Diligent AI

We built Diligent AI because VC firms spend 80% of their time on manual research that AI can do better and faster. Our agents handle deal sourcing, due diligence, and portfolio monitoring — so partners can focus on decisions that matter.

Curious how you're handling the data freshness problem in due diligence most of the useful signals (founder references, cap table history, competitive dynamics) live en unstructured or relationship-gated sources. That's where I've seen AI tools hit a wall. are your agents navigating that?

This is strong because it attacks one of the biggest bottlenecks in VC: time. If the agents can reliably handle sourcing + diligence without hallucinating or missing critical nuance, this could be a serious force multiplier for funds. I’d be curious how you balance autonomous decisioning with partner trust and verification.

If the agents can reliably handle sourcing + diligence without hallucinating or missing critical nuance, this could be a serious force multiplier for funds.

The minutes-instead-of-weeks positioning resonates — we use AI agents internally for operational work and the speed delta is real. For VC due diligence specifically, the hardest part is probably the trust calibration: how confident can a partner be in the agent's output on a first-time deal in an unfamiliar vertical? Do you surface confidence scores or flag areas where the agent's data coverage is thin? That kind of transparency would be critical for adoption beyond early adopters.

I feel this is useful in upcoming VC processes using AI.

Really interesting direction with Diligent AI. The idea of deploying autonomous agents to handle venture due diligence—especially deal sourcing, market analysis, and portfolio monitoring—addresses one of the biggest bottlenecks in VC workflows. What stands out is the promise of compressing weeks of research into minutes. If the platform can consistently deliver high-quality, verifiable insights (not just surface-level summaries), it could meaningfully improve how investors evaluate opportunities and track portfolio performance. That said, the real test will be in the depth and reliability of the outputs. Due diligence isn’t just about speed—it’s about context, nuance, and trust. It would be great to see more transparency around data sources, methodology, and how the AI handles edge cases or incomplete information. Overall, a strong concept with clear potential—especially if it can strike the right balance between automation and investor-grade rigor.

Are you planning also to offer this to startups, so they can standardize their sell-side DD and at the same time provide VCs with a steady deal flow?

For VC DD, the hardest part is reproducible sourcing: being able to point to which document a claim came from. If your agents attach page-level citations to every finding, that would differentiate you from generic "summarize the data room" tools.

This is pretty impressive—automating due diligence and deal sourcing could save VCs a ton of time. Curious how accurate the insights are compared to traditional research.

Seems like you can just create a databse of such sites not do this anaysis for all VC's