Pre-seed stage, looking for collaborators and funding.
Ours is open source. Think of us as an ERP for documents, LLM prompts, and extraction schemas. We run on top of litellm, as a portability layer, so we support all major LLM models.
Extraction schema can be configured though a drag-and-drop UI, or inline by editing JSON.
A tagging mechanism is used to determine which prompts run on which documents - so we don't run all prompts against all documents, which would be a quadratic problem.
APIs are available for all functions (upload docs, configure prompts & schemas, download results).
We are designed for human-in-the-loop workflows, where precise processing of financial, insurance, or medical data is essential.
We see two main use cases, right now:
1 - Accelerating AI adoption in other engineering organizations, who don't have time to build the AI pipelines in-house. In this use case, we can quickly develop a specialized UI for you (Lovable, Bolt + adapting the generated UI with Cursor for your use case). In this play, we are a data layer accelerator for your AI solution.
2 - Solving point problems in document processing in insurance, medical, biotech, revenue cycle management, supply chain... In this use case, the business pain point we solve is manual processing of documents in an ERP that may not have the latest AI features. DocRouter.AI sits inline, in front of the ERP, picking selected faxes, emails, docs - processing them with LLMs, and inserting structured data into your ERP, saving on human labor.
The 2nd use case is something we see again and again in the industry. Legacy ERP vendors are slow to adopt AI processing, and businesses sitting on top of an ERP find it prohibitive to switch ERPs. These businesses are nickel and dimed over any small new ERP feature (...want to support PDFs not just TIFFs? that's thousands of dollars!... want to call APIs into the ERP? that's charged per API call!...)
They desperately need solutions to solve business workflows with AI, to free up FTEs to do more interesting work.
Here is a 30m recorded talk from a Mindsone meetup: https://community.mindstone.com/annotate/article_AuDOhLA5awW... where I showed how DocRouter.AI can be used to grade middle school quizzes with AI, with a teacher-in-the-loop. This was a "1st use case" application, with a custom UI, specialized to the application.
For the grade-school-quizzes-with-AI application, we generated the quiz rubric synthetically with AI, as we did the student quizzes. The rubric is embedded in the LLM prompt. The quiz PDF is tagged with the same tag as the corresponding rubric prompt (so it's graded with the corresponsing rubric).
This idea of matching a quiz agains a quiz rubric comes up again and again in many other examples. The same mechanism can be used to:
- Match invoices with purchase orders
- Or, to verify invoices against allowed amounts in a contract.
- Or, to check if standard operating procedures for transportation security comply with government or insurance rules.
- Or, to check if medical documents comply with a set of insurance rules. This is a use case I developed over a year and a half in the Durable Medical Equipment space, as consulting work (and it inspired the design of the DocRouter as a more general solution).
The idea of a system just keeping track of prompts, extraction schemas and documents - while very simple, it can solve many problems, in different verticals.
In fact, I believe that, when multiple products can solve the same problem, it is the simplest product that has the best chance to succeed.
So, a lot of thinking goes into keeping the design simple, the APIs complete - removing unnecessary artifacts. If new features are needed, they can be added as an external block, so the central function of the DocRouter does not need to become cluttered.
(I did not know how to program React before this... but in the brave new world of Cursor and Windsurf editors, I can venture into bold new directions!)
Ping me if you are interested to collaborate, or just if you are interested in the space!
Our thesis is that the space is large enough, and there's a market for multiple players. We do specialize on business workflows with human-in-the-loop, and we offer consulting services for project integration / turnkey delivery.
I am the founder of http://DocRouter.AI, https://github.com/analytiq-hub/doc-router. Available online as http://app.docrouter.ai (no paywall, working on Stripe integration).
Pre-seed stage, looking for collaborators and funding.
Ours is open source. Think of us as an ERP for documents, LLM prompts, and extraction schemas. We run on top of litellm, as a portability layer, so we support all major LLM models.
Extraction schema can be configured though a drag-and-drop UI, or inline by editing JSON.
A tagging mechanism is used to determine which prompts run on which documents - so we don't run all prompts against all documents, which would be a quadratic problem.
APIs are available for all functions (upload docs, configure prompts & schemas, download results).
We are designed for human-in-the-loop workflows, where precise processing of financial, insurance, or medical data is essential.
We see two main use cases, right now:
1 - Accelerating AI adoption in other engineering organizations, who don't have time to build the AI pipelines in-house. In this use case, we can quickly develop a specialized UI for you (Lovable, Bolt + adapting the generated UI with Cursor for your use case). In this play, we are a data layer accelerator for your AI solution.
2 - Solving point problems in document processing in insurance, medical, biotech, revenue cycle management, supply chain... In this use case, the business pain point we solve is manual processing of documents in an ERP that may not have the latest AI features. DocRouter.AI sits inline, in front of the ERP, picking selected faxes, emails, docs - processing them with LLMs, and inserting structured data into your ERP, saving on human labor.
The 2nd use case is something we see again and again in the industry. Legacy ERP vendors are slow to adopt AI processing, and businesses sitting on top of an ERP find it prohibitive to switch ERPs. These businesses are nickel and dimed over any small new ERP feature (...want to support PDFs not just TIFFs? that's thousands of dollars!... want to call APIs into the ERP? that's charged per API call!...)
They desperately need solutions to solve business workflows with AI, to free up FTEs to do more interesting work.
Here is a 30m recorded talk from a Mindsone meetup: https://community.mindstone.com/annotate/article_AuDOhLA5awW... where I showed how DocRouter.AI can be used to grade middle school quizzes with AI, with a teacher-in-the-loop. This was a "1st use case" application, with a custom UI, specialized to the application.
For the grade-school-quizzes-with-AI application, we generated the quiz rubric synthetically with AI, as we did the student quizzes. The rubric is embedded in the LLM prompt. The quiz PDF is tagged with the same tag as the corresponding rubric prompt (so it's graded with the corresponsing rubric).
This idea of matching a quiz agains a quiz rubric comes up again and again in many other examples. The same mechanism can be used to:
- Match invoices with purchase orders
- Or, to verify invoices against allowed amounts in a contract.
- Or, to check if standard operating procedures for transportation security comply with government or insurance rules.
- Or, to check if medical documents comply with a set of insurance rules. This is a use case I developed over a year and a half in the Durable Medical Equipment space, as consulting work (and it inspired the design of the DocRouter as a more general solution).
The idea of a system just keeping track of prompts, extraction schemas and documents - while very simple, it can solve many problems, in different verticals.
In fact, I believe that, when multiple products can solve the same problem, it is the simplest product that has the best chance to succeed.
So, a lot of thinking goes into keeping the design simple, the APIs complete - removing unnecessary artifacts. If new features are needed, they can be added as an external block, so the central function of the DocRouter does not need to become cluttered.
Here are tech slides from my Boston PyData presentation, where I showed how DocRouter.AI was implemented, using React, NextJS, FastAPI, and with a MongoDB back end: https://docs.google.com/presentation/d/14nAjSmZA1WGViqSk5IZu...
(I did not know how to program React before this... but in the brave new world of Cursor and Windsurf editors, I can venture into bold new directions!)
Ping me if you are interested to collaborate, or just if you are interested in the space!
Our thesis is that the space is large enough, and there's a market for multiple players. We do specialize on business workflows with human-in-the-loop, and we offer consulting services for project integration / turnkey delivery.
Andrei Radulescu-Banu, [email protected]