Reduce LLM context bloat with automated memory curation and compression

Executive Summary

Many engineering and product teams building retrieval-augmented systems are seeing context bloat - long chat histories, duplicated documents, and noisy retrievals that drive up latency and token costs while hitting finite model windows. This problem is most acute at companies that run many fine-tuned agents or knowledge workflows, where small inefficiencies compound across hundreds to thousands of daily LLM calls. You could build an automated memory curation and compression platform that ingests traces and corpora, applies semantic deduplication, policy-driven pruning, and learned summarization to produce compact context slices, and exposes token-aware APIs and connectors to common vector stores. Aim for configurable policies and an audit trail, with typical token reductions of 2x-5x depending on workload, plus tools for human-in-the-loop validation and rollback to mitigate information loss. The timing is favorable because RAG adoption is accelerating and enterprises still face hard context window limits and rising token bills, creating strong incentives to pay for smarter memory management. With an addressable market of roughly $20.0B calculated as 2M developer teams at $10K ACV, and high market and revenue scores, there is a large, paying customer base actively looking for cost and performance improvements. To stand out you will need domain-aware compression models, tight integrations with popular vector DBs and LLM platforms, and enterprise features like on-prem execution and strong auditability. Key challenges are avoiding recall loss and keeping up with model and embedding changes, and competition from vector-store vendors and LLM platforms that may add similar capabilities, so focus on measurable ROI, safety guards, and easy integration to win early customers.