Promptcore’s technology drives products like ContentEngine. We build event-driven automated processes. AI doesn’t have to mean giving orders to a chat assistant. We use data from live events (reports, social media, web events) to trigger bespoke automated processes.
Coming from a background in algorithmic marketing, we think the applications of AI extend beyond chat assistants and basic content generation. In the coming years, AI will be critical in re-building the automated processes that occur in the backend of today’s business systems.
For years, one of the greatest struggles in process automation was that it’s difficult to deal with subjective data. Dates, numbers, and analytics KPIs are easy for machines to deal with, but it’s been historically difficult to build a system to summarize user feedback, or a business report. Machines were bad at performing semantic analysis and reasoning tasks.
The result is that automated systems have all been built around this weakness. A marketing algorithm might collect and calculate client KPIs, but humans are needed to associate changes in those KPIs meaningfully, and decide an appropriate course of action. An algorithm can tell you user engagement and sales have dropped, but not why. You needed a person to correlate that to sentiment changes in product reviews, or other semantic data points.
Re-thinking and re-engineering existing automated processes to better incorporate the semantic reasoning abilities of Large Language Models is an enormous task facing the world in the coming years, and we’ve just begun to scratch the surface.
Building reliable, scalable agentic systems is critical for products like ContentEngine. While powerful, language models can be prone to mistakes and hallucinations. In large part, this lack of reliability is a result of the extremely wide range of tasks and questions people pose to chat assistants. Language is ambiguous. The kind of information people want in their answer can differ greatly. These are enormous challenges.
Promptcore builds reliable automated systems by breaking a specific process down into manageable and repeatable steps. Our systems are event-driven, engineered workflows that perform the same kind of task every time. This allows us to rigorously test and optimize things like prompt phrasing and agent alignment using real-world inputs. Our systems don’t hallucinate, and they don’t need a human to initiate them. They just work.
