Join our customers
Own the model, own the margin with purpose‒built clinical AI
Practical AI & machine learning solutions for private clinics and healthcare providers that streamlines healthcare administration, improves private doctors’ workflows, and cuts costs.
Clinical documentation
- AI for clinical decision support to surface risks and next-best actions
- AI-assisted diagnosis for doctors using multimodal data and guidelines
- AI for electronic health records to search, summarise and reconcile notes
- Predictive analytics for private clinics to flag at-risk patients and no-shows
- AI for clinical research to extract cohorts and build reproducible datasets
Operations & administration at scale
- AI workflow automation for clinics to streamline intake, billing and referrals
- AI appointment booking system with reminders and triage questions
- AI scheduling for medical practices to balance capacity and demand
- AI for patient data management with audit logs and retention controls
- AI for healthcare quality improvement with real-time indicators and alerts
Patient access, triage & follow-up
- AI medical chatbot for clinics for secure intake, FAQs and symptom checks
- AI tools for private healthcare & AI tools for private doctors at the point of care
- AI diagnostics for clinics to pre-screen images and structured data
- AI solutions for small clinics that deploy in days, not quarters
- Artificial intelligence for healthcare providers that you can own and extend
Ready to cut costs and take control?
If a fixed-fee, private AI solution for private clinics sounds like exactly what you need, let’s talk.
💸 Cut your clinic’s AI spend
Calculate the savings your practice could bank with our medical AI solutions
How much are your tokens burning this month?
Set patient encounters and average note size to see, in pounds and pence, what GPT usage is really costing your clinic — and how much you keep with a self‒hosted stack.
Monthly cost comparison
£0
£25
£50
£75
You Save
£60
per month
Annual Savings
£720
How self‒hosted medical AI delivers ROI in just four weeks
Pick where it lives
Day 0 — your infra, your rules
- Choose on-prem, private cloud (Azure/Google), or hybrid
- Fixed-fee SoW e-signed in two clicks
- Kickoff call booked within 24 hours
Get your clinical chat assistant live
Week 1 — running in your own environment
- Open-source LLMs tuned to your specialties
- containers deployed, guard-rails tested, HIPAA/GDPR audit logging on
- EHR and dictation feeds connected (FHIR/HL7), notes and summaries ready to query
Bank the savings and protect PHI
Week 4 — results you can bill on
- Token spend down 60–80 % versus rented AI
- Documentation time cut 30–50 % per visit; faster follow-ups and fewer callbacks
- You own every model, embedding and log; PHI stays in your UK/EU tenant with predictable per-note economics
Self‒hosted AI in action: Real client wins
Our AI succes stories
David ODonnel
Founder, Mocxy.ai | AI fueled CX Design agency
"The team at LogiNet International went above and beyond at every possible instance."
LogiNet International developed an LLM wrapper for GPT for a design agency. The team built a customer-facing portal and designed the UI and UX for the platform.
They team went above and beyond to meet the client's needs and identify additional requirements. Their proactive approach and outstanding final product impressed the client.
I will definitely return to this company for more work, and they will be my first recommendation when asked!
Cut manual grind, gain AI speed
See the before and after with AI solutions for private clinics, covering cost per encounter, documentation time, PHI residency, auditability and scale.
