Treasury models on bank data that does not train BankSync AI.
Consolidate cash and positions from every fund bank and brokerage into the treasury model you already build. Your banking data is pass-through by default, not sold by BankSync, and not used inside the BankSync Boundary to train AI models. Enterprise-ready, with NDA/MNDA, custom DPA, SSO, and a dedicated success team.
Every fund and entity, refreshed on schedule.
The treasury model that's always one export behind
Three things every fund finance team does the hard way, until they don't.
Four banks, four logins, one stale spreadsheet
Operating account here, a sweep account there, the management-company card, a brokerage holding the treasury. Pulling Monday’s cash position means logging into each one and re-keying balances into a model that’s wrong by Tuesday.
The model is only as fresh as your last export
Your treasury model is beautiful. It’s also built on balances someone exported by hand last week. LPs ask for current runway and dry powder, and the honest answer is “let me rebuild the sheet.”
The vendor question nobody wants to fail
Before finance signs anything, someone asks where the bank data goes. Is it stored? Sold? Fed into someone’s AI model? Most aggregators can’t give a clean answer, and the deal stalls in security review.
The vendor question, answered before it's asked
Pass-through sync data flows from your provider to your tools by default. We store full banking datasets only where you enable a managed feature that requires storage.
We charge a subscription for the service, not for your data. Within the BankSync Boundary, we don’t sell banking data or use it for ads or data brokerage.
Within the BankSync Boundary, customer banking data is not used to train AI models. Your chosen AI tool or destination may have its own terms and settings.
The treasury close, in five steps
What it looks like to go from re-keying balances to a treasury model that keeps itself current.
every portfolio company
Connect every entity’s accounts
Add each fund and management-company bank, card, and brokerage once. Credentials never touch us. You authorize through each institution’s own consent screen via open banking, CDR, or PSD2.
Map the chart once
Tag accounts by entity, fund, and purpose so operating cash, reserves, and treasury positions sort themselves. New activity lands already labeled, so the model stays clean without manual sorting.
Wire it into your model
Point feeds at the Google Sheet, Excel workbook, or database your treasury model reads from. Balances, transactions, holdings, and cost basis land where your formulas already expect them.
Let it stay current
Supported balances and positions refresh hourly, daily, or weekly depending on the plan. Your cash position, runway, and dry-powder numbers update without manual exports.
Answer LPs in minutes, not days
When a partner or LP asks for current runway, committed-vs-deployed, or a blended cash position, the number is already in the model, or one question away if you point an AI at it.
Your fund's finance pipeline, in one place
The infrastructure that turns four banking portals into one current model.
Every fund account in one pipeline
Operating accounts, sweep and money-market accounts, corporate cards, and the brokerage holding your treasury, all connected once via regulated open banking and consolidated into a single, current view of cash and positions.
Feeds your model, not another dashboard
Pipe balances, transactions, holdings, and cost basis straight into the Google Sheet, Excel workbook, Notion, Airtable, or database your treasury model already lives in. Keep your model; we just stop you re-keying it.
Positions and brokerage data, not just transactions
Holdings, balances, cost basis, gains and losses, and loan accounts flow through alongside transactions, so dry powder, runway, and committed-vs-deployed roll up automatically across every entity.
Pass-through by default, never trains BankSync AI
Your banking data flows from your bank to your tools by default. We store it only for features you enable, do not sell it, and do not use it inside the BankSync Boundary to train AI models.
Ask your own AI, on your own terms
Point Claude or ChatGPT at your workspace with one API key over a secure connection (MCP). Ask “what’s our blended cash position this quarter?” and get it answered from your live data, which is never sent off to train anyone’s model.
Built for a finance team
Roles for analysts, controllers, and partners, with the whole firm on one enterprise plan. Audit logs, SSO/SAML, and a custom DPA when procurement asks, with no per-seat surprises.
Go deeper on the treasury workflow your model
VC teams usually already have the model. These pages show how BankSync keeps the bank and brokerage data feeding it current.
Multi-bank cash position
Roll fund banks, management company accounts, cards, loans, and brokerage cash into one current view.
Open page →SheetsGoogle Sheets treasury
Keep the fund model in Sheets and let BankSync refresh the source-data tabs behind it.
Open page →ExcelExcel treasury model
Feed the workbook used for cash reviews, LP updates, and partner reporting.
Open page →APITreasury data API
Pipe fund finance data into internal dashboards, databases, AI agents, and operations workflows.
Open page →Run it on the Enterprise plan
Unlimited connections, custom roles, SSO/SAML, audit logs, a custom DPA, and NDA/MNDA on vendor-standard terms, with a dedicated success team and custom volume pricing for multi-entity firms.
Explore EnterpriseVenture capital FAQs
Is our banking data stored, sold, or used to train AI?
Can we keep building our treasury model in Google Sheets or Excel?
Can you connect Raymond James / Alex Brown and other brokerages?
How many accounts and entities can we connect?
Will you sign an NDA or MNDA?
How fresh is the data?
Which banks and regions do you cover?
Why is this an Enterprise plan?
Feed your treasury model
Connect your first account in five minutes, or talk to sales about the Enterprise plan. Either way, BankSync does not sell your banking data or use it inside the BankSync Boundary to train AI.