Question 1

Can TBI help us define our AI agent before development begins?

Accepted Answer

Yes. You do not need to provide a complete technical specification. You can start with the task, process, users, systems, and outcome you want to improve. We help you assess the requirement, understand what information and integrations are involved, and recommend the right level of complexity before development begins.

The goal is not to push every idea into a complex custom build. We help define an approach that makes sense for the requirement and gives you a clear starting point.

Question 2

How do you decide what type of AI agent we need?

Accepted Answer

We look at the task, the systems involved, and the type of decision-making required before recommending an architecture. Some requirements are best served by a simple workflow automation, others need retrieval over your business data, and some require a fully agentic system with tools, memory, and multi-step reasoning.

We match the complexity to the requirement rather than defaulting to the most advanced option, and we walk you through the reasoning so the recommendation is clear.

Question 3

Can you configure Claude or another existing AI platform instead of building a custom agent?

Accepted Answer

Yes. Not every requirement needs a custom-built agent from scratch. In many cases, configuring an existing platform such as Claude, along with the right prompts, tools, and guardrails, can meet the requirement faster and at lower cost. We assess whether an existing platform can be configured to do the job before recommending a custom build, and we are upfront when a custom approach is genuinely necessary.

Question 4

When would you recommend LangGraph?

Accepted Answer

We recommend LangGraph when a requirement needs explicit control over multi-step reasoning, branching logic, state that persists across steps, or coordination between multiple tools and sub-agents. It is not something we reach for by default. For simpler workflows or single-purpose agents, a lighter-weight approach is usually a better fit.

We choose the framework based on what the architecture actually needs, not on what is trending.

Question 5

Can you build RAG-powered AI agents using our business knowledge?

Accepted Answer

Yes. We can build agents that retrieve answers from your documents, knowledge bases, product data, or internal systems, so responses are grounded in your actual business information rather than general knowledge.

This includes structuring and indexing your content, setting up retrieval, and testing the agent against real questions your team or customers are likely to ask.

Question 6

How do you communicate with us during the project?

Accepted Answer

We keep communication direct and consistent through regular updates, working sessions, and access to whoever is building your solution rather than routing everything through account management layers. You will know what is being built, why, and what to expect next at each stage of the project.

Question 7

Can the agent connect with our existing software and APIs?

Accepted Answer

Yes, where suitable access and integration options are available. We can design agents around APIs, databases, CRMs, internal applications, helpdesks, SaaS platforms, and other approved business systems. We assess the systems involved before deciding how the solution should connect and what permissions the agent should have.

The aim is to add AI around the tools your team already relies on rather than creating unnecessary disruption or requiring a complete rebuild of your existing processes.

Question 8

How do you test and evaluate an AI agent?

Accepted Answer

We define expected outcomes for the agent and test it against a set of relevant scenarios before it goes live, including edge cases and situations where it should decline to act. This is not a one-time check.

We continue evaluating performance after launch and use real usage data to catch issues and refine behaviour over time.

Question 9

Can humans approve or review an agent's actions?

Accepted Answer

Yes, where appropriate for the use case. We can build in approval steps, review checkpoints, and escalation paths for actions that carry risk, cost, or need for judgement, so the agent supports your team's decisions rather than operating without oversight.

The right level of human involvement depends on the requirement, and we help determine where that line should sit.

Question 10

What happens after the AI agent is built?

Accepted Answer

Launch is not the end of the engagement. We monitor how the agent performs in production, track where it succeeds or falls short, and refine prompts, retrieval, and workflows based on real usage. We can also support you as requirements evolve, whether that means expanding the agent's scope, connecting additional systems, or building the next agent for a different part of the business.

Question 11

What is the difference between a chatbot, conversational AI, and an AI agent?

Accepted Answer

A chatbot usually matches a message to a predefined response. Conversational AI goes further: it interprets what someone means, holds context across a conversation, and forms an answer from approved business information. An AI agent adds the ability to act — it can use tools, call your systems, and complete a multi-step task rather than only replying.

Most requirements land somewhere on that scale, and the right answer is rarely the most advanced option. We assess what the interaction actually needs to do before recommending where on that scale your solution should sit.

Question 12

Can you build voice agents, or agents that work in more than one language?

Accepted Answer

Yes to both. We build voice agents for situations where speaking is faster or more practical than navigating an interface, designed around the accents, speech patterns, and context in which people will actually use them.

For multilingual work, we build agents that hold a conversation across several languages and accommodate regional variation, so a user can move between supported languages without losing the context of what they were doing. Tone and terminology are adapted per market rather than machine-translated from one script.

Question 13

Do your agents use generative AI, and can you build generative AI applications on their own?

Accepted Answer

Yes. Generative models sit inside most of the agents we build — forming answers, summarising, drafting, and transforming information. We also build generative AI applications that are not agents at all: content and document workflows, product information generation, and internal knowledge tools for text, image, audio, or multimodal output.

That work includes adapting pre-trained models on your own data, tuning outputs for relevance and consistency, and connecting the capability to the review and approval steps your business requires before generated output is used.

Question 14

Can we start with a proof of concept before committing to a full build?

Accepted Answer

Yes, and it is often the sensible route when the open question is whether AI can produce useful results against your data. We build focused proofs of concept to test exactly that, then turn a validated concept into an MVP you can put in front of real users.

We structure a POC as maintainable software rather than a throwaway demo, so a successful experiment has a practical path into production instead of needing to be rebuilt from scratch.

Question 15

Can we access AI as an ongoing service instead of running the infrastructure ourselves?

Accepted Answer

Yes. You do not need to build and operate the full underlying AI infrastructure to use an AI capability. We can connect managed capabilities — language processing, speech, translation, classification, prediction, or content generation — into the applications and workflows you already run, and build the application layer that makes them useful in your business.

That also covers the work around the model: data flow, deployment, integrations, monitoring, and ongoing refinement. It makes it practical to introduce one defined capability first, see how it performs in your operation, and expand only when there is a clear reason to.

Question 16

How much does an AI project cost, and how long does it take?

Accepted Answer

It depends on scope, and we price and schedule after understanding what the system needs to do and what already exists around it. As a rough guide, a focused build can run around 9–12 weeks, while a complex production implementation can take six months or more.

Data readiness, integrations, security requirements, and the amount of validation needed before launch move that range more than the AI itself does. Adding one capability to an established application is a very different scope from a new product needing data pipelines, multiple workflows, interfaces, and ongoing evaluation. Share the requirement and we will define the scope before quoting.

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