Clinical documentation
SOAP note structuring
Physician dictation → structured SOAP note → ICD-10 suggestion → EHR field population. Zero cloud egress.
↓ 12min → 90sec intake
Your data protection officer has a simple rule: no PHI leaves this network. Not cloud, not SaaS, not API - regardless of the contract. That rule has blocked your AI roadmap for two years. Meta Llama running in your VPC is the first clinical AI architecture your DPO can actually approve.
Three things we sign up to before we write a line of code. All measurable. All agreed upfront.
Llama runs in your VPC, your data centre, or your sovereign cloud tenant. Every inference call stays inside your network boundary. The DPO, IG lead and CISO sign off on a network diagram - not a vendor promise.
We fine-tune on your institution's de-identified records - your clinical notes, your formulary, your documentation style. The result outperforms a general model on your specific tasks by 10–20 percentage points. Accuracy is measured on your eval set, reviewed by your clinicians.
The fine-tuned weights, the de-identification pipeline, the eval suite, the deployment config - all yours. Your clinical IT team operates it. You retrain when your documentation conventions change. We leave nothing that requires our ongoing involvement.
We see the same failure modes every engagement. Our delivery model is built to avoid all of them.
Most engagements start with one of these six people. The pitch is calibrated to the metric they're judged on.
Across industries. Each with a specific mechanism and a specific metric.
Physician dictation → structured SOAP note → ICD-10 suggestion → EHR field population. Zero cloud egress.
Clinical notes + payer criteria → PA letter with evidence citations. Flags likely denials. On-premise.
Prescribed medications → RxNorm interaction scan → contraindication flags. Runs on-premise.
Clinical notes → ICD-10/CPT suggestions with confidence score. Coder reviews, not codes from scratch.
Patient record → discharge summary with medications, follow-up, red flags. In-VPC.
Clinical notes → de-identified text for research datasets. 99%+ PHI detection. Audit log per record.
Pathology dictation → structured report with SNOMED-coded findings.
Shift notes → structured handover summary. On-premise. No data leaves the ward network.
Clinician Q → Llama retrieval over clinical guidelines + formulary → cited answer. In-VPC.
IG architecture document. DPIA support. VPC or NHS Azure tenant configuration. GPU sizing. De-identification pipeline setup and validated.
De-identification of training data. QLoRA fine-tuning on de-identified records. Eval suite built and reviewed by practising clinicians.
HL7 FHIR API integration. Clinical workflow integration. Confidence thresholds. Clinician review queue. Audit logs.
Supervised clinical pilot. Prompt and threshold tuning. Governance review. Runbooks. Retraining pipeline handover.
We don't force technologies. We choose the stack that best fits your AI workflows, scalability goals, integrations, and long-term product vision.
Carefully crafted AI-powered platforms designed to deliver real business impact, seamless user experiences, and intelligent automation across industries.
Deep teams with industry context - not generalists googling compliance acronyms. Each industry below has 30+ shipped projects and a partner who knows the regulator.
Telemedicine, EHR/EMR, claims automation, clinical decision support. HIPAA, HL7/FHIR, GDPR. Active partnerships with 14 hospital networks.
Core banking, neobank, payments, lending, KYC, fraud. PCI DSS, RBI sandbox, Open Banking, ISO 20022. We've shipped to Tier-1 banks in 4 countries.
Headless commerce, marketplace, omnichannel, AR try-on, AI recommendations. Shopify Plus, BigCommerce, custom. 22+ storefronts live with avg +34% AOV.
Last-mile optimisation, TMS, WMS, fleet IoT, route prediction, real-time tracking. Shipped to UPS, Alod and 11 other logistics operators.
OTT platforms, content recommendation, real-time encoding, multi-DRM, distribution at network scale. Sony Pictures, Hello Baby Direct and more.
LMS, adaptive learning, AI tutors, government portals. Shipped UKIERI for the British Council and 6 state-government education portals.
Real names, real companies, real numbers. Video on the left, written notes on the right - choose whichever feels more honest.
Although regulations prevented the site's launch, it met all requirements in terms of form and function. Fullestop's project plan charted a clear course to completion. The team's flexible, diverse talent pool enabled them to manage each stage of the project with consistent levels of skill.
Weekly demos, no surprises, and they push back when we're wrong. That last part is rare. Cut our cloud bill 47% in the first audit.
We constantly come up with top-tier resources and breathtaking
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Meta Llama’s large-scale architecture and open-source nature allow it to handle complex medical data with high accuracy, transparency, and customization.
We deploy Meta Llama solutions on-premise or in private clouds with HIPAA-compliant security measures to guarantee full data sovereignty and privacy.
Yes, we specialize in fine-tuning Meta Llama on your clinical notes, research papers, or trial data for specialized, context-aware AI performance.
Meta Llama can assist with diagnostics support, patient communication, medical research summarization, and clinical documentation automation.
Robust on-premise servers or private cloud setups with GPU acceleration are required, and Fullestop manages the infrastructure lifecycle end to end.
Yes, it can process large volumes of medical data in real-time, supporting clinicians with timely insights and recommendations.
Solutions are designed to scale from small clinics to large hospital networks, adjusting compute needs and deployment strategies accordingly.
