Conversational
AI Systems
Three areas of focus
Each service addresses a distinct phase of building a conversational AI system — from the structural decisions made before writing a line of code to the ongoing work of keeping a deployed system accurate and reliable.
Architecture & Dialogue Design
Before any model is trained, the conversation structure needs to be mapped — intents, entities, fallback handling, and multi-turn context. Getting this wrong early creates technical debt that compounds through every later sprint.
Discuss your projectNLP Pipeline Development
Building a pipeline that works in a notebook is different from one that holds up under real user input. Work covers tokenization choices, classification tuning, context window management, and integration with your existing backend.
Discuss your projectLong-Term AI Mentorship
Structured sessions over months, not a single workshop. Each engagement is built around your specific project — weekly reviews, async feedback on pull requests, and direction on architectural decisions as the system grows.
Discuss your projectFrom first call to working system
Most clients arrive with a use case in mind but uncertainty about the technical path. The process starts with understanding what you are actually building — not a generic chatbot, but a system with specific inputs, outputs, and failure modes worth thinking about carefully.
Scoping session
A focused call to map your use case, existing stack, and realistic timeline. No sales pressure — just an honest assessment of what the project involves.
Architecture review
Before building, the conversation structure and data flow are documented. This step catches assumptions that would otherwise become bugs in production.
Sprint-based development
Work progresses in short cycles with a review at each stage. Sessions cover what was built, what broke, and what the next sprint should prioritise.
Testing under real input
Synthetic test cases miss what real users actually type. The testing phase uses realistic input variation to surface edge cases before deployment.
Deployment and handoff
The system ships with documentation you can actually use — not auto-generated comments, but notes on the decisions made and the places most likely to need attention later.
Things clients ask before starting
Practical answers to the questions that come up most often during the first conversation.
Remote by design, not by compromise
Cerevyny has operated as a fully remote service since 2017. Sessions run over video call, code reviews happen asynchronously, and documentation is shared through tools you already use. Geography has never limited who can participate.
About Cerevyny