StudioTypo × Venturi Partners

Building Venturi into an AI-native VC firm.

Prepared by StudioTypo · May 2026

Four pillars of the work.

Deal sourcing

  • Pipeline tracking, Series A+ across every channel
  • CRM integration: Affinity into Slack and Excel where the team works
  • Channel performance
  • Funnel analytics
  • Sector signals
  • Historical synthesis
  • Competitive landscaping (Preqin + benchmarking)

Due diligence

  • Memo drafting from the data room
  • Comparable deals: what we passed, what we caught
  • DD calibration review: misses and early flags
  • Country and macro risk (India, Philippines)
  • Legal docs Q&A: termsheets, SHAs, resolutions
  • Rights review: drag-along, ROFR, lock-up, consents

Portco management

  • Dashboards (MIS, KPIs, cap tables)
  • Portco Q&A chatbot on focused, accurate data
  • MIS ingestion: Tresvista output flows into the dashboards
  • Early warning signals
  • Always-on news on funding, M&A and sector shifts
  • Exit window flagging
  • ESG and impact reporting per portco

GP reporting / fund raising

  • Quarterly LP report drafted from portco MIS in preferred format
  • IC decks, portco updates and co-invest memos, auto-built
  • Fund II data room in iDeals, managed and queryable
  • GP view of fund financials, treasury and LP positions
  • AGM and LP-meeting briefing notes
Intros 2,000
1st meeting 400
2nd meeting 50
Due diligence 30
Invest 2
Portcos 9 15

Complexity across tasks.

Four things drive complexity in any task — data integration, the AI layer, interface, and team adoption. Up to one is low; up to two is medium; more than two is high.

Low complexity
  • IC notetaker
  • Searchable chatbot / knowledge base
  • Meeting notes auto-logging
  • Memo drafting
  • KPI / Portco Dashboards
Medium complexity
  • Briefing notes
  • IC decks
  • Co-invest memos
  • LP reporting + quarterly reports
  • Data room Q&A (iDeals)
  • GP view (fund financials, treasury, LP positions)
  • CRM integration (Affinity → Slack / Excel)
  • Portco updates
High complexity
  • Pipeline & funnel tracking (Series A+)
  • Historical data synthesis / backtesting
  • Competitive landscaping (Preqin)
  • Country & macro risk
  • Sector and news monitoring
  • Exit analysis
  • Portfolio monitoring (early warning signals)
  • Legal Q&A & rights review
  • ESG / impact reporting
  • MIS ingestion & structured KPIs (Tresvista PDFs)
  • Cross-source Q&A
  • DD calibration review

The foundation.

This is the structure underneath everything, and what every deliverable connects to.

Pipeline tracking
Funnel analytics
Memo drafting
Portco dashboards
Recurring reports
LP reporting
Copilot
Foundation
Data
  • Internal comms (Slack, mail, notes)
  • CRM (Affinity)
  • Portfolio MIS
AI Abilities
  • Docs Agent (PPT + Excel)
  • Search Agent
Interface
  • Copilot
  • Dashboards

+ Infrastructure, deployment, and production-ready builds.

Example. An LP report is Docs Agent × (Portfolio MIS + Internal comms).

Roadmap: What comes after the foundation.

We do
  • Connect Internal comms (Slack, mail, notes)
  • Set up Copilot and Dashboards
  • Docs Agent (PPT + Excel)
  • Strategic audits and decisions (Dropbox / SharePoint, etc)
  • Add Dropbox / SharePoint
  • Add iDeals data rooms
  • Add Tracxn, Preqin, news feeds
  • Structure KPIs from MIS PDFs
  • Research Agent
  • Search Agent
  • Monitoring Agent
  • User Adoption Testing
  • Iterate on edge cases
Phase 1 Foundation 1–2 months

Data connectors are integrated. Copilot and dashboards are live.

Phase 2 Data depth 2–3 months

Wider data sources are connected and knowledge base is structured.

Phase 3 Build fast 2–4 months

Team workflows are agentic: continuous feedback and iterations.

What gets unlocked
  • Searchable chatbot / knowledge base
  • Memo drafting
  • Portco updates
  • Meeting notes auto-logging
  • IC notetaker
  • Cross-source Q&A
  • Structured portco KPIs (dashboard-ready)
  • Sector and news monitoring
  • Data room Q&A
  • IC decks
  • LP reporting + quarterly reports
  • Pipeline & funnel tracking (Series A+)
  • Legal Q&A & rights review
  • Country & macro risk
  • Portfolio monitoring (early warning signals)
  • Historical data synthesis / backtesting
  • Competitive landscaping (Preqin)
  • Exit analysis
  • ESG / impact reporting

How we work.

