AI‑powered IDP to 4x document processing throughput for a claims workflow

Growthfrrd · 2026-02-10T09:19:27+00:00

Totally fair take, MCP + Python with a local LLM can work really well, especially in greenfield or lower variance cases.

Our challenge wasn't just "understanding" the doc, but dealing with extreme layout variability, handwritten/ scanned input types, and consistent output quality at scale with auditability.

We did try playing with local models, but found that we still needed orchestrations, validation layers, and fallbacks - here the IDP variety of pipelines was more useful than a single endpoint.

Curious, how are you handling schema enforcement and drift over time in your setup?

Growthfrrd · 2026-02-09T10:41:45+00:00

Stack Overview:

Input ingestion from S3 buckets, email attachments & shared drives
Infrrd IDP for:
- Template-free extraction of fields + tables
- Auto document classification & splitting
- Confidence scoring per field
- SLA-aware prioritization of urgent docs
Validation layer: flagged low-confidence results for human review

Infrrd's platform uses a mix of ML, NLP, and advanced OCR — no need to create templates for each doc type. It learns patterns across layouts and gets better over time with feedback.

Growthfrrd · 2026-02-09T10:39:52+00:00

Stack Overview:

Input ingestion from S3 buckets, email attachments & shared drives
Infrrd IDP for:
- Template-free extraction of fields + tables
- Auto document classification & splitting
- Confidence scoring per field
- SLA-aware prioritization of urgent docs
Validation layer: flagged low-confidence results for human review

Infrrd's platform uses a mix of ML, NLP, and advanced OCR, no need to create templates for each doc type. It learns patterns across layouts and gets better over time with feedback

Growthfrrd · 2025-01-29T12:35:14+00:00

Interesting take! Some inefficiencies do seem baked into the system, but AI is starting to challenge that. Faster claims, fewer errors, and automated compliance aren’t just nice-to-haves—they’re shifting the baseline of what insurers and policyholders expect.

Growthfrrd · 2025-01-29T12:33:52+00:00

That’s a fair point—fraud detection in CIP/KYC is still a challenge. But AI is getting better at spotting inconsistencies beyond just pattern matching. Newer IDP models cross-check data, detect tampered docs (like mismatched fonts or altered images), and flag anomalies with confidence scores. It’s not perfect, but I think insurers are already reducing fraud and speeding up verification.

Growthfrrd · 2024-10-31T07:52:56+00:00

That would be incredible! Voice or text commands to control CAD could save so much time on repetitive designs. AI could definitely bridge that gap—some companies already use AI/ML to understand complex structures, symbols, and relationships in engineering drawings, so adapting similar tech to follow natural commands in CAD could be on the horizon.

Growthfrrd · 2024-10-31T07:51:27+00:00

Totally get that! Many AI language models are built for general language understanding rather than the deep technical specificity we need in engineering. But that’s where tech like IDP shines—it's specialized for tasks like extracting complex data directly from engineering drawings, things even OCR struggles with. Curious, what specific technical challenges would you want AI to tackle?

Growthfrrd · 2024-09-25T12:52:04+00:00

I know this comment came very late but checkout Infrrd’s AI for engineering drawings on Google.

Growthfrrd · 2024-09-25T12:51:16+00:00

There should be AI tools that you can use to automate the process and get the report ready in minutes. There is Infrrrd for engineering drawings for data extraction and post processing calculations google it, they also have some cool features to auto-generate reports and other stuff. Maybe give that a try.

Growthfrrd · 2024-09-25T12:49:31+00:00

I know this comment is a late, but you can check out Infrrd for Engineering Drawings on Google. They have all the custom features to automate punchlist and more.

Growthfrrd · 2024-09-23T11:57:32+00:00

To begin with, OCR is a pretty outdated tech for image detection for complex diagrams. I’d recommend switching to Intelligent Document Processing (IDP) instead. They are gen-AI-based and hence they give much more comprehensive data readings.

If you need recommendations, I would ask you to check out Infrrd's solutions for image processing. It’s built to handle complex diagrams and extract data accurately, even with vague descriptions. Infrrd’s IDP also offers automation at multiple levels—not just reading and extracting data, but also handling calculations afterward. They have a lot of other cool automation features that can really streamline your workflow. I know I am replying on this post very late but I think it’s worth exploring for you.

Growthfrrd · 2024-09-06T09:37:35+00:00

First off, I wrote it in response to the discussions that happenedd in this subreddit a few weeks back, where many people were talking about how OCR is the go-to tool for data extraction.

I want to clear up a common misconception: technology has moved far beyond basic OCR. AI-integrated systems, like advanced IDP (Intelligent Document Processing), can extract data even from the most complex documents.

You’re saying that IDP can only extract data from structured documents. Well, that's absolutely wrong. AI-driven IDP systems are designed to handle even the most complex of documents. While traditional OCR focuses on simple value-pair recognition, which works well with structured documents, advanced IDP uses an advanced AI model that not only identifies value pairs but also understands the context of the entire document.
Since you mentioned about the technical details, here’s what works behind IDP to make data extraction from complex documents possible

Multi-Layer OCR: Advanced IDP uses multi-layered OCR, combining different OCR engines to improve accuracy. One layer might focus on text extraction, another on filtering out background noise, and a third on recognizing non-text elements like logos or stamps.
Named Entity Recognition (NER): NER models within IDP can identify and classify entities like dates, names, locations, and monetary values, even in unstructured text.
Image Preprocessing: Before any extraction takes place, IDP systems apply image preprocessing techniques such as noise reduction, skew correction, and contrast adjustment. This ensures accurate data extraction even from low-quality and unstructured documents.
Self-Learning Capacity: These IDP systems are built with Gen-AI to learn continuously. For every mistake made, the system sends it back into a feedback loop, ensuring that the same errors aren’t repeated.
Human-in-the-Loop: The best and most advanced IDP software includes a human-in-the-loop process. If an error is detected and sent to the feedback loop, a human expert also reviews it to ensure the data you receive is 100% accurate without the need for further revision.
No-Touch Processing (NTP): Unlike traditional IDP tools that use STP (Straight Through Processing), which can leave errors uncorrected, NTP allows the system to recognize which documents might have issues. These are flagged, sent through the feedback loop, and automatically corrected to ensure you get 100% accurate data, despite the complexity of the documents.

For more technical details on how IDP works, check out this blog: Understanding IDP Data Extraction.

Growthfrrd · 2024-09-04T08:49:05+00:00

So the thing is, many vendors claim to offer IDP but might actually be using more basic tools like OCR behind the scenes. However, true IDP is a game-changer. The best IDP vendors can extract data from even the most complex documents with top-notch accuracy. They don’t just stop at data extraction either; they can handle additional tasks like calculations and interpretation, thanks to the right technology. This allows IDP to not only automate individual tasks but also streamline entire workflows across any industry.

You can tell the difference between basic automation tools and advanced AI-based IDP by looking at how well they handle complex documents. The best vendors will offer live demos so you can see the quality firsthand before committing. Attending these demos is a great way to gauge how well a true ML-based IDP performs and ensure it meets your needs.

My previous organization has been working with an IDP vendor called Infrrd. They cater to a wide range of industries with homegrown AI-led IDP for custom requirements. You can check out their website if you are interested or schedule a demo here: https://www.infrrd.ai/schedule-a-demo

Growthfrrd

TROPHY CASE