Do we need a large knowledge base before building an assistant?

No. The right starting point depends on the use case. A focused set of high-quality documents, FAQs, product information, or operational knowledge can be enough for an initial proof of concept. The assessment helps determine what information is actually required.

Can an AI chatbot use our company documents?

Yes. Business assistants can be designed to retrieve information from approved documents and knowledge sources. The important considerations are how the information is structured, indexed, updated, secured, and presented to the assistant.

How do you reduce incorrect or hallucinated answers?

A reliable business assistant should have appropriate retrieval, source controls, prompt and response boundaries, fallback behaviour, testing, and monitoring. In some use cases, it is also important to show where an answer came from or escalate questions the system cannot confidently handle.

Can the assistant connect to our existing business systems?

Potentially, yes. Depending on the use case, an assistant can connect to approved APIs, knowledge systems, CRM platforms, support systems, or other business tools. The assessment helps determine whether the assistant should simply retrieve information or also perform controlled actions.

Can this be used for an internal employee assistant?

Yes. Internal knowledge assistants can help teams find policies, procedures, product information, technical documentation, onboarding material, and other business knowledge without requiring employees to manually search across multiple locations.

What if customers are the main audience?

The same assessment approach applies. We consider the questions customers ask, the knowledge required to answer them, escalation needs, support workflows, website or platform integration, and the boundaries required for a reliable customer-facing assistant.

What happens after the assessment?

You receive a clearer view of whether an AI chatbot or RAG assistant is appropriate, what information it needs, how it should work, and what a sensible first implementation could look like. Where there is a strong fit, the next step can be a focused proof of concept or production assistant.

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AI & Data ConsultancyStart assessment

AI chatbots & RAG assistants

Turn your business knowledge into an assistant people can actually use.

Customers ask the same questions. Teams search through documents. Important knowledge sits across websites, files, databases, and internal systems. This practical assessment helps you identify where an AI chatbot or RAG assistant could create real value — and what needs to be ready before you build one.

Business-firstPractical prioritiesClear next steps

Business team exploring an AI assistant connected to company knowledge

The objective

Find the opportunity that deserves attention before committing budget, people, or technology.

Free assessment

Start with a sharper picture.

Tell us where the friction is. We'll use that context to identify the most useful opportunity and the clearest next move.

Why this matters

The value of an AI assistant comes from what it knows, what it can access, and what people can actually do with it.

A chatbot is easy to demonstrate. Building one that provides useful, trustworthy answers inside a real business is a different challenge. The assistant needs relevant knowledge, reliable retrieval, sensible boundaries, appropriate access, useful integrations, and a clear role in the workflow. This assessment helps determine where an assistant can help and what the implementation should look like.

Customers repeatedly ask questions that your support or operations team answers manually every day.

Employees spend too much time searching through PDFs, policies, manuals, websites, spreadsheets, or internal documentation to find information.

Business knowledge is fragmented across systems and documents, making it difficult to provide one consistent answer.

Your team wants an AI chatbot but is concerned about hallucinations, incorrect information, outdated documents, or answers without proper context.

You need an assistant that can work with company knowledge rather than providing generic answers from a public language model.

Existing support or knowledge processes rely on people remembering where information lives and manually moving between multiple systems.

You are unsure whether the best solution is a website chatbot, internal knowledge assistant, document Q&A system, RAG application, or a more integrated workflow.

Leadership wants to understand the value, data requirements, risks, and practical scope before committing to an AI assistant project.

What you'll receive

A useful point of view, not another generic AI score.

The goal is to leave you with a better decision — what deserves attention now, what needs work first, and what a sensible next phase looks like.

Assistant opportunity map

A focused view of the customer, employee, knowledge, or document workflows where an AI assistant could create measurable operational value.

Knowledge & data readiness

A practical assessment of the documents, websites, databases, FAQs, policies, and other information the assistant would need to access and retrieve.

Assistant architecture direction

A clearer view of whether the use case calls for a chatbot, RAG assistant, document intelligence workflow, business-system integration, or another approach.

