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Token-Optimized Context Router for LLMs
A middleware tool that acts as an intelligent bridge between user knowledge bases and AI models. It dynamically filters and summarizes context based on the user's specific prompt to prevent context-window bloat and minimize API token costs.
Why this matters
When you try to give an AI the full picture by connecting your entire knowledge base, the resulting prompts become massively bloated. You watch helplessly as your API bills skyrocket because you are sending thousands of irrelevant tokens with every single query. Even worse, burying the LLM in excessive documentation causes it to lose focus and hallucinate, missing the actual question hidden in the noise. You are forced to manually prune and summarize your own documents, defeating the purpose of automation. You need a smart router that reads your prompt, surgically extracts only the strictly necessary background data, and delivers a concise package to the AI.
- · Built for Developers building AI applications and power users utilizing API-based LLM interfaces..
- · Most likely monetization: Usage-based or tiered SaaS.
The Pain · Narrative
When you try to give an AI the full picture by connecting your entire knowledge base, the resulting prompts become massively bloated. You watch helplessly as your API bills skyrocket because you are sending thousands of irrelevant tokens with every single query. Even worse, burying the LLM in excessive documentation causes it to lose focus and hallucinate, missing the actual question hidden in the noise. You are forced to manually prune and summarize your own documents, defeating the purpose of automation. You need a smart router that reads your prompt, surgically extracts only the strictly necessary background data, and delivers a concise package to the AI.
Score Breakdown
Market Signal
Go-to-Market
Independent AI engineers building custom agent workflows who are highly sensitive to OpenAI/Anthropic API costs.
~50,000 to 100,000 active AI application builders.
Dev-tool directories and technical content marketing (blog posts on token optimization).
$29/month for up to 10M tokens processed
20 paying developers using the API in their production workflows.
MVP Scope · 1–2 weeks
- Set up a vector database instance (e.g., Pinecone).
- Build a Python script to chunk and embed sample text documents.
- Create an API endpoint that accepts a user prompt and queries the vector database.
- Implement basic text retrieval based on cosine similarity.
- Write a basic test suite to verify retrieval accuracy.
- Integrate an LLM step to summarize the retrieved chunks before returning them.
- Add a token-counting library to measure input and output sizes.
- Build a simple dashboard displaying tokens saved versus sending the whole document.
- Wrap the core logic into an easy-to-install npm/pip package.
- Draft a technical blog post demonstrating the cost savings of the tool.
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1The cost of API tokens drops so rapidly that optimization becomes economically irrelevant.
- 2Native caching mechanisms introduced by major AI providers (e.g., Anthropic Prompt Caching) solve the cost issue natively.
- 3The latency added by the embedding and retrieval step creates a sluggish user experience.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Commenters expressed specific concerns about dumping entire vaults of data into AI tools, noting the potential to nuke context windows and skyrocket token costs. Others worried about the system's ability to handle massive files over time without automatic compression. The community explicitly recognizes that simply forwarding all data to an LLM is unsustainable, indicating a strong desire for intelligent, prompt-specific data filtering.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Validate
Promising signals, but needs confirmation. Create a landing page, collect email sign-ups, then decide.
Landing Page Copy Kit
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
Token-Optimized Context Router for LLMs
Sub-headline
A middleware tool that acts as an intelligent bridge between user knowledge bases and AI models. It dynamically filters and summarizes context based on the user's specific prompt to prevent context-window bloat and minimize API token costs.
Who It's For
For Developers building AI applications and power users utilizing API-based LLM interfaces.
Feature List
✓ Semantic similarity filtering to inject only relevant data ✓ Token cost estimation and usage dashboard ✓ Auto-summarization of large legacy context files ✓ API endpoint for programmatic context retrieval
Where to Validate
Share your landing page in r/Product Hunt · artificial-intelligence — that's exactly where these pain points were discovered.
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