TrailSpark

The problem

I spent most of my career building lead-scoring and MQL programs for B2B companies, and the same wall kept showing up. The tools were rigid and points-based. They never looked at the context behind a signal, only whether it matched a filter, so they broke the moment the data didn’t line up exactly. They were opaque to sales: a rep got a score or an “MQL” stamp, never the signals behind it. They only handled acquisition, so upsell, expansion, and renewal needed different tools. And every time the business rules changed, re-scoring everyone was painful enough that people stopped trusting the whole thing.

Once AI got good, my teams kept asking the obvious question: why can’t we just point an LLM at this? The honest answer was more technical than anyone wanted. The signals live in different systems and none of them are normalized. There’s no shared standard for what “good” looks like. And you can’t hand an LLM your raw data: context windows and token limits make it impossible for a model to parse millions of records, join them, and return a deterministic answer every time a new signal lands seconds later.

What I built

TrailSpark is me dogfooding the fix: get the data organized first, into a shape AI can actually reason about. It resolves product, marketing, CRM, and anonymous signals into one chronological timeline per account, then an LLM scores the whole account in a single pass and shows its reasoning. Same signals in, same score out, because the normalization happens before the model, not inside it.

On that foundation it builds the buying group. It connects the committee across your systems, then surfaces who’s engaged, who’s missing, and how to activate them. Other tools score the one person who happens to live in your CRM. TrailSpark scores the committee, even when half of it isn’t in your CRM yet.

What it demonstrates

TrailSpark is the practice’s whole thesis shipped as a product. The AI is the last 5% of the work. The real moat is the plumbing that makes it run reliably and explainably on your own data. Real product, real customers, account-level signal architecture end to end.