What is Artificial Intelligence (AI), and how does EXcelerate use it?
EXcelerate applies Artificial Intelligence, including Large Language Models and specialised agents, to analyse unstructured feedback and deliver clear, data-driven employee experience insights with speed, consistency, and accuracy.
Working with Artificial Intelligence (AI)
What is AI?
Artificial Intelligence (AI) is an umbrella term for computer programs that can perform
tasks we normally associate with human thinking, including spotting patterns, learning
from examples, and making decisions. Modern AI systems learn by processing vast
amounts of data, refining their rules as they go, so they improve over time without
being given step-by-step instructions for every situation.
When we say EXcelerate uses AI, we mean:
- Language understanding at scale: Large-language models read thousands of
online comments, reviews, and forum posts in seconds and understand the gist
of each one. - Smart helpers (agents): A team of specialised mini-programs searches for
relevant posts, removes duplicates, checks facts, scores sentiment, and flags
anything that needs human attention. - Faster, richer insight: Together, these AI components turn raw, unstructured
“passive” data into clear, benchmarked employee experience insights, far faster
and more consistently than a human team could manage on its own.
We’ll discuss these terms in further detail now:
Large Language Models
Think of an LLM as the system’s “language brain”. Trained on billions of words, it can
read messy text such as tweets, reviews, or forum posts, and instantly grasp what is
being said, paraphrase it in simpler language, and file it under the right topic. In
EXcelerate, the LLM is the engine that turns raw, unstructured comments into clear
insights about workload, leadership, culture and more. Because it is built on modern
transformer technology, it recognises context, sentiment, and nuance almost the way
people do, but at super-human speed.
Agents
Around that language brain sits a small “team” of specialised helpers called agents. Each
agent has one job: one hunts for the most relevant posts, another checks facts, another
converts sentiment into a score, and so on.
An Orchestration Engine acts like the project manager, handing off work from one agent
to the next. This modular setup makes every step transparent and auditable, mirroring
how a human research team would share tasks, but without the bottlenecks.
Overall, the LLM supplies the deep language understanding; the agents supply structure
and quality control. Working in concert, they quietly sift huge volumes of “passive”
online chatter and deliver concise, benchmarked employee experience insights.
For further information on see our technical documentation