Artificial Intelligence.
At Arch, we help organisations cut through the noise and apply AI where it matters most. Whether you're exploring the potential of machine learning, building a smart product, or scaling a custom LLM solution, our AI Innovation Lab brings strategy, design, and engineering together. We collaborate closely with your team to imagine, test, and deliver AI that’s grounded in real user needs and measurable outcomes not just algorithms.
AI Discovery & Strategy Workshops.
Every project starts with clarity. Our workshops help you identify the real opportunities for AI within your organisation. We bring together stakeholders, user insight, and technical expertise to explore ideas, assess feasibility, and shape a roadmap that aligns with your business goals.
These focused sessions surface high-value use cases, demystify the tech, and build alignment across teams, creating the foundation for smarter innovation.
ClarityAI.
Clinicians are drowning in unstructured data, from consultation notes and referral letters to lab results and EHRs. Valuable insights are often buried in lengthy, fragmented records, leading to missed information, slower decisions, and burnout.
The Concept
ClarityAI is a prototype LLM-powered assistant that reads clinical notes, extracts key data points, and generates concise, medically-relevant summaries, paired with context-aware suggestions or alerts.
Use case: A GP or specialist uploads a patient’s consultation history and receives:
- A structured summary of key diagnoses, symptoms, and medications
- Flags for potential red flags (e.g. missed follow-ups, drug interactions)
- Suggested questions or follow-up actions based on guidelines
How It Could Work and Objectives.
How It Could Work
- Input Layer: Free-text notes, patient history, lab reports
- LLM Pipeline: Custom fine-tuned GPT/LLM trained on anonymised clinical datasets (MIMIC-III or synthetic data for R&D)
- NLP Modules: Named entity recognition (NER) for medical concepts, ICD/SNOMED term mapping
- Output: Concise summary + structured JSON for downstream use (e.g. EHR integration, analytics, risk scoring)
R&D Objectives
- Test the viability of LLM summarisation in high-context, high-risk healthcare environments
- Explore accuracy vs. hallucination risk
- Measure time saved for clinicians in routine triage or documentation
- Validate regulatory/compliance considerations (e.g. in UK NHS, GDPR, MHRA)
Potential Impact.
ClarityAI is designed to meaningfully reduce admin time for clinicians, improve the safety and efficiency of triage, and enhance patient communication through clearer, structured summaries. By offering integration opportunities with EHR providers and digital health platforms, it opens up scalable potential for real-world healthcare environments.
To power this, we draw on a modern, flexible AI stack:
- OpenAI GPT-4 or Claude, with fine-tuning or retrieval-augmented generation
- LangChain for orchestration of multi-step AI workflows
- FHIR-compliant output for seamless EHR compatibility
- Hugging Face transformers for on-prem or lightweight model options
- Ethically sourced data from synthetic patient records and open clinical corpora
FAQ.
1. I’m not sure where AI fits in my organisation—can you help?
Yes. Our Discovery Workshops are designed to explore potential use cases, assess feasibility, and help your team identify where AI can provide real value.
2. Can you help us prototype before we commit to a full build?
Absolutely. Our PoC phase is designed to validate ideas fast—building simple, testable versions of your AI product to demonstrate value and inform next steps.
3. What kind of AI technologies do you work with?
We work across OpenAI, Claude, Hugging Face, and AWS/GCP services. We also build with LangChain, vector databases, and LLM orchestration tools depending on the challenge.
