AI Agent Development
AI assistants grounded in your own content and connected to your own tools, with clear limits and a human to escalate to
Starting small is welcome. See Pricing for what makes a project suitable for Starter pricing.
Most AI disappointment comes from the same place: a general-purpose chatbot that has never seen your documentation, cannot check your systems, and confidently invents an answer when it does not know one.
A useful AI agent is different. It is grounded in your own content, so its answers come from your policies, products and documentation rather than from guesswork. It can be connected to the tools you already use, so it can look something up or file something rather than only talking about it. And it knows where its limits are, so anything outside them goes to a person instead of being improvised.
We build customer-support assistants, internal knowledge assistants, document question answering, lead qualification and tool-connected agents. DesignDotIT is a founder-led studio based in Japan, working with startups and growing businesses worldwide. You get a fixed price after discovery and you own what we build.
Who this service is for
Support teams answering the same questions daily
A large share of your inbox is questions already answered in your documentation. An assistant grounded in that content handles the repeats and passes the rest to a person.
Companies sitting on documentation nobody can search
You have policies, manuals, contracts or wikis that hold the answer, but finding it takes longer than asking a colleague.
Teams losing time to internal questions
New staff and busy colleagues interrupt the same few people for process answers. An internal assistant absorbs those without adding headcount.
Businesses qualifying inbound leads by hand
Someone reads every enquiry to work out which are worth a call. An agent can ask the qualifying questions first and route what matters.
Anyone who tried a generic chatbot and stopped
You have already seen a bot invent answers or fail to reach your systems, and you need one that is grounded and knows when to escalate.
Problems this service solves
- ✦A general chatbot gives confident answers that are wrong, because it has never been shown your actual documentation.
- ✦Customers wait for a person to answer questions your help pages already cover.
- ✦Staff cannot find information buried across contracts, manuals, wikis and shared drives.
- ✦The same handful of internal questions interrupt the same few people every week.
- ✦Every inbound enquiry is read and triaged manually before anyone knows if it is worth a call.
- ✦An assistant that only talks is not enough — it needs to actually check an order or create a record.
Typical use cases
Customer-support assistants
Answers drawn from your help content and policies, available at any hour, handing over to your team when a question falls outside what it should answer.
Internal knowledge assistants
A single place for staff to ask how a process works, with answers grounded in your own handbooks and documentation.
Document question answering
Ask questions across contracts, reports, manuals or research and get answers with a pointer back to the source passage.
Lead qualification
An agent that asks the qualifying questions on first contact, captures the answers and routes serious enquiries to you.
Tool-connected agents
Assistants wired into your systems so they can look up an order, check availability or create a record, not just describe how.
Human escalation and guardrails
Explicit limits on what the agent will answer, with anything sensitive or uncertain passed to a person along with the conversation so far.
What's included
Starter Engagement
- ✓A discovery call to pick one use case worth proving first
- ✓Your content prepared and indexed so answers are grounded in it
- ✓A working agent handling that single use case end to end
- ✓Guardrails defining what it will not attempt to answer
- ✓A defined escalation path to a human
- ✓A review session with real questions from your team
Full Build
- ✓Everything in the Starter engagement
- ✓Multiple use cases and conversation flows
- ✓Connections to your own tools and data sources, agreed during discovery
- ✓Deployment into where your users already are, such as your website or an internal tool
- ✓Conversation logging so you can see what is being asked and where answers fall short
- ✓A process for updating the agent's knowledge as your content changes
- ✓Evaluation across a set of real questions before launch, so quality is measured rather than assumed
- ✓Documentation covering how the agent is configured and how to adjust it
Delivery process
Discovery call
We identify which questions the agent should handle and, just as importantly, which it should refuse and pass to a person.
Fixed-price proposal
A written scope, timeline and fixed price. If a use case is a poor fit for an AI agent, we tell you before you commit.
Grounding in your content
We prepare and index the documentation, policies or records the agent must answer from, so responses trace back to your material.
Build and connect
The agent is built, guardrails are set, and any approved tool connections are wired up and tested.
Evaluation with real questions
We run it against real questions from your team, review where answers fall short, and tune before anyone outside sees it.
Launch and handover
We deploy, hand over the code and accounts, and show you how to update its knowledge as your content changes.
What You Walk Away With
Deliverables
- ✓A working AI agent deployed where your users need it
- ✓Full source code and configuration, transferred to your ownership
- ✓All API and service accounts set up in your name
- ✓Documentation covering the guardrails, escalation rules and how to update its knowledge
- ✓A walkthrough of how to maintain and adjust it
Timeline & Pricing
Starter Engagement
$600 – $1,200
Typical timeline: 1–3 weeks
Full Build
From $1,800
Typical timeline: 3–6 weeks
Prices shown in USD, GBP and JPY quotes available on request. See the full Pricing page for how Starter and Full Build engagements differ across every service.
Technologies & integrations
Relevant case study
We don’t have a published case study for this exact service yet. Browse the full portfolio for real, completed projects.
FAQs
How do you stop the AI from making things up?▾
Answers are grounded in your own content rather than the model's general knowledge, so responses come from your documentation. On top of that, we set explicit guardrails for what the agent should not attempt, and anything outside them is escalated to a person rather than guessed at. No approach removes the risk entirely, which is exactly why the escalation path matters.
What is the difference between a chatbot and an AI agent?▾
A chatbot mostly answers questions. An agent can also take an action, such as looking up an order or creating a record in a connected system. Which one you need depends on the job, and we work that out during discovery rather than selling you the more expensive option by default.
Will our data be used to train someone else's model?▾
Not without your knowledge. We use business API tiers that do not train on submitted data by default, and we confirm the specific provider terms with you during discovery. What gets sent to the model, and what stays in your own systems, is a decision we make together.
How much does an AI agent cost?▾
Starter engagements run from $600 to $1,200 and full builds start from $1,800, quoted after discovery. Note that the model provider charges separately for usage, and those costs sit with you on your own account — we will estimate them during discovery so there are no surprises.
How long does it take?▾
A Starter engagement usually takes one to three weeks. A fuller build typically runs three to six weeks depending on how many use cases and connections are involved.
Can it connect to the systems we already use?▾
Often yes, provided the system has an API or an export we can work with. We confirm exactly what is possible during discovery rather than promising it in advance.
What happens when it cannot answer something?▾
It hands over to a person, along with the conversation so far so nobody has to start again. Defining those handover points is part of the build, not an afterthought.
Security and quality
We agree during discovery exactly what data the agent can see and what is sent to a model provider — that is a deliberate decision, not a default. We use business API tiers that do not train on submitted data, and API keys are held in your own accounts rather than ours. Access to the agent follows the same authentication and permission rules as the rest of your system, so an internal assistant does not become a way around them. Conversations can be logged for quality review where you want that, with the retention period agreed with you rather than assumed. Guardrails and escalation paths are treated as part of the build and tested before launch.
After You Launch
Every build includes a 30-day period after launch where anything not working as agreed is fixed at no cost. After that, you can reach us directly for changes and tuning, quoted per piece of work. Agents usually benefit from a review once real usage data comes in, and we are happy to do that as a one-off rather than requiring an ongoing contract.
Further reading
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