# A chat that fetches context itself gives better answers

CoreAI's agentic tool-calling lets the AI decide for itself when it needs more context, and how to fetch it – exactly the way a skilled advisor would. Most chatbots, by contrast, get only one shot: they make a single lookup against your question and answer from what they found, and if the question is complex, the answer follows suit. When coreAI needs more, it retrieves again with a more precise phrasing. The result is more precise answers, fewer misunderstandings, and an AI that actually digs into the details before it answers.

## Traditional chatbot vs. CoreAI agentic context retrieval

|  | Traditional chatbot | CoreAI agentic tool-calling |
|---|---|---|
| Retrievals per question | One fixed lookup | Several – retrieves again until the answer is good enough |
| Who controls the retrieval | Pre-programmed flow | The AI decides for itself when to retrieve, and with which terms |
| Complex questions | Often misses half | Handles several criteria at once |
| Filtering on properties | Keyword or category only | Price, volume, weight, power, date and more |
| From–to ranges | Not supported | Builds precise ranges on any property |
| Unit conversion | Manual | Feet to cm, tonnes to kg – automatic |
| Approximate price | Exact match or nothing | Reads "about 5,000" as a sensible price band |
| Search technology | Keywords | Hybrid: semantic vector search + keywords |
| The customer must know | Item numbers, categories, exact words | Just natural language |

## What "agentic" means – simply put

CoreAI's agentic AI treats context retrieval as a tool it can use itself – several times if needed. A traditional chatbot, by contrast, treats every question as a one-off lookup.

When you ask a question that requires a lookup in the knowledge base, the AI does exactly that: it fetches relevant context, evaluates the results, and retrieves again with a better phrasing until it has what it needs. You notice none of the complexity – you just get a better answer.

## One retrieval, the entire knowledge base

When the AI fetches context, it hits your entire knowledge base in a single operation – articles, documents, web pages, uploaded content, API data and products. You don't need to say where it should look; CoreAI fetches the most relevant content no matter which source it comes from.

The retrieval combines semantic vector search – which understands the meaning behind the words – with keyword search that matches exact terms like item numbers and names. That way the AI finds the right information whether the customer uses technical terms or everyday language.

## Advanced retrieval with ranges and properties

CoreAI translates natural language into precise filters before the retrieval runs – without the customer needing to know how the database is built. Product properties such as volume, weight, length, payload and power are read automatically out of the product data on import and normalised to metric units, so they can be filtered on accurately.

Some examples of what the customer can ask – and what CoreAI understands:

- "Do you have a compressor with a tank between 50 and 100 litres?" → builds a from–to range on volume and fetches only products within that span.
- "I need a sledgehammer weighing more than 2 kg" → sets a lower bound on weight.
- "I have a 23-foot boat – do you have trailers that fit?" → converts feet to centimetres automatically and filters on length.
- "Trailer with a payload over 3 tonnes" → converts tonnes to kilos and filters on payload.
- "A hammer weighing more than 1 kg and costing less than 1,000 kr" → combines two properties – weight and price – in a single retrieval.
- "Angle grinder between 1,500 and 2,000 watts under 3,000 kr" → combines a power range with a price limit.
- "What's the difference between A1324 and B1325?" → fetches both products precisely via item number.

What they all share: the customer uses everyday language, and CoreAI translates it into structured queries behind the scenes.

## Smart interpretation built in

CoreAI also handles interpretation that would otherwise require manual logic:

- Approximate price – "about 5,000 kr" automatically becomes a sensible price band from 75 % to 125 % of the amount, so the customer gets products in the right price range instead of only exact matches.
- Unit conversion – feet become centimetres, tonnes become kilos, everything normalised to metric before filtering.
- Date periods – "this autumn" or "between January and April" are translated into concrete from–to dates.
- Type scoping – product questions are scoped automatically to products, job questions to open positions.

In addition, the retrieval can be scoped on stock status, so the customer always gets relevant and available results.

## 3 reasons this gives a better customer experience

### Right answer to complex questions

When a question contains several criteria at once – volume, power, price and type – they are all handled in one go, instead of missing half.

### No frustrated customers who can't find their way

The customer doesn't need to know item numbers, categories, units or exact search words. Natural language is enough.

### Sells smarter

When the customer says what they have to work with and what they're after, products in the right price range and specification are presented automatically – and the path from question to purchase gets shorter.

## In summary

The agentic tool-calling makes CoreAI more than a chatbot – it becomes an active assistant that decides for itself when to fetch context and what to fetch, and that reasons its way to the right answer. Combined with automatic interpretation of prices, dates, units and ranges, CoreAI understands what the customer really means, and fetches exactly what they need from the entire knowledge base – including the product catalogue.

Smarter context. More precise answers. Happy customers.