Numbers come from the source
Charts and tables use values returned by GA4, Google Ads, Meta, Search Console, and other connected platforms. The language model does not invent the values displayed in them.
The AI marketing workspace
Your AI marketing team, briefed on your business.
An analyst, a copywriter, a dashboard builder, an SEO strategist, a UX reviewer, and a planner, all working from your context and live platform data.
Connects to
Google AnalyticsGoogle AdsMetaSearch ConsoleGoogle Tag ManagerGitHub
Your team, right now6 agents active
Insightexplaining Tuesday’s conversion dip, with citations
Canvasbuilding the blended ROAS dashboard
Scribedrafting Meta ads for “Busy Parents” in 3 languages
SEOtracking 42 keywords across 3 markets
UXpinning 3 conversion issues on the pricing page
Planner2 proposals waiting for your approval
The context layer
Answers that know your business, not just your data.
Generic AI guesses. Odaq agents read your brand voice, customer personas, timeline events, and uploaded documents alongside the numbers, so a traffic dip gets explained by last Friday's outage, not a hallucinated theory.
Brand voiceTone and claims agents may make
PersonasWho the copy is written for
Timeline eventsSales, outages, launches, dated
File knowledgePDFs and docs, retrieved on demand
Why did conversions drop last Tuesday?
Insight
Your payment gateway was down for two hours that day. Your timeline notes a "Stripe incident". Excluding that window, conversions were up 12%.
GA4 · sessionsContext · timeline
Transparent by design
The numbers come from your data. Odaq turns them into answers.
Odaq agents plan the investigation, fetch the required data through connected APIs, and apply visible filters, formulas, joins, and transformations. Once the results are returned, the agent interprets the evidence and explains what it means.
You receive a useful answer, not just a data dump, while keeping the ability to inspect every number behind it.
See how Odaq handles your data- Question
- AI plan
- API datasets
- Visible recipe
- Verified results
- AI interpretation
- Answer or export
Visible recipe Verified
How the result was built
- Source
- Google Ads and GA4
- Date range
- Last 90 days
- Filter
- Campaign status equals Active
- Join
- Campaign ID
- Formula
- ROAS equals Revenue divided by Spend
- Sort
- ROAS descending
AI interpretation2 sources
What the evidence means
Spend increased, but most of the additional budget went to campaigns with declining conversion rates. Three campaigns account for 71% of the efficiency loss.
Google Ads · spendGA4 · revenue
Inspect the supporting data Odaq interprets the findings
The agent connects patterns across returned datasets, highlights meaningful changes, and turns the evidence into a clear answer or recommendation.
Every transformation is visible
Inspect which datasets were fetched and how they were filtered, sorted, joined, merged, or calculated.
Take the supporting view with you
From the table view, download the displayed rows and visible columns as Excel or CSV. Active grouping is reflected in the file.
The team
Six specialists. One shared memory.
Each agent owns a marketing function. They share your context, hand work to each other, and file everything through one backlog.
InsightThe analyst
Ask questions over your live GA4, Ads, Meta, and Search Console data. Every claim cites the dataset behind it.
ExploreCanvasThe dashboard builder
Describe the chart you want. Canvas blends platforms, computes columns like ROAS, and replays every build against live sources.
ExploreScribeThe copywriter
Persona-targeted copy for Google, Meta, LinkedIn, and email in 28 languages, with a pre-flight fact review.
ExploreSEOThe strategist
Crawls your site, tracks rankings across five markets, checks your schema graph, and files prioritized fixes.
ExploreUXThe reviewer
Captures the pages you send traffic to on mobile and desktop, then pins each conversion issue to the element causing it.
ExplorePlannerThe backlog
Proposals arrive ranked by impact × effort. You approve, your team executes on a board wired to GitHub, and agents verify the result.
ExploreOne shared context layer
Brand voice, personas, timeline events, and file knowledge, read by all six.
From insight to shipped
Most tools stop at advice. Odaq closes the loop.
Recommendations become tasks, tasks become GitHub issues, and nothing gets marked done until an agent has checked the live result.
01
Agents propose
Insight and the SEO agent turn findings into recommendations, ranked by impact and effort. Nothing enters the backlog without your approval.
02
You approve
Review each proposal with its evidence. Accept it into the backlog, or dismiss it. You stay the editor-in-chief.
03
Team executes
A kanban board with assignees and PDF work briefs. Drag a task to the GitHub column and the Planner agent drafts the developer issue for you.
04
Agents verify
Before a task closes, agents check the live result: the page itself, ad copy in your campaigns, GTM tags. Every consulted verifier must pass.
Proposed
SEOFix duplicate title tags
InsightShift budget to Search
In progress
ScribeQ3 ad copy refresh (3 personas)
On GitHub
#214Add JSON-LD to /pricingVerified done
Pause weekend campaigns
Focus your marketing.
Six agents, your context, one backlog, and proof the work shipped.
Request early access