CLIENT STORIES / AI4U
Trustworthy AI answers for regulated industries.
We built AI4U an answer system for industries where the quality of information is critical. It answers from verified sources, shows where each answer comes from and hands uncertain questions to a person.
One example use case: customer questions answered from verified sources, with escalations to a person. AI4U support inbox, demo workspace with sample data.
One example use case: a customer question answered with a cited official source. Sample conversation.
01
THE CHALLENGE
Answers had to be fast, accurate and traceable.
AI4U needed an answer system for regulated industries, where the quality of information is critical. Answers had to draw on internal reports, government databases and industry research, and stay accurate, traceable and compliant. A basic search tool could not do that.
SOURCE 01
Public knowledge
External: government reports, academic research, industry databases
SOURCE 02
Private knowledge
Internal: documents and institutional knowledge
One system
One place to ask
01
Understand context
It had to grasp nuanced questions, not just match keywords like a basic search tool.
02
Answer from verified sources
It had to bring public and private information together and answer only from sources that could be checked.
03
Stay secure and compliant
Everything had to stay secure and compliant with UK regulations, with a person stepping in when an answer was uncertain.
02 — WHAT WE DELIVERED
One answer system. Built on verified knowledge.
We built an AI answer system that works like a knowledgeable expert on hand. It finds information across public and private sources and answers from what it can verify.
DELIVERY SCOPE
KNOWLEDGE MAP / SCHEMATIC
PUBLIC
Government reports
Academic research
Industry databases
ANSWER SYSTEM
One place
to ask
PRIVATE
Internal documents
Institutional knowledge
ONE QUESTION
A single answer can draw on public and private sources together.
01 / OUR CONTRIBUTION
We connected public sources.
Government reports, academic research and industry databases.
02 / OUR CONTRIBUTION
We integrated private sources.
Internal documents and institutional knowledge, connected securely.
03 / OUR CONTRIBUTION
We added smart retrieval.
The system finds the relevant, verified information before it writes a response.
04 / OUR CONTRIBUTION
We customised the AI engine.
We selected an open-source AI model and adapted it to AI4U’s specific needs.
05 / OUR CONTRIBUTION
We built in security.
Privacy controls, access controls, encryption and complete audit trails for accountability.
BUILT TO KEEP IMPROVING
The system gets better the more it is used.
Built-in learning capabilities help the system get better over time, so answers keep improving with use.
03 — HOW WE BUILT IT / DELIVERY DETAIL
How we built it, in detail.
We built an answer system that brings together public and private knowledge, supported by a carefully selected and fine-tuned open-source language model.
The aim was answers people in regulated industries can rely on: grounded in verified sources, easy to check, and handed to a person when the system is not sure.
CONTEXT FOR NON-TECHNICAL READERS
RAG
Retrieval-augmented generation. Before answering, the system finds the relevant information, then writes its response from it.
FINE-TUNED OPEN-SOURCE LANGUAGE MODEL
The engine behind the answers
A language model is the AI that reads a question and writes the reply. Open-source models are openly available to run and adapt. Fine-tuning customises one for a specific organisation’s needs.
GDPR
Data protection and privacy controls for GDPR, the UK and EU data-protection law.
Access
Users only see information they are authorised to access.
Encryption
Data stays secure through encryption and proper handling.
Audit
Complete audit trails for transparency and accountability.
HOW WE BUILT IT / 4 PHASES
SCOPE: DISCOVERY → DEPLOYMENT
01
Discovery and planning
We started with in-depth conversations with stakeholders to understand day-to-day challenges. We mapped existing data sources, found where people were getting stuck and defined what success would look like.
02
Development and integration
We structured the knowledge database and fine-tuned the chosen language model. We built a secure connection layer between the AI and both public and private sources, focusing on speed and accuracy.
03
Testing and optimisation
Extensive testing showed where to improve. We refined how information is retrieved, improved response quality and made sure everything met UK regulatory requirements.
04
Deployment and training
We launched the system in a secure cloud environment and trained AI4U’s team to get the most out of it. Monitoring tracks how well it works and where to improve next.
CHALLENGE 01
Giving accurate answers quickly, from scattered sources.
HOW WE ADDRESSED IT
We connected public and private sources to one system that understands context and retrieves verified information before it answers.
CHALLENGE 02
Keeping sensitive knowledge secure and compliant with UK regulations.
HOW WE ADDRESSED IT
We built in GDPR-ready privacy controls, access controls, encryption and complete audit trails, and tested against UK regulatory requirements.
04 — HOW WE WORKED TOGETHER
One team around AI4U’s knowledge.
PQ Impact led the project from discovery to launch. We worked with AI4U’s stakeholders to understand their daily challenges, then trained their team to get the most out of the system.
PQ IMPACT
Build the system
Knowledge database, language model fine-tuning, a secure connection layer, testing and cloud deployment.
PQ IMPACT + AI4U
Map the knowledge
Stakeholder conversations, existing data sources, where people got stuck and what success would look like.
AI4U TEAM
Put it to work
Trained to get the most out of the system, with monitoring in place to guide further improvement.
A TECHNICAL DECISION IN PRACTICE
Open-source, not proprietary.
THE CONSTRAINT
Proprietary AI solutions are expensive and limit long-term control.
THE DECISION
Select an open-source language model and customise it for AI4U.
WHAT WE DELIVERED
Lower AI costs, long-term control and a modular design that is easy to update.
ABOUT THE PARTNERSHIP
ABOUT THE CLIENT
AI4U
London-based AI company building AI assistants that answer from an organisation’s own verified knowledge, for sectors such as accounting, legal, financial services and healthcare.
CLIENT WEBSITE
ai4u.digitalOUR ROLE
PQ Impact
We designed, built, tested and deployed the answer system, then trained AI4U’s team and set up monitoring.
CLIENT LOCATION
London, UK
INDUSTRY
AI
DELIVERY
4 phases
DEPLOYMENT
Secure cloud
05 — RESULTS
Faster answers. Backed by sources.
AI4U now gets the information it needs, when it needs it, with answers grounded in verified sources. People spend less time searching and more time on the work that matters.
Visit AI4U ↗WHAT IMPROVED
EFFICIENCY
Faster
Faster information access, quicker decisions on complex issues and more reliable information across searches.
COST
Lower cost
Lower AI costs through open-source technology, less time on manual research and faster project delivery.
DECISIONS
Better informed
Real-time access to current information, insights that combine external trends with internal data, and consistent responses.
ARCHITECTURE
Room to grow
New data sources and users can be added, and the same approach adapts to other regulated areas. Open source keeps long-term control.
ONE EXAMPLE USE CASE · DEMO WORKSPACE
One example: customer questions on UK VAT
This is one way AI4U is used, shown in its own product with sample conversations. When verified sources contain the answer, AI4U replies and cites them. When a question needs a team decision, it escalates and prepares a draft for a person to review. The same approach works in other regulated areas.
One example: a customer question answered with a cited official source (HMRC · GOV.UK). Sample conversation.
Human in the loop: a question that needs a team decision is escalated, and a person reviews the prepared draft. Sample conversation.
Human in the loop: a question that needs a team decision is escalated, and a person reviews the prepared draft.
Looking ahead: the same foundation can support images and audio, voice queries, live data feeds and predictive insights.
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