Metrics that move the needle for your clinic
Cost per encounter:
Documentation time per encounter:
PHI residency and data control:
Responsiveness (note turnaround):
Auditability and compliance:
Scaling during peaks:
Clinical and finance visibility:
Onboarding time:
Prompt control and safety:
Vendor risk:
Before AI (manual or SaaS wrapper)
• Variable, per-token bills that rise with usage
• 10–15 minutes per visit, heavy clinician after-hours work
• Notes and embeddings copied to external SaaS datacentres
• End-of-day batching, delays to follow-ups and coding
• Manual logging, scattered audit trails, DPIA overhead
• Overtime or SaaS overage during flu season/surge clinics
• Unclear ROI, hard to link AI spend to throughput and revenue
• Weeks of vendor negotiation and data-export hoops
• Black-box prompts, no control over context windows or retrieval
• Price hikes, feature removals, lock-in to a single model
After LogiNet
self-hosted assistant
• Predictable fixed fee, 60–80% lower token spend
• 4–7 min with AI note drafting and AI for electronic health records summarisation
• All PHI, embeddings and logs stay in your UK/EU tenant (HIPAA/GDPR aligned)
• Near-real-time notes and summaries at point of care
• Tamper-proof query and citation logs, exportable on demand for audits
• Spin up extra on-prem containers with no per-token penalty
• Live dashboard maps £ and minutes saved per encounter, by specialty
• Containers deployed in days, connectors for FHIR/HL7 configured quickly
• Full visibility and tuning of prompts, RAG sources and model routing
• Model-agnostic design lets you swap providers without rewrites
Built by experts who’ve been doing AI before it was a buzzword
At LogiNet, we’ve been delivering real-world AI solutions since long before GenAI became a trend. From LLM-based assistants to privacy-first automation, we’ve built AI tools that run inside critical systems—fast, safe, and tailored. Our AI chat agents don’t just answer questions. They understand your business.
Tools and technologies that power your AI success
From open-source models to custom workflows, here’s what drives real-world AI medical tech
Backend core
Python 3.11+ – orchestration, pipelines,
FastAPI – REST & OpenAPI docs,
SQL DB – MariaDB / PostgreSQL (+ time-series ext.), Redis – cache, sessions,
Celery – background tasks / training jobs
Model-workflow & deployment tools
TensorFlow Extended (TFX), PyTorch, ONNX & ONNX Runtime, JAX / Flax, CUDA / cuDNN, NVIDIA TensorRT, Triton Inference Server, Hugging Face Transformers / Diffusers / PEFT, MLflow, PaddlePaddle
Commercial foundation-model APIs
OpenAI GPT family, Google Gemini, Anthropic Claude, Microsoft Phi (Phi 4), Alibaba Qwen, Cohere Command-R,
Amazon Titan (Bedrock)
Open-source LLMs
Llama 3 (8B / 70B), Mixtral 8×22B, TinyLlama, Falcon 180B / 40B, Phi-3-mini (MIT), Qwen-14B / 72B, RWKV, Vicuna / Alpaca / Orca, RedPajama, BLOOM, GPT-J, GPT-NeoX, GPT-Neo
Text-to-speech engines
ElevenLabs Voice AI,
Microsoft Neural TTS, Google Cloud TTS,
Amazon Polly, OpenAI TTS,
Murf AI, PlayHT
Speech-to-text engines
OpenAI Whisper, Deepgram, Google Speech-to-Text, AssemblyAI, Amazon Transcribe, Microsoft Speech
Computer-vision APIs / libraries
GPT-4o Vision, Azure Vision Studio,
Google Vertex Vision, AWS Rekognition,
Roboflow, OpenCV
Image-generation models / services
DALL·E, Midjourney,
Stable Diffusion,
Runway (video)
Agentic & workflow frameworks
LangChain, LangGraph, Haystack, Agno, LlamaIndex, AutoGen, CrewAI, Griptape, MetaGPT, ExaTools
Vector databases / stores
Milvus (Zilliz), Pinecone, Weaviate,
Qdrant, Chroma, pgvector,
Redis Vector
Embedding model families
OpenAI text-embedding-3,
Cohere embed-v3, Google Gecko,
Sentence-Transformers
AI-powered search & retrieval tools
Perplexity,
Exa.ai
Optimization engine
NumPy, SciPy,
Pandas, OR-Tools (CP-SAT, VRP, MIP)
Machine-learning stackBackend core
