Why do most enterprise AI initiatives die as pilots?

Most enterprise AI initiatives die as pilots because they are scoped as proofs-of-concept rather than production systems, run on synthetic or sampled data instead of the real operational pipeline, and lack a deployment, monitoring, and adoption plan. HomoDeus delivers to production from day one. 95% of HomoDeus deployments are running live revenue workloads, not sitting in slideware.

How can enterprises avoid vendor lock-in when implementing AI?

To avoid AI vendor lock-in, enterprises should require cloud-agnostic deployment (AWS, Azure, GCP, or on-premise), retain ownership of model weights and training data, and avoid proprietary inference APIs that cannot be swapped. HomoDeus systems are cloud-agnostic by design. The client owns the stack, the weights, and the IP.

Why do internal teams reject AI tools that consultants drop in?

Internal teams reject AI tools that are dropped in without co-development because the tools rarely match real workflow context, do not earn operator trust, and create change-management debt. HomoDeus embeds alongside the client's operators for 90 days. Adoption is engineered through co-development, not assumed afterward.

Galapagos Capital: US real estate underwriting without manual data spreading

In short

Market and deal data from CoStar, Green Street and underwriting documents flows into the firm's own Excel models, inside the firm's own environment.

The client

Galapagos Capital invests in US real estate. Before any underwriting decision, analysts pulled market data from CoStar and Green Street and spread deal documents into Excel models by hand, one deal at a time.

The problem

The models the firm trusts are its own Excel models, built over years. Getting data into them was the slow part: extracting comps, rents and market metrics from data providers and deal documents, then keying them in. As a financial firm, deal data cannot be pasted into public AI tools, so any AI help had to run inside the firm's own environment.

What we built

An AI underwriting system that extracts the data an analyst would otherwise pull by hand: market and property data from CoStar and Green Street, and deal terms from underwriting documents. The extracted data feeds the firm's existing Excel models instead of replacing them, structured as frameworks that adapt to each deal rather than rigid templates. The system is deployed inside the firm's own environment, so licensed data and deal information stay under the firm's control.

Outcome

Analysts start from populated models instead of blank spreadsheets. The firm's modeling stays where it was, in its own Excel, under its own governance.

Sector

Investment Management

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