VENTURE CAPITAL

An AI-native operating platform for a venture capital firm

A venture firm wanted to run sourcing, outreach, diligence and portfolio work on agents. Wexa is building a multi-tenant operating platform with seven agents that share one memory and learn from partner feedback.

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agents and sub-agents with a common design
paid build engagement
shared long-term memory across agents

CLIENT: Early-stage venture capital firm
REGION: India / US
WHEN: 2026
ENGAGEMENT: Paid solution build
WEXA COMPONENTS: 7 agents · shared memory · governance and human gates

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The challenge

Deal sourcing across many data sources
Outreach compliance and follow-up
Meeting notes and diligence status scattered across tools

What Wexa built

Sourcing agent: natural-language query → search across professional networks, deal databases, CRM and files → enrichment and fit scoring → ranked cards for partner approval
Outreach agent with a compliance sub-agent checking every message
Meeting-intelligence agent (pre-brief and post-call capture) and a market-signal marketing agent
Multi-tenant platform with row-level security, durable workflows and an LLM gateway routing reasoning and classification models

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How it runs on Wexa

FigureHow this use case runs on Wexa: client systems connected, ontology discovered, context graph in the Wexa memory layer, agents and process flows, and governance. Highlighted components are central to this use case.

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System architecture

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Agent architecture

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Results

Measure	Before / without Wexa	With Wexa
Analysts for sourcing, outreach and diligence support	6 analysts	3 analysts
Annual cost of the process	$540K	$270K
Companies screened per partner per week	Baseline	3×
Time to implement the AI solution	6 months	20 weeks
AI build cost (one-time)	$480K	$148K
AI operating cost per year (models, infrastructure, upkeep, platform)	$312K	$166K
Total AI cost in year 1	$792K	$313K