Finance
· Identity AI
KYC Document Verification for Global Bank
Automatically validating national IDs and international passports at branch registration points.
Encounter grouping across sources
Two Claims, Two Sources, One Visit.
A single ED visit often produces two separate claims — an institutional (facility) claim and a professional (physician) claim — and today those might arrive as a claims flat file from one system and a FHIR feed from another. This runs claims from both source shapes through the same chaining rules for deciding which claims belong to one emergency-department encounter: an exact same-day/same-facility match, a next-day transfer, a general date overlap at the same facility, or — since professional claims don’t carry facility or discharge fields — a looser overlap-or-adjacent-day rule when either claim is professional.
The four chaining rules are copied from an actual open-source healthcare-data dbt project’s real emergency-department encounter-grouping model, applied here with a union-find over the same pairwise conditions instead of that model’s original row-closure SQL — same rules, same resulting groups. The FHIR shape uses the standard public FHIR Encounter resource fields, not that project’s own FHIR-ingestion connector code (which lives in separate repositories this build didn’t have access to). Worth flagging directly: that project’s real connectors cover claims flat files, FHIR feeds, EHR database extracts, and ADT feeds — no CCDA support was found anywhere in its actual code, so it’s left out here rather than invented. Runs entirely in JavaScript — no model download.
Claims file (Source A)
Headers just need to resemble the sample below — exact wording isn’t required.
FHIR Encounter feed (Source B)
Standard FHIR Encounter resources — id, subject, class and period are the fields this reads.
Grouped encounters
Case Studies
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Technology
· Infrastructure
Real-Time Anomaly Detection at CloudNet
Monitoring millions of concurrent packets to identify distributed system threats before they escalate.
Healthcare
· Computer Vision
Medical Imaging AI for RadiologyPlus
Empowering radiologists with pre-processed clinical segmentations of scans in milliseconds.
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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You fully own IT consulting and software delivered
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You get a highly personalized solution
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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.
How do we get started?
Send us a note through the form above and we will set up a working session with the engineers who would build it.