Digibee, a low-code service that helps enterprises build integration workflows, raised a $60M Series B led by Goldman Sachs, taking its total funding to $90M+
Mike Wheatley / SiliconANGLE :
Context & Ripple Effects
Digibee is scaling its low-code enterprise integration platform on the back of a $25M Series A led by SoftBank's Latin America Fund early last year; the new $60M Series B lifts total funding past $90M and swaps in a very different kind of lead investor — Goldman Sachs.
The choice of lead matters because Goldman is not just a passive growth investor: per the related coverage it is also among the financial groups reportedly partnering with Nvidia on a roughly $500B AI infrastructure funding package. The same balance sheet is now underwriting both the data-center layer and an application-layer workflow tool.
First-order effects
- Digibee gets $60M to expand its integration-workflow business, moving from SoftBank regional sponsorship to a global financial institution as lead.
- Goldman Sachs adds a direct equity position in the enterprise integration layer, complementing its reported infrastructure-side commitments.
Second-order effects
- Rivals in adjacent data-movement tooling — such as DataBahn, which raised a $40M Series B of its own — now compete against an integration vendor armed with a top-tier institutional backer, raising the fundraising bar for the category.
- Goldman's dual positioning gives Digibee a natural channel into banking clients modernizing legacy system integrations, something smaller iPaaS competitors cannot match.
Third-order effects
- If large financial institutions keep funding both ends simultaneously — massive AI compute buildouts and the workflow software that makes enterprise data usable — capital splits into two tracks, favoring startups whose backers can bridge infrastructure spend and application-layer adoption.
- Integration platforms risk consolidating around vendors backed by systemically important financiers, pressuring independent low-code players toward acquisition or specialization.
The trend: Institutional capital is hedging across the AI stack, financing both the compute buildout and the enterprise workflow tools that consume it — a bifurcation in how AI-era infrastructure money flows.