Solution
For asset management data operations teams: every inbound fund report, datasheet, and portal download extracted into your database through one pipeline, with the team reviewing exceptions instead of keying files.
The problem
Portal downloads, PDFs, spreadsheets — every file must be found, identified, and keyed by hand. The people who understand the data spend their days on data entry.
Inbound volume rises every quarter. The manual chain scales one way: more hands, more cost, more keying errors under deadline.
Industry standards shift and issuers redesign their documents. Each change means a hand-built parser or a retraining cycle before data flows again.
The product, not a promise
How it works
Crawlers fetch asset documents and data files from each configured source on a schedule.
Incoming files are auto-sorted by asset type and format — no manual triage queue.
The platform pulls the relevant fields from each document, whatever the layout.
Analysts verify flagged records; every value links back to its source page.
Clean, structured asset data flows into the downstream asset management database.
Who it's for
Data operations analyst
Head of data operations
Risk & IT
Asset data arrives faster than a team can key it in. A global asset management firm faced exactly that: dozens of sources in dozens of formats — portal downloads, PDFs, spreadsheets — each file identified, downloaded, and entered by hand. Volumes grew daily, industry standards kept shifting, and the analysts who understood the data spent their time on data entry.
Botminds crawlers fetch asset data automatically from each configured source. Incoming files are classified by asset type, then the relevant fields are extracted — whether the source is a structured feed or a scanned datasheet. Onboarding a new data type takes hours rather than a development sprint, because the platform learns the format instead of requiring a hand-built parser for each one. When standards change, the pipeline adapts at the same speed: point it at the new format, confirm the extraction, and it runs.
Asset data feeds reporting, and reporting has deadlines and auditors. Two properties carry the weight. First, every extracted value is cited to the exact page it came from, so a reviewer verifies a number in seconds. Second, nothing enters the asset management database without passing review — the platform proposes, a human approves.
That combination let the firm scale volume without scaling headcount or risk. Analysts stopped hunting for documents and started reviewing exceptions. On-time asset reporting improved because the bottleneck — manual identification and entry — was gone, and accuracy improved because every record carried its own evidence. The same pattern holds wherever data outruns the team keying it: many sources, many formats, one governed pipeline from document to decision.
Objections, answered
Every value is cited to the exact page and location it came from, so a reviewer verifies a number in seconds instead of reopening the file. Records the platform is less confident about are flagged, and nothing posts without a named approval.
Extraction targets map to your fields and your classification scheme. The platform learns each document format against your model — configured once, applied identically to every file from that source.
Point the pipeline at the new layout, confirm the extraction on sample files, and it runs. Adapting is a configuration step, so a standards change costs hours instead of a parser rebuild.
The platform already reads fund reports, datasheets, and feeds. Setup is connecting your sources and mapping fields to your database — configuration work measured in days, with new data types added in hours after that.
Watch a raw portal download become classified, extracted, cited records in your structure — live in the demo.
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