Industries · InsurTech
Claims documents, turned into structured data
Contracts, emails, and demand letters in PDF and DOCX, with the fields your claims workflow needs extracted, labeled, and quality-checked by people.
Overview
Turning claims paperwork into data you can automate on
Insurance runs on documents. Injury and property claims move between law firms and carriers as contracts, emails, demand letters, and supporting paperwork — some scanned to PDF, some exported to DOCX, almost none in a consistent layout. InsurTech companies use AI to pull the facts that matter out of those documents and move claims along, so less time goes to administrative back-and-forth and settlements happen faster and more fairly.
The hard part is the extraction. A field that sits in a table on one firm’s letter is buried in a paragraph on another’s, and a scanned page adds noise of its own. Pulling fields by hand is slow, expensive, and error-prone, but an extraction model has to be trained on accurately labeled examples, and its output has to be reliable enough to automate on. An automated claims workflow is only as good as the fields that feed it.
MLtwist combines AI extraction with human review. Models are trained to find the fields you need across document types, people label and check every extraction, and the results are cleaned and structured to drop into your workflows. Documents stay in your own cloud storage, encrypted at rest, and each carries a Data ID Card recording where it came from and who reviewed it — see data provenance for what that record covers.
Bring MLtwist in when you’re building or improving an extraction model, when manual processing is holding up claims, or when your team needs structured data it can trust without re-checking. For one InsurTech company working between plaintiff firms and carriers, it replaced a slow manual process across 13 key fields.
The problem
What makes insurtech data hard
Unstructured inputs
Scanned PDFs and exported DOCX files arrive in every layout, from many law firms and carriers. The same field can sit in a table on one letter and the middle of a paragraph on the next, and scanned pages add noise of their own.
Manual extraction
Pulling fields by hand is slow, costly, and error-prone. Every document waiting in a queue holds up a claim, and every mistyped field starts another round of back-and-forth between firm and carrier.
Accuracy you can automate on
Downstream workflows only work if the extracted fields are right. An extraction model that's merely close still needs a person to check its output, which defeats the point of automating.
How it works
From a claims document to structured fields
Models do the first pass at scale. People make sure the fields are right before anything downstream relies on them.
- 01
Ingest every format
Scanned PDFs and exported DOCX files from many firms and carriers are taken in and normalized, so every document enters the same process. Files stay in your own cloud storage throughout.
- 02
Extract with models
AI models are trained to identify and extract the key fields across document types — 13 in one program. The models handle volume, so people spend their time checking fields rather than searching for them.
- 03
Label and review
People label and check every extraction before it counts as done. Errors are corrected here, not discovered later inside a claims workflow.
- 04
Clean and deliver
Extracted fields are cleaned, structured, and quality-checked, then delivered in a form that drops straight into your claims and settlement systems. No reformatting is needed on your side.
Case study · InsurTech company
Faster settlements from structured claims documents
An InsurTech company that streamlines communication between plaintiff law firms and insurance carriers needed to pull 13 key fields from contracts, emails, and repair demand letters — scanned PDFs and exported DOCX files in every layout. Doing it by hand was slow, costly, and error-prone, and it stalled settlements.
MLtwist deployed a tailored AI data pipeline. Models were trained to identify and extract the 13 fields from a variety of unstructured documents, every extraction went through a rigorous labeling process, and the results were cleaned, structured, and quality-checked before delivery into the company's automated workflows.
Extraction and processing ran 50% faster than manual methods, labeling accuracy reached 97%, and operational costs fell 44%. With the bottlenecks gone, the company could scale more efficiently, serve its customers faster, and put the resources it saved toward other strategic work.
- labeling accuracy
- 97%
- lower operational costs
- 44%
What you get
Fields you can automate on
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Structured fields
The fields you need, extracted and cleaned into a consistent structure. The same field comes out the same way whichever firm or carrier sent the document.
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Human-checked accuracy
Every extraction is labeled and reviewed before delivery. In one program, labeling accuracy reached 97%.
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Ready for your workflow
Output is shaped to drop into your claims and settlement systems. Your team works with the data instead of reformatting it.
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A Data ID Card
Each document carries a record of where it came from and who reviewed it. That record matters when the data feeds decisions about real claims.
More work
Related programs
FAQ
Common questions
Which documents can you process?
Contracts, emails, repair demand letters, and other claims documents, as scanned PDFs or exported DOCX files. Documents from many firms and carriers, in different layouts, go through the same process.
Which fields do you extract?
The fields your workflow needs, defined with you before extraction starts. In one program, models extracted 13 key fields from each document, and every extraction was labeled and reviewed.
Do people check the output?
Yes. Every extraction is labeled and reviewed before delivery, so errors are caught before they reach your claims workflow. In one program, labeling accuracy reached 97%.
What does it save?
For one InsurTech company, processing ran 50% faster than manual methods and operational costs fell 44%. Pricing is custom, typically per file, based on data type and complexity, volume, quality requirements, and timeline.
Where are sensitive documents stored?
In your own cloud storage — Google Cloud Storage, Amazon S3, or Azure Blob — encrypted at rest, with role-based access. Each document's Data ID Card records who reviewed it.
Bring us your insurtech data
Tell us the data type, volume, and timeline. We'll scope it with our team, yours, or both — and deliver it versioned, in your format.
Also available through Carahsoft and Google Cloud Marketplace.