Healthcare
Clinical note structuring
PHI can't leave hospital Azure tenant. Llama on AKS, fine-tuned on clinical notes.
↓ 12min → 90sec per intake
We build self-hosted Meta AI on your own infrastructure, giving you data residency, predictable inference cost, domain fine-tuning, and full ownership without per-token bills or vendor lock-in.
Self-hosted meta AI on your infrastructure data residency, ownership, no lock-in.
We size the GPU cluster for your workload, instrument utilisation, and give you a fixed monthly infra cost before we deploy. No per-token surprises. Scale up by adding nodes, not by negotiating a new pricing tier.
We fine-tune on your proprietary dataset - with an eval harness that proves the fine-tuned model outperforms the base model on your actual tasks before it touches production. Accuracy is a metric, not a feeling.
The weights, the fine-tuning data, the prompts, the eval suite, the deployment config. If you want to take it in-house on day 180, you walk away with everything. No royalty, no lock-in.
Vendor lock-in, unpredictable inference bills, and data residency rules rule out public APIs.
Whoever owns inference and data: we deploy meta AI where your data must stay.
Clinical notes, contract classification, and document intelligence running on self-hosted meta AI.
PHI can't leave hospital Azure tenant. Llama on AKS, fine-tuned on clinical notes.
Client data under NDA no public API acceptable. Fine-tuned on 40k historical contracts.
MNPI concerns. Air-gapped deployment on bare-metal. 99.1% field accuracy.
Data sovereignty requirement. On-prem deployment, no cloud egress.
Proprietary technical documentation. Fine-tuned Llama on factory edge hardware.
80,000 tickets/month. GPT-4o cost: $34k/yr. Llama infra cost: $6.8k/yr.
Student data under FERPA no vendor subprocessors. Self-hosted, fine-tuned on rubrics.
Confidential internal docs. Llama + pgvector in private VPC. Citations from real runbooks.
Rights-sensitive content. Air-gapped GPU. No external API calls.
From scoping to a fine-tuned model, we deploy on your own infrastructure.
GPU sizing for your workload. Data audit for fine-tuning. Baseline accuracy on your tasks using the base model. Fixed-price plan with a measurable target.
QLoRA fine-tuning on your proprietary dataset. Eval harness with golden sets per task type. We don't declare the model ready until it beats a measurable target.
VPC deployment, SSO, API gateway, rate limiting, retries, fallbacks, cost monitoring, HITL queues where required.
Runbooks, training, model version management docs, retraining pipeline setup, on-call drills.
The meta AI, serving, and fine-tuning stack that keeps inference owned and affordable.
Live, fine-tuned meta AI models running on your infrastructure at predictable cost.
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 champions open-source, transparent AI with models like Llama 3 that allow businesses full ownership, customization, and control without vendor lock-in.
Yes, Fullestop supports on-premise and private cloud deployments, ideal for regulated industries needing full control over data and infrastructure.
Through model fine-tuning with proprietary data, the AI learns industry-specific language and customer intents for accurate, domain-relevant responses.
Effective prompt engineering is vital to produce reliable, consistent, and brand-aligned AI outputs, turning raw models into dependable business tools.
Fullestop implements content moderation tools like Llama Guard to maintain brand safety and align AI interactions with ethical guidelines.
Finance, healthcare, legal, retail, and other sectors needing secure, customizable AI with strict compliance benefit greatly from on-premise and private deployments.
Open-source models like Llama foster a collaborative ecosystem where developers contribute improvements, accelerating AI advancements and customized solutions.
Yes, Llama 3 supports multiple languages, enabling global applications and versatile communication in diverse markets.
Deployments follow strict security and governance frameworks to ensure data sovereignty while AI operations comply with privacy and regulatory standards.
Custom chatbots, virtual assistants, voice bots, content generators, and AI agents tailored to specific workflows and brand voice.
