Relevance AI
Relevance AI
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Campaign numbers read like an analyst

Dashboards show you clicks. The Campaign Reporter ties every channel to the pipeline it actually opened, checks whether a spike is real or one outlier deal, and says when the data is too thin to call.

Scheduled run · Fri 4pm
Campaign Reporter

Weekly report. Tying every channel to opened pipeline before anyone sees a click count.

Pulling campaign performance4s
Attributing to opened pipeline6s

Search wins on clicks but email wins on pipeline. Reporting the click view alone would point budget the wrong way.

Posting the report to Slack1s
Report is posted. The headline: email converts at 4.2% and sourced $310k in pipeline, while search wins clicks but converts at 2.1%. Judged on pipeline per dollar, email is 3× more efficient. My recommendation, with the attribution linked: shift 20% of search budget to email for the next cycle.

How it works

Friday 4pm scheduleChannel question askedCampaign endedQuarter closed
Weekly report postedBudget shift recommendedOutliers separatedReview checkpoint set

Triggered on a schedule

The report runs every Friday afternoon, and answers ad-hoc channel questions from Slack between cycles.

Judges channels on pipeline, not clicks

The agent attributes every channel to the pipeline it opened, traces spikes to the deals behind them, and separates real trends from outliers and noise.

Posts the report to Slack

The weekly report lands in your marketing channel with the recommendation, the reasoning and the attribution linked.

Without Relevance

With Relevance

Reports rank channels on clicks and CTR, because that is what the ad platforms export.

Every channel is judged on the pipeline it opened per dollar spent, with the attribution shown.

A channel doubles its number and the deck says double down, no one asks why.

The agent traces the spike to its deals first. One outlier contract skewing the average gets reported as one outlier, not a trend.

Someone loses Friday afternoon to exporting, reconciling and pasting the same report together.

The report posts itself every Friday, built the same rigorous way whether the week was quiet or chaotic.

The report ends at the numbers. Deciding what to do with them is next week’s meeting.

Each report closes with a budget recommendation and the reasoning behind it, ready to accept or challenge.

Two weeks of promising data becomes a confident slide, then a quarter of misallocated budget.

When the sample is too small to trust, the agent says so and sets a review date instead of forcing a verdict.

The methodology shifts with whoever builds the deck, so this month’s numbers cannot be compared to last month’s.

One attribution method, applied identically every week, so trends across reports actually mean something.

Held to a quality bar, on every run

Every report is checked against eval test cases written for this exact job before it posts. If a run fails the bar, it never ships.

Campaign Reporter

Pass rate · last 14 days

Sampling 2% of live runs · 95% passing this week

Channels judged on pipeline, not clicks96%
Percentages always carry their base numbers93%
Outliers reported as outliers94%
Small samples flagged, never called91%

Train your agent like an employee

Onboard it with your playbooks and correct it in plain English. It learns the lesson for good, and nothing changes until you approve it.

Campaign Reporter
Instructions updated
Tool added
Pipeline attribution query
13 evals added
Channels judged on pipeline, not clicksPercentages always carry their base numbers+11 more
Publish version 4
PDFAttribution Methodology.pdf240 KB
Report every channel using this attribution model.
Read Attribution Methodology.pdf. Updated my instructions, added a pipeline attribution query tool, and wrote 12 evals to check every run.
Never report a percentage change without the absolute numbers behind it. A 200% jump on a tiny base is not a headline.
Understood. I added an eval that fails any report where a percentage claim appears without its base numbers, so small-sample jumps can never read as trends.
Teach it something new…
Ask

Built for enterprise teams

Run agents on your real data with the access controls, audit trails, and residency guarantees enterprises require all built in.

Monitoring

Real-time visibility into every agent’s activity, performance, and cost.

RBAC

Control who can use, build, edit, and deploy agents.

Data residency

Multi-region deployment keeps your data within your required geography.

Version control

Full version history on every agent. Roll back to any previous state.

Audit logs

Every action, every decision, every tool call logged and exportable.

Human-in-the-loop

Set approval gates on any action or allow agents to ask questions.

SSO / SAML

Enterprise single sign-on with SAML 2.0. Centralize identity & access.

PII masking

Detect and redact personally identifiable information.

SOC 2 Type IIGDPROTEL & Delta Sharing
KPMG
"The ability to be vendor agnostic and the ability to scale across a breadth of functions is a really key feature."

Levi Watters

Partner, KPMG Australia

Read more
Autodesk
"The key for us was how we can modularize industry knowledge and the best playbooks, and apply it."

Allen Roh

Senior Marketing Manager, Autodesk

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Canva
"We're looking for every place where AI can allow sellers and customer success reps to be more engaged with customers."

Rob Giglio

Chief Customer Officer, Canva

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"The ability to be vendor agnostic and the ability to scale across a breadth of functions is a really key feature."

Levi Watters

Partner, KPMG Australia

Read more

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Put the Campaign Reporter to work this quarter

We partner with enterprise teams to take this work off their plate. An agent built with you, proven against your quality bar, and live in weeks.