Methodology
  • Two-week agile sprints
  • Phase 1: 3–4 sprints
  • Discovery first, then build
  • Plan two sprints ahead, re-scope as we go
What we need
  • Core data access (Slack, mail, Affinity, Portfolio MIS)
  • Data room (iDeals) and Dropbox
  • 2–3 team members for 30-min interviews
  • Cloud provider for hosting (AWS / Azure / GCP, or your preferred)

+ A retainer model with an agreed roadmap and weekly check-ins suits continuous engagement against shifting priorities. Alternatively, work can be scoped into discrete project deliverables after discovery.

Potential deliverables in the first month.

Sprint 1 · Discovery
  • Interviews with 2–3 team members
  • Scoping doc
  • Infra cost estimate
  • Plan for the build sprints

Two weeks. By the end, you'll have a plan we can act on.

Sprint 2 · Build
  • Core data integration — sources connected into a Claude workspace
  • 3–4 portco dashboards (MIS, KPIs, cap tables)
  • Tech and infra deployment begins
  • Work on Copilot starts

Two weeks. By the end, dashboards are live for your team, your data sources are connected to the Claude ecosystem, and Copilot is under way.

+ Every sprint is scoped against the roadmap. Final data sources and dashboard depth are confirmed together after Sprint 1.

Our pricing.

USD 6–10k

per sprint

Two weeks each. Ship a working deliverable, plan the next sprint, learn. Price set against deliverables; agreed in advance.

+ The first sprint is a one-time Discovery Sprint at USD 4k. Every sprint after that is a regular sprint, priced as above.

Team Studio Typo.

Arpit Agarwal

Arpit Agarwal

Founder
  • BITS Pilani
  • 8+ years shipping production AI and data systems
  • Full-stack engineering, cloud infrastructure, LLM pipelines
  • Built enterprise dashboards, copilots and agentic workflows
arpit@studiotypo.xyz LinkedIn GitHub
Charu Tak

Charu Tak

Founder
  • BITS Pilani
  • 8+ years in product, interactive systems and AI
  • Full-stack development, data pipelines, real-time visualisation
  • Built internal tools, automation platforms and product showcases
charu@studiotypo.xyz LinkedIn GitHub

Our projects 1 / 2

Credit GPT

Graphik AI

AI copilot for credit teams

  • Conversational copilot for underwriting and monitoring teams
  • Ingests borrower financials, covenants, MIS and filings into one queryable layer
  • Drafts credit memos, monitoring notes and risk summaries with citations
  • Flags early warning signals and covenant breaches across the book

Moodflow

The G Story

Pitch deck agent

  • Turns a one-line brief into a complete on-brand pitch deck
  • Generates structure, copy and layouts slide by slide
  • Stays on-brand: respects fonts, palette and visual direction
  • Editable canvas to refine, regenerate or export any slide

Roqit Dashboards

Roqit

Search agent

  • Custom analytics builder inside Roqit's fleet platform
  • Chat agent to query the analytics in natural language
  • Step by step wizard for data source, fields, chart type and filters
  • Live SQL data with auto refresh, self serve for operators

Our projects 2 / 2

Darji

Klydo

Brand intelligence layer for content

  • Ingests catalogue, brand assets, market trends and campaign performance into one intelligence layer
  • Reads what's working: viral formats, seasonal signals, competitor moves
  • Generates product images, videos and copy grounded in the catalogue and brand
  • Stays on-brand across SKUs and campaigns, learns from every shipped asset

Talent Search

Levita Capital

Talent sourcing for technical due diligence

  • Natural language search for talent, founders and companies
  • Four retrieval modes (Auto, Fast, Instant, Deep) with live latency and cost
  • Filters for sector, stage, geography and headcount
  • Auto-enriches profiles with funding rounds, investors, revenue and LinkedIn

BridgeNet

4Wall AI

LLM agent playground

  • Physics based bridge building game with an LLM playground
  • Trains AI agents to solve engineering puzzles
  • Live evaluation of agent strategies and failure modes
  • Reusable benchmark environment for reasoning models

Thank you.