scikit-learn, LightGBM, XGBoost, TensorFlow / Keras, SHAP
Backend core
Python 3.11+ – orchestration, pipelines,
FastAPI – REST & OpenAPI docs,
SQL DB – MariaDB / PostgreSQL (+ time-series ext.), Redis – cache, sessions,
Celery – background tasks / training jobs
Model-workflow & deployment tools
TensorFlow Extended (TFX), PyTorch, ONNX & ONNX Runtime, JAX / Flax, CUDA / cuDNN, NVIDIA TensorRT, Triton Inference Server, Hugging Face Transformers / Diffusers / PEFT, MLflow, PaddlePaddle
Commercial foundation-model APIs
OpenAI GPT family, Google Gemini, Anthropic Claude, Microsoft Phi (Phi 4), Alibaba Qwen, Cohere Command-R,
Amazon Titan (Bedrock)
Open-source LLMs
Llama 3 (8B / 70B), Mixtral 8×22B, TinyLlama, Falcon 180B / 40B, Phi-3-mini (MIT), Qwen-14B / 72B, RWKV, Vicuna / Alpaca / Orca, RedPajama, BLOOM, GPT-J, GPT-NeoX, GPT-Neo
Text-to-speech engines
ElevenLabs Voice AI,
Microsoft Neural TTS, Google Cloud TTS,
Amazon Polly, OpenAI TTS,
Murf AI, PlayHT
Healthcare AI questions, answered plainly
Cost & ROI clarity
We bill by the appointment. Will a fixed-fee model really lift margin per encounter?
What if OpenAI or Anthropic raise prices overnight?
We already pay for a SaaS medical scribe. Why isn’t that good enough? Why build your own AI medical chatbot for clinics?
Will we need to budget for new GPUs or long cloud reservations?
Client confidentiality, data sovereignty & regulation
We need airtight compliance.
Can AI still help?
Will any patient data ever leave our UK or EU data centres?
Does a self-hosted assistant reduce paperwork for audits and DPIAs?
Who owns the embeddings and knowledge base if we part ways with LogiNet?
Accuracy, control & risk mitigation
If the agent hallucinates, who carries the liability: you or us?
How do we stop clinicians blindly trusting an automated answer?
Can we veto responses that exceed a risk threshold?
Can we veto responses that exceed a risk threshold?
Implementation, integration & upkeep
How long from signature to first production note?
Who watches token burn and latency once live, your team or ours?
What internal resources do we need?
Can we expand beyond scribing to admin and triage?
Done exploring? Let’s start building.
You’ve got the answers. Now let’s build the artificial intelligence tools your healthcare team actually needs.
💡 The AI implementation reality check
Real costs. ROI ranges. Readiness. Spelled out.
Get a no-nonsense guide that helps you answer three things fast:
- Costs: clear bands for tools, integration, training, support.
- Returns: ROI ranges by use case, from service to finance.
- Readiness: one check: real problem, usable data, budget, buy-in.
Download now!
Drop in your work email and we’ll send you a 25-answer Q&A guide covering: cost breakdowns, expected payback, and a simple readiness test you can run this week.
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Fresh thinking from LogiNet
AI resource centre: watch, learn, implement
From pilot to pay‒off: turning AI experiments into real ROI (2025 edition)
What’s inside the video?
- Why chasing an “AI strategy” is a distraction, focus on profit leaks first.
- Where to start: back-office workflows vs. client-facing experience.
- Concrete ROI wins in support and sales (transferable to legal ops).
- The truth behind the hype: 80 % of great AI is disciplined software engineering.
- Live demo: a self-hosted voice agent resolving real customer calls, no SaaS lock-in, full control.
Ready to shrink your AI bill? Book your 30‒minute AI cost audit
Share a few details and we’ll confirm a time, benchmark your current AI spend, and outline a self-hosted or private-cloud roadmap that cuts costs while keeping PHI inside your own environment.
John Radford
Client Services Director UK
Current AI tools used
Select one or more options
…or simply book a 30‒minute AI cost audit on Calendly
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