Many engineering teams, platform groups, and companies running assistant or RAG workflows are seeing token bills climb as prompts include large amounts of retrieved or historical context; with an estimated 2 million software teams and a $3K ACV average (total addressable tooling + cost-savings market of about $6.0B), token inefficiency is a tangible line-item pain. The problem is not just cost—irrelevant or stale context also increases latency and can degrade model outputs when models are forced to consider noisy inputs. A practical product would be a lightweight middleware plus SDK and IDE/CI plugins that dynamically prunes retrieved and conversational context to meet an adaptive token budget using a mix of fast heuristics (recency, type filters, semantic similarity thresholds) and learned rankers, with fallbacks and per-request cost/quality controls; it should expose telemetry showing per-call token and dollar savings and integrate with popular retrievers and vector stores. This is compelling now because LLM adoption in developer workflows is accelerating, retrieval-augmented architectures make the retrieval-vs-model-cost tradeoff explicit, and developers expect seamless plugins—giving a clear commercialization path to the $3K ACV target via tooling subscriptions and measurable cost reduction. To stand out you would emphasize deterministic, auditable pruning policies, per-tenant tuning, tight local/hybrid IDE integrations, and an ROI dashboard that proves 20–50% token savings on representative workloads. Be honest that risks include potential quality regressions from over-pruning, significant integration work across retrievers and models, and the need for strong case studies to close sales; a focused MVP with 50 pilot teams and clear saving metrics would be the fastest way to validate unit economics and product–market fit.