Proof
What the work actually looks like
Two engagements, written up honestly. Both are with companies where the buyer is an expert and the sales cycle is long, which is the kind of problem we are built for. We do not publish clients' commercial performance data, so these describe the problem, the approach, and what we found.
CSI | Pharma · Clinical trial supply
Finding pharma buyers in a market full of the wrong audience
Search demand around clinical trials is dominated by patients, while the actual buyers are a small universe of sponsors and CROs. We built acquisition that filters for the few, across paid search and a production AI assistant.
What we did: Paid search on lead quality · SEO and AI search · Retrieval AI assistant
Read the case studyRoutePerfect | Travel technology
Analytics a product team can actually trust
Three analytics tools gave three answers, and a share of the traffic was never a person. We built the first-party event stream, layered bot-filtering cohorts, and self-serve reporting the team can audit.
What we did: First-party event pipeline · Bot-filtering cohorts · Self-serve reporting
Read the case study
Recognise the problem?
If your category buries a small number of real buyers under a much larger irrelevant audience, or your own numbers are not telling you the truth, those are the two problems we solve most often.
