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Applied AI, built for real use cases

Reliable enterprise-grade solutions tailored to data, processes, and compliance with human oversight

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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.

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.

Custom Model Development

Custom models are used when general-purpose systems cannot meet the accuracy, control, or compliance that specific tasks require. They are trained or fine-tuned on proprietary data and workflows, with evaluation and safety checks to verify performance. This approach makes models fit real platform needs instead of forcing teams to adapt to one-size-fits-all systems. We build models that align with the data, tasks, and standards our clients depend on.

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. The result is faster, more reliable access to legal guidance.

5
+

Years of engagement and trust

5000
+

Monthly search sessions, up from 100

2x

Faster path to relevant legal content

Read Case Study

Solutions

AI-powered search experience

Search solution that interprets intent, applies organizational data with RAG, and delivers precise, context-aware answers.

AI document assistant

Analyzes content from documents, images, data, and research to deliver instant insights, tailored summaries, and interactive answers.

Aeldris

Aeldris lets you build AI assistants in minutes. From search and chat to document intelligence, you stay in control with orchestration, oversight, and real-time analytics

Try Aeldris
AI Solutions

Our AI values

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

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

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

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

Bring your use case, we will build it

In a 2-week risk-free pilot, see what AI can do for your business before you invest further

Frequently asked questions

What AI solutions are businesses using today?

Common solutions include predictive analytics, process automation, natural language processing (NLP), computer vision, and generative AI for text, images, and code. Retrieval-augmented generation (RAG), AI assistants, and workflow agents are also being adopted for support, compliance, and knowledge-heavy processes.

How do we get started?

Most companies start with a proof of concept tied to a single priority workflow. The first AI use case should be narrow and measurable, such as support queries, compliance reviews, or document-heavy processes.

What resources are needed from our team?

Business leads set goals, IT connects systems, and compliance teams review boundaries. Client effort is kept light, focused on configuration and validation.

What risks and challenges should we plan for?

The main risks are data quality, integration with legacy systems, and regulatory requirements. AI systems can also drift or return incomplete answers. These challenges are managed with monitoring, review loops, and governance frameworks.

How do AI pilots scale into long-term systems?

AI Pilots validate a single use case. If results are proven, integrations and governance expand step by step: production rollout, wider adoption, and ongoing monitoring under AI operations.

How is the ROI of applied AI solutions measured, and what results do companies see?

ROI is tracked through cycle times, hours saved, and faster access to accurate information. Typical examples include lower support costs with chatbots, reduced waste in supply chains with predictive models, or higher conversion rates with personalization. Many companies see results within 6 to 18 months.

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