Claude & AI for VC & Investment

Permissions, prompt injection and data boundaries: what makes an agent that can act also one that can go wrong, and how to run it anyway without carrying the risk. This pilot module is built for organizations moving agents past read-only demos into things that write, send and change data. It's the module security teams should sit in on.

A live opening session built to shift the room, not lecture it: real demos of what Claude can already do inside your workflows, run in front of the group. Participants leave with a working sense of where AI helps and where it doesn't, and enough curiosity to start using it that afternoon.

What tokens, context windows and hallucinations mean, explained without the math and without the hand-waving. Once a team understands why a model sometimes gets things wrong, it stops either trusting it blindly or dismissing it after one bad answer. Every other module in the program builds on this one.

Claude Skills turn a procedure your team already does well into something Claude can repeat on its own: the same checklist, the same tone, the same edge cases covered every time. You build it once with the person who actually knows the process, and from then on every teammate gets that expertise on demand. For teams where quality depends on one or two people, this is how that stops being a bottleneck.

Structure, context and role framing: the craft of asking in a way that gets a usable result on the first try instead of the fifth. Participants practice on real prompts from their own work, not toy examples, so what they take home already fits the job. Every other module assumes this fluency, which is why it comes early.

Claude Projects hold your documents and instructions in one persistent workspace, so every conversation starts already knowing your company, your terminology and your rules. Teams that set this up once save the ten minutes of re-explaining context at the start of every session, every day. For anyone running the same kind of task repeatedly, this alone changes the math.

Artifacts are documents, dashboards and small interactive tools Claude builds live inside the conversation, editable on the spot instead of copied out and reformatted. The session covers static outputs and interactive artifacts alike, with an eye on what a non-technical teammate can actually ship without engineering help. It's the module that convinces people Claude is a builder, not just a chatbot.

The shift from asking Claude a question to handing it a multi-step task it completes on its own, checking in only where it matters. Participants learn what actually counts as an agent, when handing over work beats doing it turn by turn, and how supervision needs to change once Claude is running unattended. Get this wrong and you either babysit an agent that didn't need it or trust one that did.

Claude Cowork brings the team into one shared environment: common folders, defined roles, and agents working alongside people day to day rather than in a private chat nobody else sees. It's the module for organizations moving past individual experimentation toward a shared, repeatable way of working with AI. Participants leave with a live setup, not just a demo.

A full pass across the Office suite: drafting and redlining in Word, tables and analysis in Excel, decks in PowerPoint, and inbox triage in Outlook, taught as one connected workflow rather than four separate tricks. Every exercise uses documents close to what participants actually produce at work. It's the broadest module in the program, and the one most teams come back to most after training.

Claude inside Excel for finance work specifically: building and reviewing models, tracing a formula back through six sheets to find where the logic breaks, and producing outputs that hold up in front of a board. This is for teams whose spreadsheets are the actual product, not a side task. Formula archaeology alone tends to pay for the session.

Connecting Claude to the systems your team actually runs on: drives, CRMs, databases, through connectors and MCP, with the permission boundaries covered before the connection ever gets made. Participants leave knowing exactly what to expose and what to keep out of reach. This is the module that turns Claude from a smart typewriter into something acting on real, current data.

Real engineering workflows built agent-first: how a task moves from a prompt in the terminal to reviewed, shipped code. The module is built for developers, but the data-analysis half works just as well for a non-developer who lives in spreadsheets and needs that same rigor applied to a script. Teams leave with a working setup running in their own repo.

How context windows, memory and cost interact, and what to do about it before a long working session starts drifting or the bill surprises someone. Participants learn to spot when Claude is still hauling a task that should have been closed an hour ago, and how to reset without losing what mattered. A short module with an outsized effect on both output quality and the invoice.

The rules every employee needs before their first real session: what data is safe to share, what isn't, and what org-level guardrails should exist so individual judgment isn't the only safeguard. This module is usually the one legal and IT ask for by name. It replaces vague anxiety about compliance with a clear, specific policy people can actually follow.

Claude's deep research mode run on real questions: multi-source investigations that come back with citations you can trace, not just conclusions to take on faith. Participants learn to frame a sharp question before opening the tool, and to separate what a source actually says from what Claude inferred. A pilot module, built for teams whose decisions need a paper trail.

Working with real data files in Claude: spotting trends, generating charts, and getting to the follow-up question a first look wouldn't have surfaced. This pilot module is built around participants' own files rather than a sample dataset, so what's covered is directly usable the next day. It suits anyone who currently exports a report and stares at it, rather than interrogating it.

Where people belong in an AI-assisted workflow: which steps need a review gate, which need an approval, and how to calibrate trust so oversight isn't either everywhere or nowhere. This pilot module gives teams a concrete framework instead of a gut feeling about what to check. Get the calibration right and quality goes up without slowing anything down.

Coming soon: preparing and governing the data an AI system actually works from, structure, access and quality, before automation gets layered on top of it. This syllabus item is for teams that already tried an AI project and hit a data problem they weren't expecting. Skipping this step is the single most common reason automation projects stall.

Coming soon: meetings that document themselves, notes, decisions and action items captured without someone assigned to type through the whole call. This syllabus item closes the loop with follow-through, so a decision made in a meeting doesn't quietly disappear by Thursday. A small module with a very visible payoff across every team, not just one.

Coming soon: verification habits and hallucination hygiene, so research produced with AI holds up when someone in the room actually pushes back. This syllabus item is built for roles where being wrong in public carries real cost: legal, finance, VC. Participants will practice on claims they'd otherwise have taken at face value.

Coming soon: writing the AI policy an organization actually needs, usage rules, risk tiers and rollout governance, instead of a generic template nobody follows. This syllabus item is for leadership and compliance stakeholders shaping how AI gets adopted company-wide. Built to produce a document teams can point to, not a discussion that goes nowhere.

Coming soon: writing with Claude without losing the voice that makes a brand recognizable, tone, house language, and the specific discipline of native-quality Hebrew rather than a translated one. This syllabus item covers spotting AI tells in a draft as much as producing one. For any team whose writing represents the company in public.

From a data room to a DD binder: a systematic pass over the documents, red flags with sources, and a partner summary built on citations rather than memory.

Triage of inbound deal flow against the fund's own criteria — every deal gets a short profile and a recommendation, and partners only meet what passed.