Executive Summary

Enterprises building multi-turn LLM agents and conversational systems routinely suffer from ambiguous deictic references—“it”, “that order”, “the previous price”—which drive incorrect retrievals, hallucinations, and wasted tokens across support, sales, and internal knowledge workflows. This problem is especially acute for mid-to-large firms (roughly 200,000 potential customers) that spend an average $175K/year on conversational/AI customer-experience software, yielding an addressable market of about $35.0B. You could build an API middleware that performs automatic conversational coreference resolution and referent grounding before retrieval or generation, exposing simple endpoints and SDKs to normalize pronouns and deixis into concrete entity IDs, document spans, or vector DB queries. The product would plug into RAG pipelines and major vector databases, emit deterministic resolved referents to reduce on-model ambiguity, and offer on-prem or hybrid deployment options for compliance and latency-sensitive use cases. In practice this can plausibly cut token usage and context-related errors materially — for many workflows we estimate 10–40% lower token volume and a comparable drop in deictic hallucinations depending on dialog complexity. The timing looks attractive: enterprises are replacing scripted bots with LLM-led agents, teams are separating retrieval and reasoning (creating room for middleware), and token-cost sensitivity pushes buyers toward pre-model disambiguation; the opportunity scores 93/100 for market attractiveness and 88/100 for revenue potential, with medium competition. To stand out you’ll need best-in-class precision and latency, comprehensive connectors (vector DBs, CRMs, chat platforms), clear ROI metrics, and privacy-first deployment options, while acknowledging real challenges around integration complexity, hard edge cases in referent resolution, and the risk that large LLM vendors or open-source projects incorporate similar features.

Executive Summary

Market Opportunity

Deictic-resolution for chatbots — automatic coreference API for conversations targets a $35.0B = 200,000 mid+large enterprises x $175K average yearly spend on conversational/AI/customer-experience software total addressable market with medium saturation and a year-over-year growth rate of 22% (conversational AI & CX automation market growth).

Key trends driving demand: LLM-led automation -- enterprises replacing scripted bots with multi-turn LLM agents, increasing need for robust context handling; RAG & vector DBs -- teams increasingly separate retrieval and reasoning, enabling middleware to insert resolved referents before generation; Token-cost sensitivity -- businesses prefer pre-processing ambiguity off-model to save tokens and reduce hallucinations; Multimodal conversation -- references span text, images, and attachments, increasing the complexity of deictic resolution and the value of specialized middleware.

Key competitors include Hugging Face, Stanford CoreNLP / Academic coreference tooling, LangChain (and similar orchestration frameworks), Rasa.

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Deictic-resolution for chatbots — automatic coreference API for conversations

Executive Summary

Deictic-resolution for chatbots — automatic coreference API for conversations

Executive Summary

Market Validation

Market Opportunity

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