 9/mo for up to 100 reports (included with Ad Ops Premium), or Viz Scale 9/mo for unlimited reports.
Yes. Every operation on every connected platform - reads included - is captured in the audit trail with the authenticated user’s identity, the platform, the operation, and the timestamp. There’s no side channel: all gateway activity is logged activity.
Yes. Read-only is a server-side property of the seat, enforced per platform on every call. It doesn’t depend on ad-platform role quality, assistant behavior, or prompt wording - a write request from a read-only seat fails at the gateway, and the attempt is logged.
Mad Fish Elements is built around documented security controls. We do not claim financial-industry regulatory certifications. Your compliance team evaluates fit against your own regulatory obligations, and the per-seat permissions, per-user OAuth, and full audit trail are built to support that evaluation.
No API keys anywhere in the flow. Each user clicks Connect and signs in to each platform through native OAuth with their own credentials. Credentials are per-user, access is per-seat, and offboarding means deprovisioning a seat rather than hunting down shared keys.
Claude, ChatGPT, Microsoft Copilot, and Gemini on the assistant side. For most financial brands the core set is Google Ads, LinkedIn, Microsoft Advertising, and Meta for acquisition; GA4, Google Search Console, Google Tag Manager, and BigQuery for measurement; HubSpot or Salesforce for the funnel - all within the 33 connected platforms and roughly 1,470 operations.
Your team is going to use AI this year. The only decision is whether it happens inside a governed gateway with per-seat permissions and a complete audit trail, or in the shadows. Bring your compliance lead to a walkthrough - the permission model is the part they’ll want to see first. Join the waitlist to get started.
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