[ Resources ⬩ Library ]
Better decisions. Working systems.
Reading for investment firms building AI around how they work: what to build first, who should build it, and how to know it works.
Explore the library
[ 01 ⬩ Foundations ]
Build on what lasts.
Firm memory, architecture, and the engineering behind useful AI.
Every Firm Should Own Its Memory
Why a firm’s operating knowledge should remain useful as tools and models change.
Agents run best on Cloudflare
Why we run agents on Cloudflare, and what a firm gets from that.
Railblocks 2.0
Our approach to AI operating systems and forward deployed engineering for investment firms.
[ 02 ⬩ Buyer guides ]
Choose the right approach.
Who to work with, what it costs, and how to test what gets built.
Forward deployed engineering for private equity and VC firms
Railblocks embeds forward deployed engineers with VC firms, PE firms, and family offices to build AI systems the firm owns. Retainers start around $10K a month.
How to evaluate an investment AI agent
How VC and PE firms can test an AI agent on their own workflows, check its sources, and decide when it's ready for the team.
Forward deployed engineering for private equity: FDE partner, consultant, SaaS, or internal team?
When a VC or PE firm should buy AI software, hire a consultant, bring in a forward deployed engineering partner, or build an internal team.
Ode with Anthropic vs Railblocks for VC and PE AI
How Ode with Anthropic and Railblocks differ for VC and PE firms: who they serve, model choice, firm memory and ownership, and which fits when.
How to turn portfolio updates into searchable firm memory
How VC and PE firms can turn company update emails and board packs into a searchable record for monitoring, board reporting, and LP work.