Equmenopolis builds conversational AI that tracks intent, retains context across sessions, and responds like it was paying attention.
Equmenopolis maps every utterance to a structured intent graph. Ambiguous phrasing resolves against prior turns, not just the last message.
Each session builds a persistent context object. References to earlier topics resolve correctly even after topic switches.
Responses are conditioned on the full dialogue state - not a sliding window. The model knows what it already said and why.
Structured intent mapping with entity linking. Handles multi-intent utterances and intent correction mid-dialog.
Persistent dialogue state across messages. Coreference resolution handles pronouns and implicit references accurately.
Dialog management for complex flows: clarifications, confirmations, corrections, and graceful topic transitions.
Single endpoint for dialog management. JSON in, structured response out. Latency under 80ms at P95 for standard turns.
Turn-level metrics on intent confidence, fallback rates, and context hit ratios. Identify exactly where conversations break.
SOC 2 Type II certified. Data never used for training. Context objects encrypted at rest with AES-256. EU and US regions.
Handle multi-step support workflows where users describe problems across several messages. Resolve tickets faster without forcing users to repeat context after a handoff.
Build Q&A bots that remember which products or topics a user already asked about. Stop answering the same question from a cold start every turn.
Guide users through configuration steps that depend on earlier choices. The dialog engine tracks what was confirmed and what needs follow-up.
Developer plan. No card required. Context up to 20 turns per session.
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