AI & ML
Automating Invoice Processing for Pacific Financial
Pacific Financial deployed a high-capacity parsing system to extract transaction records from complex tables.
99.1% Extraction Precision
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Claims data normalization
Line-Level Claims. One Claim-Level Answer.
A claim can arrive with several lines, and a field like admission type is sometimes coded inconsistently across them. This "votes" across a claim's lines — the most-frequent value wins — to pick one claim-level answer, then classifies each line-level field into valid / invalid / null, exactly the three-bucket split a real data-quality pipeline uses.
The voting logic, the valid/invalid/null classification, and the UB-04 admission type codes below are copied from an actual open-source healthcare-data dbt project's normalization and data-quality models — including a real quirk: its paid-amount check only has valid/null buckets, so a negative amount still counts as "valid" there. The NPI/ICD-10 reference lists here are a small illustrative sample, not the full real terminology tables. Runs entirely in JavaScript — no model download.
Column headers just need to resemble the sample below (e.g. "ClaimID" or "Claim Number" both work) — exact wording isn't required.
Claim-level voting (admission type)
Line-level data-quality buckets
Claim Line Billing NPI Diagnosis Paid amount
Data-quality summary
Field Valid / total Filled / total
Case Studies
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Technology
AI-Powered Resume Screening at TalentForge
TalentForge integrated neural search embeddings to screen millions of developer candidate portfolios accurately.
82% Time-to-Hire Saved
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Vervelo is a digital-health software partner blending deep clinical insight with world-class engineering to build tailored, secure, interoperable healthcare platforms.
Benefits of custom software solutions
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Customize and integrate seamlessly
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On-demand scalability is always possible
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Do you provide custom CRM solutions?
Yes, we design and develop fully custom CRM solutions tailored to your workflows.
Can these AI solutions run on our own infrastructure?
Yes. Every solution on this page is built to run inside your environment — on your machines, with your data staying where it is.
How long does a typical engagement take?
Most pilots go live in four to six weeks, with production rollout following once the results are validated against your data.
Do you fine-tune models on our data?
We do, where it earns its keep. Many of these solutions start from open models and are tuned against your domain data.
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