Case studies

Outbound and research systemBiobot Analytics

Problem: Biobot uses wastewater samples to track the spread of disease. It wanted to sell that data to pharmaceutical companies and needed help finding the right buyers and reaching them.
Solution: We built AI tools to research target companies, assess their fit, and draft messages explaining how each could use Biobot’s data. Our two-person team reviewed the research and ran the calls, emails, and LinkedIn outreach.
Outcomes: 35 meetings booked; 27 billed as qualified meetings with client-approved accounts.
$2.4M in potential sales opportunities, according to our campaign records.

Outbound and research systemPrompt Security

Problem: Prompt Security helps companies manage the security risks of using AI. It needed to reach security and AI leaders at banks and financial services firms. Direct outreach was difficult: one bank’s security chief would only consider vendors through its reseller.
Solution: We used AI to review the job titles of people following a security reseller on LinkedIn and select roles relevant to Prompt Security. Alongside this list, we researched target companies and pursued introductions through partners.
Outcomes: 4,036 potential contacts selected from 8,613 profiles, based on their job titles.
Three weekly reports from the wider outreach campaign recorded two meetings booked and one tentative meeting.

Support and knowledge assistantBalancer, Solana Foundation, Binance

Problem: Developers building on blockchain platforms asked technical questions in Discord, including questions the documentation didn’t cover. Support teams had to answer them manually, often revisiting questions they had answered before.
Solution: We built assistants that searched each client’s documentation, code, and previous community answers to respond with sources. A dashboard let client teams review questions, correct answers, and add information the assistant could use next time.
Outcomes: Deployed in Balancer’s Discord community; used internally by Binance’s BNB Chain team.
In BNB Chain’s test of 50 technical answers, the average accuracy score was 67.9%. Half scored 90% or higher.

Research pipelineC.E.C. Analytics

Problem: C.E.C. Analytics makes equipment for collecting wastewater samples. It wanted to reach university researchers who could use that equipment, which meant finding the labs running relevant studies and the people leading them.
Solution: We searched published wastewater research for authors and their universities, then used those findings to build a prospect list. A researcher used the list to find professors and lab staff at each institution.
Outcomes: 50 universities with wastewater research programs identified for prospect research.
A research brief for finding the professors and staff at each university.

Token cost optimizationThesis Finance

Problem: Thesis Finance uses AI to answer questions about portfolios and stock options. Some answers required several model calls that repeatedly charged for the same background information.
Solution: We added caching so the model could reuse that information at a lower cost, and removed duplicate and unnecessary data from the options results sent to it.
Outcomes: 57% lower model cost in an offline replay of 11 recorded options requests: $2.09 to $0.90 per answer.
Options data sent to the model was 2.5–6× smaller. The savings have not been measured on live traffic.

Client names appear with permission. Companies named as met were prospects we approached on a client’s behalf, not clients of ours.

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