1. Audit Before You Automate
A 30-person UK medtech company processes 200 support tickets a week. Forty percent involve retrieving the same 12 clinical trial documents from Confluence. The median cycle time is 4.2 hours per ticket, and 11% require rework because the wrong document version was sent. The audit identifies this as the highest-impact workflow: high volume, repetitive, and error-prone. The fix is a RAG assistant over Confluence that retrieves the correct document version and drafts a response. A human approves anything touching patient data. The pilot runs for two weeks with a measured baseline. Cycle time drops to 1.8 hours. Error rate falls to 3%. The client now has a concrete ROI figure to justify rollout across the remaining 60% of tickets.
2. Route PHI to On-Prem, Everything Else to Claude
HIPAA requires that PHI never leaves the client’s controlled environment. Forfis runs open-weight models on the client’s own hardware for any workflow touching PHI, while using Anthropic Claude API for non-PHI tasks like ticket classification or document summarization where data can be de-identified. The architecture is model-agnostic by design. The same workflow routes PHI-sensitive calls to on-prem models and non-sensitive calls to the API. This keeps both speed and compliance intact. A 30-person medtech firm does not need to choose between a fast API and a compliant on-prem model. It uses both, in the same pipeline, with a routing layer that checks whether the input contains PHI before dispatching the call.
3. Plug Into Confluence and the Helpdesk, Not Around Them
The AI layer plugs into existing systems through their native APIs. A RAG assistant over Confluence reads from Confluence’s REST API. A ticket triage system writes classifications back to the helpdesk via its webhook. The client’s existing data model, access controls, and audit logs remain untouched. The AI layer is a thin, reversible addition rather than a platform migration. For a 30-person firm, this means no data migration, no retraining on a new tool, and no disruption to the existing workflow. The integration work takes 3 to 5 days per system, which fits inside the 4-week pilot timeline. The client keeps its Confluence, its helpdesk, and its CRM. The AI layer sits on top.
4. Score Tickets Before a Human Reads Them
Predictive scoring assigns a probability to each incoming ticket indicating likely resolution path, expected handling time, or risk of escalation. For a medtech company, this flags tickets mentioning adverse event language for immediate human review while routing routine dosage questions to a first-response agent. The scores are generated by the LLM and validated against historical ticket outcomes during the pilot. A human approves any action that touches patient data or contractual commitments. The model drafts the classification and the score. The person decides whether to act on it. This human-in-the-loop default is non-negotiable for any workflow touching money, health data, or a contract. It is the reason the pilot ships with a measured error rate baseline.
5. Ship a Measured Baseline, Not a Demo
The pilot ships with a measured before/after baseline on two metrics: cycle time and error rate. For a typical 30-person healthcare firm, Forfis has seen cycle time drop from 4.2 hours to 1.8 hours and error rate fall from 11% to 3% on document-heavy support workflows. These numbers are captured in a one-page report delivered at the end of week 4. The client gets a concrete ROI figure to justify rollout. The report also includes a list of edge cases the model handled poorly, which becomes the input for the next iteration. Without this baseline, the client cannot prove ROI or identify which workflow actually has the highest error rate. The audit and the measured pilot are the two things that separate a working deployment from a demo.
6. Three Mistakes That Kill a 4-Week Pilot
The most common failure is skipping the audit and jumping straight to a demo. Without a measured baseline, the client cannot prove ROI or identify which workflow actually has the highest error rate. The second pitfall is assuming a single model handles all tasks. A 30-person medtech firm might need Claude API for nuanced clinical document summarization but an open-weight model on-prem for PHI-tagged ticket routing. The third is underestimating integration work: connecting to Confluence, the helpdesk, and the CRM through their APIs takes real engineering time that a 4-week timeline must account for. The audit, the model routing, and the integration scope are the three things that determine whether a 4-week pilot delivers a measurable result or a slide deck.
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