Hedge funds & asset managers · Use case
Your model, refreshed from the filings, without an AI touching your formulas.
OpenHelm pulls the latest actuals from SEC filings, maps them to your Excel model, and hands back a reviewable diff with a citation on every cell. The agent proposes; your analyst approves. Formula cells are never overwritten, and every run is backed up first.
now
Already in production with
The control room
Built for analysts who will never trust a black box near their model.
It proposes — you approve
The agent does 90% of the fetching, parsing and cell-mapping, then halts. Nothing is written to your model until an analyst reviews the diff and marks it approved. "Review-ready model refresh", never "AI that edits your files".
Formula-protection guarantee
On inspection every cell is classified as a hardcoded input or a formula. Formula cells are read-only. If an approved edit now targets a formula, the apply job halts, flags an integrity error and skips the cell rather than break your math.
A citation on every number
Each proposed change carries the exact cell address, an old → new diff, a confidence rating and a direct link to the SEC EDGAR filing and XBRL tag it came from. The analyst never has to guess where a number originated.
Automated, timestamped backups
Before any write, the apply job saves a timestamped copy of the workbook (Tesla_Model_Backup_2026Q1.xlsx) to the parent folder. Styles, macros and untouched cells are preserved by a non-destructive writer.
Works with the storage you already use
A uniform file toolset reads and writes Google Drive (narrow drive.file scope via the Picker, no CASA review), OneDrive / SharePoint via Microsoft Graph, and AWS S3 via your IAM keys. Keep using Excel exactly as you do today.
A memory that learns your mappings
When an analyst approves or adjusts a mapping, OpenHelm remembers it in a model-map table. Low-risk, settled mappings can be flagged auto-apply; everything else keeps the human in the loop.
Job 1 · Propose
Discovery and extraction, written to a review ledger.
- 1
Secure retrieval
The target .xlsx is downloaded from your repo (Drive, OneDrive or S3) into an isolated execution sandbox. Nothing runs on your machine.
- 2
Workbook inspection
openpyxl and pandas inventory every sheet, label and reference, classifying each cell as a hardcoded input or a read-only formula.
- 3
Filing extraction
The SEC EDGAR company-facts API is queried with a compliant user-agent for the latest raw XBRL financial facts on the ticker.
- 4
Semantic alignment
Raw concepts (e.g. us-gaap:RevenueFromContractWithCustomer…) are mapped to your custom workbook labels, reconciling unit scales and reporting periods.
- 5
Divergence analysis
Reported numbers are compared with what is already in the cells. Matches are ignored; only genuine divergences become proposals.
- 6
Proposed-changes ledger + email
Each proposal (cell address, diff, confidence, citation, rationale) is written to an interactive ledger, then a summary email lands: "Apple reports Q1 revenue $119.5B. 14 updates proposed for review."
Job 2 · Apply
The committal step is deliberately boring, and safe.
When an analyst marks changes approved, the apply job re-downloads the live file, backs it up, verifies each target is still a hardcoded input, writes only those cells, and uploads, flagging every row applied.
| Manual data entry | Raw AI that edits files | OpenHelm | |
|---|---|---|---|
| Who writes the cell | Analyst, by hand | The model, unsupervised | Analyst approves an agent-drafted diff |
| Formula safety | Depends on the analyst | Can clobber formulas | Formula cells are read-only, integrity-checked |
| Provenance | Re-keyed from the filing | None | Direct SEC EDGAR + XBRL citation per cell |
| Backups | If you remember | Rarely | Automatic, timestamped, every run |
| Where data lives | Your storage | Vendor cloud | Pulled into a single-use sandbox, then destroyed |
What clients say
OpenHelm basically gave us an entire marketing department overnight. This is what it feels like to punch above your weight!
Dr Thom Van Every
Founder · Smoothie Wars
Common questions about model maintenance
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Twenty minutes
Bring a model and a recent filing. We'll propose the diff live.
Show us one workbook and a ticker that just reported. We'll run the propose job on the call, walk the proposed-changes ledger cell by cell with you, citations and all, and show you exactly where the human stays in control.