The Tools
The Tools, Adapted per Vertical
Aura's tool set is configured per deployment, because the questions a multi-location restaurant operator asks are not the questions a behavioral health facility director asks. The agentic architecture is identical across verticals. The tools are not.
Here's the production tool set for a multi-location restaurant deployment as a representative example.
Location and Performance Tools
List Locations
Every active location with city, state, and identifiers
Get Store Performance
Sales, orders, AOV, ad spend, ROI, month-over-month change, for one location or the whole portfolio over any date range
Get Daily Trends
Day-by-day time series of sales, orders, and ad spend
Analytical Tools
Run Correlation Analysis
Pearson r, optimal lag detection, adstock-transformed correlations, weather correlations, in three modes (raw daily, detrended, weekly)
Get Attribution Analysis
M-CE's causal model coefficients, daily lift estimates per channel, iROAS, adstock hyperparameters, and stability diagnostics
Context Tools
Get Weather Data
Daily temperature, precipitation, and conditions for any location, for any date range
Get Foot Traffic
Placer.ai visit counts, unique visitors, repeat rate, dwell times, cross-shopping brands, demographics, capture rate
Get Location Notes
Annotations and operational context attached by your team
For Autonomous Weekly Insights, the Tool Set Expands
Campaign-level breakdowns (Google ad groups, Meta ad sets), creative performance, and platform-specific drilldowns. Aura can pull up to 50 rows of campaign data and 20 individual ad creatives when writing the Monday morning report.
For Healthcare Deployments
The tool set is reshaped around the admission funnel: program lists, activity summaries by pipeline stage (verified benefit → prospect → admitted), channel breakdowns with conversion rates, dispositions, ad performance, daily activity trends, facility annotations, weather, predictive forecasts, and causal lift analysis.
Eleven tools, shaped to the questions a healthcare marketing director actually asks.
For B2B Deployments
The tool set is built around long-cycle deal mechanics: deal summaries, attribution breakdowns (originated vs. influenced), pipeline stage analysis, new logo identification via Leadfeeder, deal-level deep dives, and website visitor analysis.
Eight tools, shaped to a 127-day sales cycle.
The architectural pattern is identical across all three verticals. The tools are vertical-specific because the questions are vertical-specific. Aura adapts to your business; you don't adapt to Aura.
Context Layer
The Human Context Your Data Doesn't Capture
Most marketing analytics tools share the same blind spot: it only sees what got logged. It doesn't see that your busiest location had a staffing crisis in week 3. It doesn't see that you ran a soft launch in a new market with intentionally throttled spend. It doesn't see that the regional manager went on parental leave. It doesn't see that a competitor opened across the street in early March.
These things matter. They explain anomalies that the data alone can't. And no causal model is going to reverse-engineer them from POS receipts.
Aura includes a notes layer for exactly this. Any user can attach a note to a specific date, date range, or location: staffing changes, renovations, new competition, promotional anomalies, regulatory events, seasonal one-offs. Notes are stored alongside the structured data and surfaced to Aura through a get_notes tool whenever relevant.
Example
"Why did sales drop at the Carmel location in week 3 of February?"
"Sales at Carmel dropped 18% in week 3 of February. The weather model estimates the winter storm cut Tuesday and Wednesday traffic by about 40% and accounts for roughly 11 of those 18 points. The operational notes show the general manager was on bereavement leave and the assistant manager was running the store that week, which may explain some of the rest."
That's a different kind of answer than a pure-data analytics tool can produce, because the answer required information that lives in human heads and got captured in a notes field, not in an API.
Marketing data has a much larger context window than the database. Aura is built to work inside that larger context.
Start With the Fit Check
Free and honest, with no obligation on your side. If your data can support causal conclusions, you'll get a written fixed-fee quote and timeline. If it can't, you'll know that too.
Start the Fit Check →