Case Studies

Proven delivery in complex environments

Selected case studies from regulated and high-stakes industries.
View Cases
Case Study

Proven Outcomes
from Projects

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Biotech
GPU Infrastructure, Observability, HPC Virtualisation
Reduced container startup from 10 seconds to under 1 second.
Global Biotech / R&D
Data Virtualisation, Platform Engineering
Significant operational efficiency through automated upgrades and restarts.
Information Technology & Services
LLM Fine-Tuning, NLP, Domain Adaptation
Achieved an F1 score of 0.892 (~89%), significantly outperforming untuned LLM/RAG baselines.
Life Sciences
AI Information Extraction / NLP
Ensured continued compliance with regulatory constraints on patient consent.
Manufacturing & Logistics
AI Platform Engineering
Increased test coverage (0% → 80%) dramatically improves system reliability.
Global Pharmaceuticals
Enterprise-Scale RAG
Estimated 30–40% reduction in time to find relevant information and generate reports.
Coming soon

New Ideas Turning
Into Reality

In-progress prototypes focused on scalability, performance, and next-generation machine intelligence
AI
Life Sciences
Institutional Intelligence Platform
Codifies our internal knowledge into a reusable AI system to accelerate scoping, reduce rework, and improve delivery quality
AI
Life Sciences
Delivery Memory System
A system that captures project decisions, trade-offs, and outcomes to prevent teams from repeating the same mistakes.
AI
Life Sciences
Board Reporting Agent
Produces board-level narratives from operational data.
FAQs
Frequently Asked
Questions
All Questions
General Questions
Getting Started
Implementation
Results
What makes Hybrid Mind’s AI approach different?
Most teams jump straight to technology. We start with value. Our framework prioritises ROI, feasibility, and adoption from day one, ensuring AI delivers measurable impact—not theoretical promise.
How quickly can we see results?
Most clients see clarity within 3 weeks, early validation within 6 weeks, and production-grade impact shortly after. The entire model is built to compress decision-making and accelerate ROI.
Do you only work with companies that already have strong data foundations?
No. Many clients come to us without mature data environments. The Readiness Assessment highlights what’s required, and later phases address gaps pragmatically as solutions scale.
What internal resources do we need to start?
Typically, access to a few business stakeholders, a technical contact, and someone who understands the operational workflow. We deliberately minimise internal burden.
How do we know if AI is right for our organisation?
The Readiness & ROI Assessment answers that immediately, mapping opportunities, quantifying value, and showing exactly where AI can deliver meaningful returns.
What does the first 3 weeks involve?
Interviews, data reviews, feasibility assessments, and ROI modelling. The outputs are a validated use-case portfolio and a roadmap that removes guesswork from investment decisions.
Do you need access to our data before beginning?
Not for the initial assessment. We only request access later when validating feasibility or building prototypes.
How do you prioritise use cases?
Each idea is evaluated across ROI potential, feasibility, change complexity, and adoption likelihood—ensuring only commercially viable opportunities advance.
What happens during rapid prototyping?
We build a functional MVP using real workflows and data where possible, validating accuracy, usability, and ROI before committing to scale.
How do you ensure prototypes are enterprise-ready?
HybridMind uses governance-ready accelerators pre-validated for compliance, security, and auditability, aligning prototypes with production standards from day one.
What’s the difference between scale-up and production deployment?
Scale-up covers industrialisation: pipelines, monitoring, retraining, governance, testing, and change management. Deployment is the final live rollout.
Do we need our own engineering teams?
No. Hybrid Mind can deliver end-to-end or embed specialists within your team through our Embedded Technical Teams model.
How do you measure success?
Every project is anchored to a business metric—productivity, cycle time, cost savings, accuracy uplift, or capacity creation.
When do we know a prototype is worth scaling?
Once it demonstrates technical viability, user acceptance, and a clear ROI case. Only evidence-based solutions progress.
What kind of ROI do clients typically see?
Most validated prototypes show productivity gains of 20–60%, cost reduction, or significant improvements in speed and decision quality.
How do you ensure AI adoption across the business?
We involve stakeholders early, test with real users, and pair deployment with process redesign and change enablement.
Have more questions?
Contact our team anytime - we’re here 24/7.
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