15+ hours returned per week. Deal volume tripled. Same lean team.

Moonlock is a Web3 investment network and advisory firm that used Dynome's AI Audit and targeted automations to process three times the inbound deal volume and return over 15 hours per week to their team, without hiring.

Moonlock is a Web3 investment network and advisory firm based in Toronto, founded in 2024. The business sits at the intersection of deal origination, project advisory, and distribution, helping early-stage Web3 projects raise capital through a curated investor network, reach their target audiences through a roster of influencers, and navigate go-to-market through hands-on strategic guidance.

Unlike a traditional venture capital firm, Moonlock's model is built around network density and active deal facilitation rather than direct capital deployment. With 50+ active portfolio projects, 200+ investors in network, and more than 500 influencers across Tier 0 through Tier 3, the firm generates significant value at every stage of a project's journey. The challenge is operational: all six of Moonlock's service lines (investment facilitation, OTC fundraising, advisory, influencer acquisition, business development, and network introductions) require high-frequency, personalised human activity to deliver at quality.

By the time Moonlock engaged Dynome, Tyler Dart and the team had built something genuinely differentiated in the Web3 space. The question was whether the operational model could keep pace with the ambition.

Moonlock's inbound volume was healthy, a sign that the firm's reputation in the Web3 space was working. But healthy inbound at scale creates a bottleneck. Every project submission arrived with a deck, a whitepaper, a tokenomics model, and a Telegram channel. Evaluating each one against Moonlock's thesis across DeFi, AI, DePIN, infrastructure, and gaming required several hours of manual reading before a go or no-go decision could be made. At lower volumes, that was manageable. As inbound grew, the backlog deepened, and promising projects sat unread for days while the team's analytical time was absorbed by submissions that should have been filtered out in the first ten minutes.

Portfolio monitoring presented a different kind of problem. With 50+ active projects spanning early advisory relationships through to post-TGE investments, the relevant signal surface was enormous. Token prices, exchange listing announcements, on-chain activity, team updates, community sentiment on X and Telegram, anything material could happen to any project at any time. Without a systematic monitoring layer, Moonlock was learning about portfolio developments at the same time as the rest of the market. For a firm whose value is active support and strategic guidance, that lag was a real competitive liability.

The influencer network was perhaps Moonlock's most differentiated asset, but translating it into an operational advantage required Tyler to hold the full picture in his head: which influencers were active, which audiences matched which project type, which tier was appropriate for which budget, who was available. That institutional knowledge was not systematised. Getting from a project brief to a sent outreach message was taking the better part of a week, every time.

The impact of the three automations was felt immediately in how the team spent its time, but the more significant shift was structural. The deal flow screener didn't just save hours, it changed the quality of the decisions being made. With every inbound project arriving with a one-page brief, a thesis-fit score, and a set of targeted questions, the first conversation with any project became sharper and faster. Tyler was spending time on calls, not on reading decks.

The portfolio intelligence digest had a similar effect on Moonlock's advisory relationships. Rather than learning about portfolio developments reactively, the team was surfacing intelligence in calls with portfolio companies before they raised it themselves, a meaningful shift in how Moonlock shows up as a partner. For a firm whose value lies in the quality of its network and relationships, that proactive posture matters.

The influencer engine addressed what had previously been a ceiling on the business. Moonlock's 500+ influencer network was only as valuable as the speed at which it could be matched and activated for any given project. Cutting the brief-to-outreach cycle from nearly a week to a single day meant deals could be moved at a pace that matched client expectations, and freed Tyler's time for the parts of the influencer relationship that genuinely require a human.

The challenge Moonlock faced is not unique to Web3. Any business built on network density, deal facilitation, and high-frequency relationship management runs into the same constraint: the activities that create the most value are the ones that don't scale naturally. You can't automate the relationship, but you can automate almost everything that surrounds it.

The Dynome AI Audit exists to find exactly those opportunities. Before committing to platforms, workflows, or team hires, the audit gives a structured picture of where the operational leverage is, and what can be removed from the team's plate without reducing the quality of what the client or partner experiences. In Moonlock's case, that meant three targeted automations that left every client-facing and influencer-facing interaction exactly as human as it needed to be, while returning significant capacity to the team for the work that genuinely requires them.

The audit-first model also means Moonlock knows what they're not building yet, and why. That sequencing is often as valuable as the automations themselves.