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Addressing business opportunities with AI-powered solutions, alongside a team of domain experts
Services
Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) improves the reliability of AI by grounding responses in trusted data sources. Rather than relying only on pre-trained models, it retrieves information from documents, databases, or APIs before generating answers, making outputs accurate, contextual, and traceable. We help teams apply RAG securely, with governance and data pipelines for confident adoption.
Agent-Based and Multi-Agent Workflows
Agent-based systems carry out structured, repeatable tasks that slow teams down when handled manually. They can fetch data, follow rules, and pass information between systems. When several agents work together, they form multi-agent systems. With the Model Context Protocol (MCP), tasks can be divided, verified, and reassigned if one part fails. We design and implement these systems so routine work is handled automatically, and humans only step in where their judgment adds value.
Voice and text assistants
AI assistants handle domain-specific queries across chat, voice, and product interfaces. They can run tasks in connected systems, answer routine questions, and hand requests to people when needed. Use cases range from internal helpdesks and IVR call flows to product guidance and support. We create assistants that adapt to different channels and interaction styles, so users get consistent help whether they type, tap, or speak.
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AI Consulting
AI consulting helps organizations move past pilots and see results. It starts with mapping workflows and running pilots that test if automation cuts manual work, speeds up reporting, or reduces errors. When pilots succeed, the next step is to make them part of daily operations with the right checks and metrics. We help teams double down on what works and stop spending time and money on what doesn’t.
AI Monitoring and Evaluation
AI monitoring keeps deployed models reliable by tracking drift, bias, and performance drops that traditional monitoring often misses. Each system logs usage and outputs, with review loops so teams can audit results and enforce governance. This oversight is critical where accuracy and compliance cannot be compromised. We help teams set up monitoring that makes AI dependable in practice.
Fine tuning Foundational/Small Models
We tailor foundational and small language models to your domain for sharper accuracy and context awareness. Using efficient fine-tuning methods like LoRA and PEFT. We deliver high-performing, cost-effective models that align with your business needs and are ready for rapid deployment.
Building AI systems to improve public legal help for ILAO
Over five years, QED42 partnered with Illinois Legal Aid Online. The latest phase introduced semantic search and a conversational assistant powered by RAG, grounding answers in verified legal content.
5
+
Years of engagement
Monthly search sessions
2x
Faster path to content
Platforms
We work across cloud platforms, selecting the right AI services for each use case rather than defaulting to one provider.
Build with Azure AI
Azure OpenAI Service
Copilot Studio
AI Search
Document Intelligence
Our AI Values
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AI for good
AI should be used to solve real-world problems, promote fairness, and improve lives. From addressing accessibility challenges to reducing inequalities, AI can create a positive social impact when implemented responsibly
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Human-in-loop
AI works best when combined with human oversight. Including people in critical steps ensures accuracy, improves decision-making, and keeps the system aligned with real-world needs
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Adapting to context
AI should continuously learn and adjust based on new data, changing user behaviours, and evolving business needs. This ensures it remains relevant, effective, and aligned with the context in which it operates
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Ethics first
Ethical AI means being fair, transparent, secure and private. It involves spotting and reducing biases in systems and making decisions that users can trust. Accountability at every stage builds long-term reliability in AI solutions
Process
QED42 PROCESS
How we work
We listen before we build. The partnership continues past launch
18 +
YEARS OF DELIVERY
4.85
Net Promoter Score
Drupal Diamond Partner
01
Understand
Technical debt, constraints, users need the things the brief doesn't say. That's where we start. Before a line of code is written
02
Build
Architecture, design, engineering — in cycles, with continuous client sync. No disappearing acts. Delivered in cycles, not in one go
03
Stay
Post-launch support, performance tracking, platform evolution. Results you can point to. The work doesn't stop at go-live
Frequently asked questions
We ran a pilot that worked. Why is production so different?
How do you handle data sovereignty and compliance when building AI systems?
What does AI monitoring actually involve after deployment?
Can AI integrate with our existing Drupal or CMS platform?
How long before we see measurable results from AI?
What does our team need to provide for an AI engagement?
From the Team
Related Blogs
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Bring your use case, we will build it
Not sure where AI fits? We will help you figure that out