✍️How to Automate Your Fund Without Developers
1. Key Themes
Theme 1: The "workflow builder" wave, and the Q3 hangover
The AI-coding boom turned everyone in VC into a builder, then into a maintainer
Cheap AI-generated software created a surge of DIY automation inside funds, followed by a pullback once maintenance costs surfaced.
"Around Christmas last year, Claude Code got good enough that anyone could build seemingly reliable software. By Q1, everyone in VC was suddenly a builder. Investment professionals, finance teams, ops teams. By Q3, many of them were pulling back. The weekend build turned into a maintenance job nobody had signed up for."
Most funds will stay engineer-free, so a new archetype has emerged
Andre defines the "workflow builder" as the dominant operating model for funds without engineering teams.
"Most funds will never hire a dedicated engineering team. In our 2026 Landscape Report, we call them workflow builders: investment, finance and ops people stringing bought tools together into end-to-end workflows, often without a single engineer."
Theme 2: Data foundation sets the ceiling for all automation
One system of record, first-party data, then third-party enrichment
Data quality is the prerequisite; every downstream workflow inherits its gaps.
"Every workflow you build later reads from the same data, so its quality sets the ceiling for everything else." "Pick one system of record. For us that's Affinity. Everything else connects to it."
Proprietary historical material is an underused asset
"Bring in your first-party data. Pitch decks, IC memos, board decks. At Earlybird that's three decades of material, and most of it lived in Google Drive."
Verify against ground truth
"Take a random sample from your portfolio and recent deal flow, and check whether the system has them, and whether the data is right."
Theme 3: Buy the parts, own the glue
The defensible layer is integration logic, not the tools
"Almost every part of the stack can be bought today, and a workflow builder should buy almost all of it. What you own is the glue: how the tools connect, what triggers each workflow, and what the output looks like."
Two-layer architecture: automation plus agents, with MCP as the connector
"An automation layer that moves data between tools when something happens. n8n and Zapier are a good place to start. An agent layer that reasons over the data. For us that's Claude, connected to our system of record through an MCP." "Your Claude or ChatGPT is only as smart as the connectors you give it."
Maintenance is the hidden cost
"Workflows break when a tool changes its API or someone renames a field. So for every workflow you wire up, write down which tools it touches, what triggers it, and who gets the alert when it fails."
Theme 4: Standardized "skills" create consistency that humans can't
Agents with a standardized skill give uniform output
"Give the same input and the same desired output to five investment professionals, and you'll get five different outcomes. Give it to an agent with a standardized skill, and for the first time ever, you get consistency."
Train on your own past outputs, and reuse community skills
"Collect 10 of your own past memos and give them to the model as the target. Every fund's memo looks different, so show it yours." "Before writing one from scratch, check vcskills.com for a version someone in the community has already shared."
Theme 5: Ownership and governance determine adoption
Technical ownership correlates with firm-wide AI adoption, but most funds lack it
"In our Landscape Report, the more technical the owner of the stack, the higher the AI adoption across the firm. Most workflow builders don't have that person yet. In 80% of them, investors and partners own the stack."
Put a partner/GP on strategy and budget, then hire technical talent once ROI is proven
"Give ownership of the strategy and the budget to someone at least at partner level. Ideally a GP, someone who knows the P&L of the management company." "Once the workflows prove their ROI and you have the conviction, find someone technical to own it full time."
2. Contrarian Perspectives
1. Don't hire engineers first; buy the stack and own only the glue. Against the instinct to build proprietary tooling, Andre argues for buying nearly everything and keeping a lightweight, documented integration layer.
"You don't need an engineering team to automate a fund." "Start from clean data, automate the work you've mapped, buy what someone else maintains, and put a senior name next to the budget." Evidence: the Q3 pullback, where "the weekend build turned into a maintenance job nobody had signed up for."
2. Start automation with the lowest-value, easiest-to-verify layer (productivity), not the sexy scoring layer. Many funds want to automate deal scoring first; Andre advises the opposite.
"The productivity layer is where most workflow builders should start. It's the easiest to verify and the cheapest to get wrong." "The biggest mistake I see: firms start doing something before they've structured the problem."
3. The real risk of AI automation isn't failure, it's "good enough" output that kills critical thinking.
"The failure mode I worry about most is AI slop. Once the workflows run, everyone can produce an 80-90% good output. Even interns. People feel incredibly powerful, and they quickly stop thinking." "I think this will become way more of a problem over the next 12 months."
