# AI Marketing Systems

-   Built AI-agent workflows that moved paid media review from monthly to daily
-   Built tools for market research and answer-engine citation analysis
-   Created shared go-to-market context with evidence and review rules

## Build tools around the recurring work

As Blaxel's first marketing hire, I owned the strategy and budget while also doing the work. I built tools for the recurring reviews: what was happening in paid acquisition, what was changing in the market, and which sources were shaping answers about the category.

Each tool had a specific input and a decision to support. Paid review brought together delivery and product outcomes. Market research connected a market read to its sources. Answer-engine citation analysis helped identify conversations worth reviewing.

## Review paid performance daily

The paid-growth system combined Google Ads delivery, PostHog product activity and Stripe payment outcomes. AI-agent workflows synthesized conversion data and queued proposed optimization changes, moving review from monthly to daily.

I kept campaign changes behind human approval. A proposed change had to pass validation, match the approved change set and have a rollback or compensating action. Low-volume evidence remained inconclusive.

Paid growth / Review workflow

### From paid signals to proposed changes

Review cadence

Monthly → **Daily**

Delivery

#### Google Ads

Spend, clicks and ad conversions

Product

#### PostHog

Product activity and conversion behaviour

Payments

#### Stripe

Payment outcomes

AI synthesis

#### Findings + proposed changes

Bring the evidence into review. Keep low-volume findings inconclusive.

Human decision

#### Approval before campaign changes

Validation, a valid approval matching the change set, and a rollback or compensating action.

The review workflow behind the move from monthly to daily paid media reviews, with its inputs and change controls.

That made the automation useful for preparation and review while keeping responsibility for spend explicit. The [paid-growth page](/work/blaxel/blaxel-paid-growth-and-measurement) covers the program's results and measurement boundaries.

## Make market research traceable

The Market Signal dashboard kept each assessment linked to its sources. The workflow collected and scored material, then checked the sources behind the claims before including them in the dashboard.

![Blaxel Market Signal dashboard showing changes against a rolling baseline, supporting source links and suggested next actions, with an evidence key distinguishing source material from synthesis.](/images/writing/blaxel/market-signal-archive.png)

The Market Signal dashboard, captured from the August 28, 2026 archive. Each assessment links to its supporting sources and a proposed action. The scores are research assessments from that snapshot. [View the dashboard capture at full size.](/images/writing/blaxel/market-signal-archive.png)

## Find the conversations behind AI answers

I also built a citation-review tool around Profound's answer-engine data. It identified Reddit conversations for review using three signals: frequently cited threads, topics where Blaxel was less visible and threads gaining citation share.

The tool turned those signals into a weekly queue. It helped direct attention to the source material influencing answers, with human review before any participation. It didn't automatically post into the conversations.

Community Citation Radar / Queue design

### Three signals shape the weekly queue

Profound citation data, with Reddit thread context.

#### Highest cited

Up to **3** review slots

What already shapes the answers?

Threads with strong citation activity in the category.

#### Underrepresented

Up to **3** review slots

Where is Blaxel less visible?

Cited conversations in topics with a visibility or representation gap.

#### Newly climbing

Up to **3** review slots

What is gaining attention?

Threads gaining citation share across the latest complete week.

**9** threads at most

#### One weekly review queue

Each selected thread appears once. Read the source and decide whether there's a useful contribution to make.

Participation stays human-reviewed.

The ranking code reserves three places per signal and removes duplicate selections. This shows the queue's designed capacity; fewer eligible threads can leave places empty.

This was part of the answer engine optimization work: understand what the engines were citing, inspect the source and decide whether there was a useful contribution to make.

## Give the tools shared context

The go-to-market (GTM) knowledge graph held positioning, customer context, messaging, proof and writing guidance in versioned files. It distinguished source-backed claims from inferences and drafts, with review rules for deciding what was ready to use.

That gave the work a common reference. A launch draft, a paid review and a positioning decision could draw on the same product and customer context while preserving the evidence each task needed.

## Questions this page answers

### What marketing tools did Michael Preuss build at Blaxel?

I built Blaxel tools for paid media review, market research and answer-engine citation analysis, supported by a shared go-to-market knowledge graph. Each tool brought evidence into a specific marketing review or decision.

### How did Blaxel's paid-review automation work?

Blaxel's paid-growth system combined Google Ads delivery, PostHog product activity and Stripe payment outcomes. AI-agent workflows synthesized conversion data and queued changes for human approval. Campaign changes required validation, a matching approval and a rollback or compensating action.

### How did Michael Preuss use answer-engine citation data at Blaxel?

At Blaxel, I built a tool that used Profound citation data to identify Reddit conversations worth reviewing. Its weekly queue considered frequently cited threads, topics where Blaxel was less visible and threads gaining citation share. Participation remained human-reviewed.

### What changed in Blaxel's marketing review cadence?

My AI-agent workflows moved Blaxel's paid media review from monthly to daily. The market and citation tools produced research inputs for review; no separate revenue or answer-engine visibility lift is assigned to those tools.

## Page details

- Description: The marketing tools I built at Blaxel: paid media review, market research, answer-engine citation analysis and a shared go-to-market knowledge graph.
- Timeframe: Joined June 2026
- Company: Blaxel
- Role: Head of Growth & Marketing