Executive Summary

Knowledge worker teams, developer orgs, and companies productizing autonomous agents increasingly face runaway per-request inference costs and duplicated computation across workflows. Across a $120B addressable market—200 million knowledge workers with an estimated $600/year potential spend on agent tooling—these costs compound for teams running many agents and retrievers in production. You could build a cost-aware agent orchestration layer that learns and amortizes compute by capturing intermediate traces, caching reusable subtasks, routing requests to progressively cheaper model paths, and proactively precomputing recurring answers into vector memory. Core capabilities would include a trace repository, a cost-optimizer that picks model/precision/latency tradeoffs, SLO-aware precomputation and cache eviction policies, plus SDKs to instrument agents and an analytics console that shows dollars saved. Integrations with open/distilled model endpoints and in-cluster fine-tuning would let the system shift inference from expensive LLMs to distilled specialists as patterns emerge. The timing is favorable: agentization is accelerating demand for orchestration, efficient fine-tunable models are dropping inference costs, and production-ready vector search makes cached reuse practical—factors that make $120B of potential spend addressable today. This product can stand out by optimizing for cost as the primary signal, building model-agnostic hooks for live learning of amortization graphs, and locking in customers through measurable ROI, but realistic challenges include needing sufficient scale and sensitive telemetry to learn effectively, potential competition from cloud vendors bundling similar features, and the upfront effort to prove savings across diverse enterprise workflows. If you can secure early pilots with measurable cost reductions and maintain neutral integrations with model providers, pursuing this is sensible; without early data and anchor customers, the idea is promising but risky.

Executive Summary

Market Opportunity

Cut AI task costs over time — an agent that learns & amortizes compute targets a $120B = 200M knowledge workers x $600/yr potential spend on AI-agent tooling and automation total addressable market with medium saturation and a year-over-year growth rate of 35%.

Key trends driving demand: Agentization -- workflows are being wrapped by autonomous agents, increasing demand for orchestration and lifecycle tooling; Open/efficient models -- cheaper fine-tunable and distilled models let teams push inference to low-cost paths; Vector search + memory -- production-grade retrieval makes cached/reused knowledge practical, lowering repeated compute; LLMops commoditization -- more standardized tooling accelerates adoption but increases need for differentiation.

Key competitors include LangChain, LlamaIndex (previously GPT-Index), Pinecone, OpenAI (ChatGPT / GPTs / API), Deepset (Haystack & Haystack Cloud).

View Plans

Cut AI task costs over time — an agent that learns & amortizes compute

Executive Summary

Cut AI task costs over time — an agent that learns & amortizes compute

Executive Summary

Market Validation

Market Opportunity

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