Dispatches
FIG. 19 AI enterprise search → FIG. 18 Athena Python SDK → FIG. 17 Athena Skills → FIG. 16 Web Monitor → FIG. 15 Athena for Operations → FIG. 14 Athena for Analytics → FIG. 13 AI search for your DMS → FIG. 12 Multiplayer AI agents → FIG. 11 Athena Anywhere → FIG. 10 Hey Athena → FIG. 09 Athena for Finance → FIG. 08 What code review becomes → FIG. 07 GTM without a CRM → FIG. 06 The company brain → FIG. 05 Two identical stacks → FIG. 04 Finance builds its own APIs → FIG. 03 Model routing in production → FIG. 02 Knowledge workers don't have GitHub → FIG. 01 Pricing, read from transcripts →
Customer story

Spreadsheet consolidation

Department spreadsheets, each in a different shape, collapsed into a single intake table every two weeks—now in fifteen minutes.

2 weeksTime to normalize dozens of departmentspreadsheetsEvery budget cycleManual processCollect dept filesRun fragile macrosChase missing dataBuild intake tableTwo weeks per cycleDozens of files in different shapesDozens of spreadsheets, different shapes, fragile macrosSESSIONNormalize these to one intaketable—Dept, Category, Amount, Month,Owner columns.Writing Python normalization. Targetshape understood.marketing_aug.xlsxfiles uploadedsales_emea_budget.xlsxhr_expenses.xlsxPrototype built overnightUpload files, describe target shape in a paragraphintake_august.xlsxDeptCategoryAmountMonthOwnerMarketingTravelnormalizedAugBudget teamSales EMEASoftwarenormalizedAugBudget teamHRRecruitingflaggedAugBudget teamEngineeringCloud infranormalizedAugBudget teamOperationsFacilitiesflaggedAugBudget teamProductContractorsflaggedAugBudget teamLegalOutside counselnormalizedAugBudget teamWHO CHANGED WHATAthenaNormalized files to targetshapeAthenaFlagged 40 rows, lowconfidencePython runs inside the workbook; transformed cells attributedAthena flagged40 rows could not be mapped withconfidenceFinance analyst maps flagged rowsRule applied to future cyclesWAITING FOR A PERSONMapping saved for next monthHuman mapped once; rule stuckAnalyst maps 40 flagged rows once; rule sticks15 minTime per cycle after the first passDown from two weeksExcel just needed handsathenaintel.com
Fig. 02The story in 25 seconds, from the old way to “Excel just needed hands.”
Before · what we did · after
  1. Before

    Dozens of department spreadsheets in different shapes. Two weeks a cycle to normalize them into one intake table for Finance and HR. Macros nobody would touch.

  2. What we did

    Uploaded the files. Described the desired column structure (Dept, Category, Amount, Month, Owner) in a paragraph. Athena wrote and ran the normalization in Python, with every transformed cell attributed in the workbook.

  3. After

    15 to 20 minutes. The prototype was built overnight before the first working session.

intake_august.xlsxDeptCategoryAmountMonthOwnerMarketingTravelnormalizedAugBudget teamSales EMEASoftwarenormalizedAugBudget teamHRRecruitingnormalizedAugBudget teamEngineeringCloud infraflaggedAugBudget teamOperationsFacilitiesnormalizedAugBudget teamProductContractorsflaggedAugBudget teamLegalOutside counselnormalizedAugBudget teamWHO CHANGED WHATAthenaNormalized files to targetshapeAthenaFlagged 40 rows, lowconfidenceFinance analystMapped flagged rows onceAthenaRule applied to futurecycles
Fig. 01Python normalization runs inside the workbook; every changed cell attributed
How long it takes · what to plan for

The first pass flagged 40 rows it could not map with confidence. A human mapped them once; Athena saved the rule for future cycles.

Excel is great. Excel just needed hands.

Example workflow

From two weeks to fifteen minutes

SURFACES INWHAT COMES INTHE PLATFORMWHAT GOES OUTDELIVERED BACKWebMobile · voiceText (SMS)EmailSlack · TeamsChromeMeeting botMeetingsWordPowerPointSheetsFiles · PDFsDataAgentsAppsLibrarySessionsSpacesStudiosMeetingsLakehouseSemantic modelsDatabaseDashboardsWordPowerPointDashboardsData · APIsApps · portalsWebMobile · voiceText (SMS)EmailSlack · TeamsChromeMeeting botOffice add-in1Workflows (AOPs)2Office add-in3Sheets41 INUpload dozens ofdepartment spreadsheets,each in a differentlayout.2 BUILDAthena writes and runsPython normalization tothe target shape describedin plain language.3 CHECKForty rows flagged;analyst maps them once andthe rule sticks for futurecycles.4 OUTSingle intake table readyin fifteen minutes withfull attribution on everytransformed cell.
Fig. 03The route on the system map: Office add-in → Workflows (AOPs) → Sheets. Connectors: Upload department files, describe target shape in a paragraph
What it runs on
Athena Sheets →Workflows →Attribution & reversibility →
What customers say
“Gave Athena the spreadsheet, explained which column I was trying to figure out, and then ten secs it told me... exactly how the calculation was based on the other data.”
Analyst · A global manufacturer
“One workspace. All different, let's say, avatars of their output data. Right? Suddenly, it's dashboard. Excel is here. PowerPoint is when it's all together. It's very impressive.”
Global Sustainability lead · A global manufacturer
“I'm very excited. This is something that I have been piecemealing with the limited tools that analysts get... rather than me at nine PM getting a failure email and deciding I'll just do it myself.”
DC Ops Data & Analytics lead · A Fortune 500 retailer
“It's good to understand the capability of the platform. That how far, and how quickly you guys can get these things going [...] that has been impressive overall.”
VP-level AI leader · A Fortune 500 retailer
“Lets us get a lot more out of our data than [...] We can sit on top of all of the data in [our data warehouse] as opposed to just, like, our little ingested, you know, three gigabytes semantic models. I think we have a lot of power here.”
Analytics lead · A Fortune 500 retailer
“He wrote the entire stuff. By itself. And I was there, and I was thinking, alright. If I had to write all this stuff, I mean, I would've spent days, days, days.”
Manufacturing analyst · A global manufacturer
“I send that to the programmer and he said, alright. I read your file. I copied the section of the code because he has the right interface, and it works. Wow. Just like magic.”
Manufacturing analyst · A global manufacturer
“Use the Athena platform probably closer to the concept and how it was designed, like, with the spaces architecture where you've got a set of primitives and capabilities, and you bring together the right set of capabilities to solve a task.”
Audit lead · A global professional services firm
“Digital worker, it gets spun up, it gets given access to the toolkits that it needs. It gets given access to the data that it needs. Just exists for the period it needs to exist to perform the task. And then it gets torn back down again afterwards.”
Audit lead · A global professional services firm
“I like that direction, that concept of the agent is the agent. It becomes an entity in its own right with its own ownership and its own missioning, and you get rid of the gray area and the blurry lines.”
Audit lead · A global professional services firm

Verbatim from customer calls. Customers anonymized.

Related

Related products and stories

Athena Sheets →Over a million rows. AI in the cell. Every cell has an author.Supply Chain, Ops & Logistics →Six partner spreadsheets in a shared inbox. One report. Your own Python.Deal sourcing agents →A private-equity team recorded a conversation and had a running sourcing agent two days later.Sub-second BI dashboards →A store-operations dashboard that took four hours to refresh now cross-filters hundreds of millions of rows in under a second, included in Athena services.

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