Implementation roadmap

A practical path from initial proof of concept to a useful production assistant, including knowledge preparation, retrieval, integrations, safeguards, testing, and improvement.

Assessment framework

What determines whether the idea is worth pursuing?

Business value comes first. The technology, data, workflows, and controls need to support it.

Business use case

What problem should the assistant solve, who will use it, how often, and what improvement would make the project worth pursuing?

Knowledge sources

Which documents, websites, FAQs, policies, databases, knowledge bases, and business systems contain the information the assistant needs?

Retrieval & answer quality

Whether the available knowledge can be organised, retrieved, contextualised, and presented in a way that supports useful and trustworthy answers.

User experience

How customers or employees will interact with the assistant, what questions they are likely to ask, and what happens when the assistant cannot confidently answer.

Integrations & actions

Whether the assistant needs to connect to CRM, support, business, or operational systems and whether it should only answer questions or also trigger actions.

Safety & governance

Access control, sensitive information, answer boundaries, source attribution, escalation, monitoring, and other safeguards needed for responsible business use.

Built for

Teams that need clarity before commitment.

You do not need to have the answer already. You need enough context to ask a better question.

Customer experience leaders

Customers repeatedly ask similar questions and your team spends significant time providing answers that could potentially be handled more efficiently.

Operations & knowledge teams

Employees struggle to find policies, procedures, documents, product information, or internal knowledge when they need it.

Product & technology teams

You are considering an AI assistant, RAG application, document Q&A capability, or knowledge interface and need a clearer technical direction before building.

Business leaders

You want to understand whether an AI assistant would solve a real operational or customer problem before investing in a larger AI initiative.

How it works

From business problem to practical next move.

The assessment is intentionally lightweight. The value comes from connecting the right questions before anyone starts building.

Start with the interaction

Define who will use the assistant, what they will ask, what problem they are trying to solve, and what a successful interaction should achieve.

Map the knowledge

Identify where the trusted information lives, how current it is, how it is structured, who can access it, and what gaps could affect answer quality.

Choose the right assistant pattern

Determine whether the problem calls for a simple chatbot, RAG assistant, document intelligence workflow, system integration, or a combination of approaches.

Define the implementation path

Receive a practical recommendation for a focused proof of concept or production path, including knowledge preparation, retrieval, integrations, safeguards, and testing.

Inside the assessment

A practical decision brief.

The output is designed to help you have a better internal conversation about what to pursue, what to fix, and what should happen next.

Unlock the assessment

AI Chatbot & RAG Assistant Assessment

Preview

Assessment output

A clearer view of what matters most and what should happen next.

1Highest-value chatbot and assistant opportunities

2Customer and internal knowledge use cases

3Knowledge-source readiness

4Document and data-access requirements

5RAG and retrieval considerations

6Integration opportunities

7Key risks and governance requirements

8Recommended proof-of-concept direction

9Practical implementation sequence

Decision focus

Start with the opportunity where business value, feasibility, and readiness overlap.

Designed for useful decisions

The assessment starts with the user problem and business workflow rather than assuming that every problem needs a chatbot.

Recommendations consider knowledge quality, retrieval, user experience, integration requirements, reliability, security, and operational fit together.

The assessment helps distinguish between a useful business assistant and a demo that looks impressive but fails under real-world use.

The output is designed to be understandable to both business stakeholders and technical teams.

The assessment can stand alone or become the starting point for a focused AI chatbot or RAG implementation.

FAQ

Questions worth answering.

Still deciding whether the assessment is the right starting point? These answers cover the common questions we hear first.

A general AI chatbot can generate answers from the model's learned knowledge and the context provided to it. A RAG assistant is designed to retrieve relevant information from a trusted knowledge source and use that information when generating its response. For business applications, retrieval can help connect answers to current company documents, websites, policies, or other approved sources.

Your next move

Make your business knowledge easier to find, understand, and use.

Start with the assessment. Move from scattered ideas to a clearer decision, a practical route forward, and the right conversation about what should happen next.

Get your free assessment Discuss your project

Discover

Understand the opportunity.

Scope

Define what is worth building.

Build

Move into a focused engagement.