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Udit Mitalleft a comment
Great question. This is exactly the problem a dedicated SLM solves better than a frontier model. A general-purpose LLM handling inconsistent PDF layouts is guessing. It has no prior knowledge of your specific document structures, your supplier formats, your table conventions, or your validation rules. Every document is a cold start. A NeoSmith SLM trained on your extraction workflow is...
We will build a free custom AI model for your agentic workflow. Looking for 3 teams.
Udit MitalJoin the discussion
Udit Mitalstarted a discussion
We will build a free custom AI model for your agentic workflow. Looking for 3 teams.
We are building NeoSmith and we are looking for 3 teams to work with right now at no cost. Here is the deal in one line: point your LLM to ours and we automatically create a dedicated Small Language Model for your exact workflow. Nothing from your end. No dataset, no labeling, no fine-tuning work, nothing. You just keep running your agents the way you already do. What actually happens is this....
Udit Mitalleft a comment
Hey PH 👋 Udit here, co-founder of NeoSmith. Quick story on why we built this. I was watching teams spend $40k/month running GPT-5 on workflows that did literally one thing: extract structured data from documents. Same input shape. Same output shape. 50,000 times a day. Nobody wanted to fix it because "fine-tuning takes months and we don't have the dataset." That's the problem NeoSmith solves....
NeoSmith AICustom SLM for AI Agents: 40–55% cheaper, 3–5x faster
Neosmith trains a custom Small Language Model from your LLM interaction logs. The SLM handles 80–90% of agent tasks at 40–55% of the cost and because it's trained on your workload, accuracy goes up too.
One endpoint swap. No MLOps.
NeoSmith AICustom SLM for AI Agents: 40–55% cheaper, 3–5x faster