3. Companies Identified
- Description: AI-first private capital CRM.
- Why mentioned: Andre's chosen system of record, and the newsletter sponsor.
- Quotes: "Pick one system of record. For us that's Affinity. Everything else connects to it." / "Brought to you by Affinity – The AI-first private capital CRM"
- Description: VC firm (Andre's firm).
- Why mentioned: Case study for first-party data assets and an AI usage rule.
- Quotes: "At Earlybird that's three decades of material, and most of it lived in Google Drive." / "Our rule at Earlybird: before anyone throws a case into Claude, they answer one question. What are the 2-3 key things that need to go right to make this a huge outlier?"
Claude / Claude Code (Anthropic)
- Description: AI assistant and coding agent.
- Why mentioned: The agent layer in Andre's stack; Claude Code was the catalyst for the builder wave.
- Quotes: "Around Christmas last year, Claude Code got good enough that anyone could build seemingly reliable software." / "An agent layer that reasons over the data. For us that's Claude, connected to our system of record through an MCP."
- Description: Workflow automation platforms.
- Why mentioned: Recommended starting points for the automation layer.
- Quotes: "An automation layer that moves data between tools when something happens. n8n and Zapier are a good place to start."
ChatGPT (OpenAI)
- Description: AI assistant.
- Why mentioned: Alternative agent layer, noted as dependent on connectors.
- Quotes: "Your Claude or ChatGPT is only as smart as the connectors you give it."
- Description: Community repository of shareable AI skills for VC workflows.
- Why mentioned: Resource to reuse skills rather than build from scratch.
- Quotes: "Before writing one from scratch, check vcskills.com for a version someone in the community has already shared."
- Description: LP database platform (30k+ active family offices, fund of funds, pensions and more).
- Why mentioned: Sponsored placement.
- Quotes: "Find new LPs in our database of 30k+ active family offices, fund of funds, pensions & more via Final Close"
- Description: Law firm and AI/data company, respectively.
- Why mentioned: Co-hosts of the DDVC x Slush Breakfast in Helsinki.
- Quotes: "Join the DDVC x Slush Breakfast in Helsinki Nov 18 with Goodwin & Harmonic"
4. People Identified
- Description: Author of Data Driven VC; Earlybird investor.
- Why mentioned: Author and source of the five-step framework.
- Quotes: "Hi, I'm Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with data and AI."
Lindsay Bayuk
- Description: CMO of Affinity.
- Why mentioned: Co-host of the Nov 17 webinar on data-driven fund AI practices.
- Quotes: "Join Andre Retterath of Data Driven VC and Lindsay Bayuk, Affinity's CMO, on Nov 17 at 1 PM ET"
5. Operating Insights
1. Shadow a partner to find what to automate. Use an AI-native student to document every task, its frequency, and time cost, then automate the top 20% of tasks.
"Get a young AI-native student. Have them shadow a partner for 2-3 weeks, every single hour of the day. Write down every task, how often it comes up, and how much time goes into it. Then automate 80/20. Two months in, you'll be surprised how much is automated, and you'll have the ROI to justify a real budget."
2. Maintain a one-page workflow register as your "codebase." Document tools touched, triggers, and failure-alert owner for every workflow.
"For a fund without engineers, that one-page register is the closest thing you'll have to a codebase."
3. Force a "what makes this an outlier" question before using AI on a deal. A cheap guardrail against output complacency.
"Before anyone throws a case into Claude, they answer one question. What are the 2-3 key things that need to go right to make this a huge outlier?"
6. Overlooked Insights
1. IC papers are heavily recycled, and that's an automation opportunity. Mentioned only in the webinar promo, it signals how much memo work is repeatable and where standardized skills could pay off.
"...why messy-data scoring models lose trust, why 80%+ of IC papers are recycled, and how permission-aware MCPs protect sensitive deals."
2. Permissioning is a looming issue for agent access to deal data. The promo notes the hard part of AI automation is organizational (ownership, visibility), not technical, and flags permission-aware MCPs as a safeguard for sensitive deals.
"AI has made investment workflows cheap to automate. The hard part is organizational: who owns scoring, who sees which deals, and which IC memo sections need a human."