HEALTHCARE / EUROPE

AI Copilot Solution For Complex Business Workflows

HEALTHCARE / EUROPE

We developed an AI Copilot solution for a client to streamline complex workflows, starting with a Health Technology Assessment (HTA) extraction use case. The system transforms unstructured data into actionable insights by enabling users to extract and structure information and simplify complex issues.

CHALLENGES

Our client wanted to develop an innovative AI Copilot solution to streamline complex business workflows, starting with their first use case: the HTA Extract. This solution leverages a multi-step process orchestration system in order to transform unstructured data into actionable insights. By enabling users to extract structured information and simplify complex issues, the solution empowers informed decisions efficiently.

SOLUTION

We delivered a robust AI-powered tool tailored to address the client’s challenges and goals, focusing on six key areas:

Verification and Validation

Built processes to ensure the AI system provides accurate, reliable, and safe outputs that meet real-world requirements.
Referencing

Implemented systems to link specific documents or passages, enhancing transparency, trust, and factuality in AI-generated responses.
Multi-Part Outputs

Designed AI models capable of generating comprehensive, multi-component answers to address complex workflows and challenges effectively.
Collaboration, Approval and Audit

Integrated features for human oversight to improve accuracy, safety, and ethical use while facilitating collaboration and enabling continuous system improvement.
Security and Permission Model

Ensured the protection of sensitive data through robust access control, compliance with privacy regulations, and system integrity safeguards.

Value added

Increased Accuracy and Trust:

Verification, validation, and referencing mechanisms enhanced the reliability of AI-generated outputs.

Enhanced Data Interpretation:

AI’s ability to understand tables and figures provided deeper insights into complex datasets, improving decision-making.

Improved Collaboration and Oversight:

Collaboration, approval, and audit features ensured human involvement, reducing risks and promoting ethical AI use.

Data Security and Compliance:

A secure permission model safeguarded sensitive data and ensured regulatory compliance, fostering client confidence.

Key Takeaways

Prompt engineering is the secret sauce to any LLM interface
Model and prompt changes affect the output
Metrics for quality/relevance are beneficial and are needed for LLM’s outputs
Information extraction pipeline from multiple sources is needed i.e. knowledge graphs
The granularity of grounding (or leakage) to source/support evidence is mandatory
Multiple LLms orchestration is likely to be required in complex enterprise use cases
UX principles need a refresh to reflect a collaboration between the user as pilot and the AI as co-pilot
Some use cases may be well served by 3rd-party solutions, but attention should be paid to potential lock in and ability to own the competitive advantage

SERVICES DELIVERED

TEAM COMPOSITION

4 AI Developers
1 Quality assurance engineer
1 Solution Architect
1 Project Manager

PROJECT DURATION

Development project duration: 12 months.

The development process is still in progress, with the implementation of Stage 2 continuing and ongoing fine-tuning of the entire system.

TECHNOLOGIES

.NET Framework
Semantic Kernel
React
Azure

We’re extremely thankful to have
earned 5-star ratings from our valued clients.

Flowmatters

12 reviews

“I appreciated the coordination by mails.”
Owner, Clinic

“Everything we want has been possible, and they improve my ideas and meet out needs.”
General Director, SC EDU TRUST SRL

“What we appreciate the most is their availability and willingness to clarify technical aspects and terms.”
National Sales Representative, PicoEnergy Romania

“Very professional team who had a good feeling.”
Expert Marketing Online, Tabita Impex

“They don’t only deliver, they are also there to help you any time you may have questions.”
CEO, Maradent

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