An AI Knowledge Assistant is an AI system where specialised agents work together to turn static documentation into dynamic, searchable intelligence, giving every employee instant access to expert-level insights.
An AI Knowledge Assistant turns hours and days of hunting for answers into an instant lookup, accelerating your decision-making and business growth.
Cuts training from weeks to days, enabling new hires to contribute faster.
Delivers instant, context-rich answers for better, faster choices.
Allows specialists to focus on high-value, strategic initiatives.
Makes expert knowledge accessible to everyone when they need it.
Streamlines processes and removes bottlenecks.
Enables faster execution and better-informed opportunities.
Provides quicker, more accurate responses that improve service quality.
From structuring and enriching your data with the right knowledge infrastructure to deploying specialised agents for accurate retrieval and evaluation, we ensure your knowledge assistant delivers precise, reliable answers. Built on Faktion’s Evaluation-Driven Development process, we continuously monitor performance, capture feedback, and refine the system to make it smarter and more trusted over time.
From multi-agent architecture to evaluation-driven development, we deliver AI Knowledge Assistants that are accurate, trusted, and integrated across your enterprise.
Break complex workflows into focused tasks like retrieval, planning, acting and evaluation. The result is higher accuracy, faster iteration and assistants that adapt across business domains.
Evaluation is baked in across retrieval, reasoning and answers. Feedback and usage signals are turned into actionable tasks by evaluation agents, so relevance, accuracy and trust improve with every release.
We transform fragmented, unstructured data into secure, enriched knowledge sources with metadata and taxonomies. This ensures every answer is context-aware and reliable.
Confidence scores, fallbacks, and traceability give users full visibility. Low-confidence answers escalate to domain experts, while verification layers prevent hallucinations.
Agents integrate directly into your tools, Salesforce, SharePoint, Slack, Teams, so insights and feedback are captured where your people already work, boosting user adoption.
Unlike single-model solutions, multi-agent architectures break complex workflows into specialised tasks, retrieval, planning, acting, and monitoring, allowing for more accurate answers and evolving performance.
Reliability and accuracy is the two key factors for an AI system to achieve user adoption. That's where Evaluation Driven Development (EDD) comes in.
EDD ensures any implicit or explicit feedback and signals are captured, turning them into actionable tasks for domain experts and AI engineers to improve the system until it reaches the necessary reliability and accuracy, hence achieving user adoption.
We make your knowledge base AI-ready by converting raw data into structured, enriched repositories with intelligent metadata, taxonomies, and access controls.
But it doesn’t stop there. Through intuitive interfaces and workflows, domain experts will stay in control and can validate sources, check relevance and accuracy, flag outdated content, and review metadata.
Users see how confident the system is in every answer, and low-confidence outputs trigger safe fallbacks like domain-expert escalation.
This builds trust while post-processing checks (with reasoning) prevent hallucinations and make the system reliable in critical business contexts.
Integrate seamlessly into your enterprise tools so insights are delivered and feedback is captured right where your domain experts work.
Embedding AI Knowledge Assistant in familiar platforms boosts adoption, accelerates productivity, and ensures it not only informs decisions but drives action.
We begin with a deep dive into your knowledge ecosystem, auditing structure, taxonomy, metadata, and domain-specific sources. Together, we define what success looks like for your Knowledge Management Assistant and align stakeholders on shared goals and expectations.
We prepare your data for AI by structuring and enriching it with intelligent metadata and taxonomies, then deploy the foundational Retrieval-Augmented Generation (RAG) baseline. From day one, we embed evaluation pipelines so every agent interaction can be measured, monitored, and improved.art systems that learn, adapt, and handle complex tasks on their own.
We develop specialised agents for retrieval, reasoning, and observability, integrating them directly into your existing tools. Every step is informed by user feedback and performance data, ensuring accuracy, reliability, and adaptability across your departments.
Once live, we track system performance in real time, capture both explicit and implicit feedback, and close the loop with continuous improvement cycles. The result: a knowledge assistant that stays aligned, accurate, and increasingly valuable over time.
Explore our cutting-edge AI solutions and success stories.
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