Background: A 2,400-Person Medtech Firm in Austria
This case study is a composite drawn from patterns Forfis has observed across multiple engagements in healthcare and medtech. We do not name real clients. The company described here is a mid-sized Austrian medtech firm with roughly 2,400 employees, operating in the DACH region and serving hospital networks in Austria, Germany, and parts of the UK. It sells diagnostic equipment and consumables, and its customer support team handles order confirmations, shipment tracking, and return requests. The support stack is a mix of a legacy helpdesk, an ERP for order management, and a CRM for account records. The company is not a digital-native; its IT team maintains the existing systems but has no in-house AI capability. The trigger for change was a 22 percent year-over-year increase in support ticket volume, driven by a new product line and a shift toward direct-to-hospital sales. The support team of 34 agents was already at capacity, and first-response times had drifted past the 4-hour internal target.
The Challenge: 4.2-Hour First Responses and a HIPAA Constraint
The core problem was not a lack of agents but a lack of speed in the first step: reading the inbound document, extracting the relevant fields, and drafting a response. Each ticket arrived as a PDF or scanned image, often a mix of an order confirmation, a shipping label, and a handwritten note from the hospital’s procurement office. An agent had to open the file, read it, cross-reference the order number in the ERP, check the shipment status, and type a reply. The average cycle time from receipt to first response was 4.2 hours, with a peak of 9 hours during Monday mornings. The error rate on manual extraction was 11 percent, mostly misread order numbers or confused shipment references. The compliance constraint was non-negotiable: the company serves US-based hospital partners and is subject to HIPAA. Any document containing patient-identifiable information, even indirectly through a hospital’s internal reference number, had to stay on the client’s own infrastructure. The deadline was four weeks, aligned to the start of the next fiscal quarter, when the support team would be restructured.
Approach: A Four-Week Pilot with a Dedicated AI Team
Forfis deployed a dedicated AI team of four: two backend engineers, one product designer, and one engineer focused on the integration layer. The first week was a process audit. The team sampled 800 tickets from the prior quarter, categorized them by document type, and measured the baseline cycle time and error rate. The audit identified three document types worth automating: order confirmations, shipment status requests, and return authorizations. The pilot scope was fixed to the first two: order confirmations and shipment status. The architecture used a two-tier model setup. Open-weight models, fine-tuned on the client’s historical documents, ran on the client’s own GPU server for all extraction tasks involving PHI. A commercial API model handled the drafting of the first-response text, but only after the PHI fields had been stripped by the on-premises layer. The pgvector index stored embeddings of the client’s order history and shipment records, enabling the system to match an extracted order number to the correct ERP record in under 18 milliseconds. The integration layer was a set of custom REST API endpoints and webhooks that wrote back to the helpdesk and ERP without replacing either system.
Outcome: 38-Minute First Responses and a 3.4 Percent Error Rate
By the end of week four, the pilot was in production for the two in-scope document types. First-response time dropped from 4.2 hours to a median of 38 minutes, with the 95th percentile at 2 minutes 14 seconds. The extraction error rate fell from 11 percent to 3.4 percent, with the remaining errors concentrated in handwritten notes, which the system correctly flagged for human review rather than guessing. The human-in-the-loop layer caught 14 percent of documents in the first week, dropping to 4.8 percent by week four as the model adapted to the client’s document formats. The support team reported that agents spent 60 percent less time on data entry and cross-referencing, redirecting that time to complex cases. The cost per ticket, measured as fully loaded labor cost divided by tickets handled, fell by an estimated 31 percent. The client’s compliance officer confirmed that no PHI left the on-premises environment during the pilot. The system handled 1,200 tickets per week at peak, a 40 percent increase over the pre-pilot volume, without adding headcount.
Lessons for Teams in Regulated, Document-Heavy Support
- Fix the baseline before you build. The two-week pre-pilot measurement of cycle time and error rate is not optional. Without it, the post-pilot comparison is anecdotal, and the client cannot justify the rollout to the board. Forfis treats the baseline as a deliverable in its own right.
- Scope the pilot to one or two document types, not a whole department. A four-week timeline is realistic only if the scope is narrow. Expanding to return authorizations, warranty claims, and invoice disputes in the same window would have pushed the timeline to ten weeks and muddied the metrics.
- Put the PHI boundary in the architecture, not in the policy. The on-premises model for PHI and the API model for non-PHI text are separated at the routing layer. A policy document saying “do not send PHI to the API” is not a control. The code enforces it.
- Human-in-the-loop is a tuning parameter, not a fallback. The confidence threshold for routing to a human is adjusted weekly during the pilot. Starting too high (routing 40 percent of documents to humans) defeats the purpose; starting too low (routing 2 percent) risks errors. The 12-to-5 percent drop over four weeks reflects this tuning.
- The integration layer is the real product. The LLM is a commodity. The REST API adapters, webhook handlers, and pgvector index that connect the model to the client’s existing helpdesk and ERP are what make the system work in production. Budget engineering time accordingly.
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