Boswell collects and verifies a target individual’s mindset, perspectives, knowledge base and communication style. It creates a cognitive profile that lets you know if they are a good prospect and what matters to them. What do they need to hear? What do they need to feel? It can also give you deep insight into their skills and experience.
Previously this level of human analysis would have cost thousands of dollars and taken many hours of research work. Now it is available at a fraction of that cost.
For finding buyers, building expert AI, understanding customers, and preparing for conversations that matter, there has never been something like the Boswell Engine.
Not a personality test. Not mind-reading. A sourced interpretation that can be challenged.
The same evidence-first machinery can answer very different questions. Choose the problem you’re trying to solve.
Fewer names. Better reasons to make contact.
Boswell qualifies prospects before writing a word. It checks who they are, researches their public record, develops sourced interpretations of their priorities and communication style, and drafts outreach in the sender’s voice. It also tells you whom not to pursue.
Not a costume. A traceable body of knowledge.
Boswell studies an expert’s published work, talks, articles, and documented positions, then constructs a versioned research collection: principles, disagreements, methods, and the evidence behind them. Those collections can inform specialized AI personas without pretending the source expert authored the AI’s decisions.
Beyond demographics and the last purchase.
The B2C application is intended to examine appropriately governed customer records, language, and behavioral evidence for recurring motivations, objections, and decision patterns. The goal is better audience hypotheses and more useful targeting, not a diagnosis or a claim to know what is in someone’s head.
For the conversation you can’t afford to waste.
Before a pitch, sales call, negotiation, or interview, Boswell can assemble a source-backed briefing on the person and the context: their stated priorities, relevant history, communication patterns, likely questions, and the uncertainties that still need to be resolved.
Boswell is built around a simple rule: no unsupported finding should quietly become a confident statement. The engine separates source material, interpretation, criticism, and the action that follows.
Language can support hypotheses about a person’s expressed priorities and communication habits. It cannot establish hidden motives, clinical traits, or future behavior with certainty. Boswell’s outputs must be read with their sources and limitations attached.
For the B2B outreach application, I tested the same writing model with and without Boswell’s research and decision process. The engine’s version was preferred in all six blind assessments across three executives by two independent AI judges.
The useful finding wasn’t just the result. When we deliberately gave the engine the wrong communication approach, its advantage disappeared. Understanding how to speak to a person mattered more than simply adding detail.
Read the complete white paper ↗Controlled internal study of B2B outreach only. Three prospects, one offer, AI judges rather than human buyers. This is evidence of performance in that test, not proof of real-world reply or sales lift, and not validation of all four applications.
People leave a remarkable record in what they write, publish, explain, defend, and refuse. Most systems skim it for keywords and titles. Boswell was designed to read it much more closely, preserve what can be verified, and make that research useful.
I’m Atma Degeyndt, a marketer who can code. Boswell began as an experiment in better B2B outreach and developed into a broader research engine for understanding people, constructing expert knowledge, and improving consequential conversations.
More about my work →A prospect list, a body of expert work, an audience, or one important conversation.