Use-case discovery
Identify the support questions, audience and approved knowledge.
Data boundaries, access requirements and pilot criteria.
AI Automation · Chatbots
Custom enterprise AI chatbot development for first-line (L1) help desk support
A good support chatbot closes the easy tickets before they become tickets: the password policy question, the VPN setup guide, the "where's the holiday form". A bad one makes up an answer and wastes everyone's time. The difference is almost entirely in how it's grounded, scoped and handed off.
/ Fit check
Best for
IT and customer support teams with a working knowledge base and plenty of repeat questions
Not for
Teams with no documentation yet. A bot can only be as good as its sources
Deliverables
Knowledge scoping, bot build, integrations, test set, handoff rules and usage reporting
/ Direct answer
What is custom enterprise AI chatbot development?
Custom enterprise AI chatbot development combines a language model, selected knowledge sources and integrations with systems such as ticketing and identity. The work includes access controls, answer-quality testing and a route to a person when the bot cannot help. For help desks, the aim is to handle suitable routine questions and requests while keeping sensitive decisions with the responsible team.
- Selected knowledge sources
- Tested access controls
- Handoff to the support team
/ Scope your engagement
Test the answer, the uncertainty and the handoff.
We also deliver AI and automation alongside BPro's core IT operations. Start with a workflow where permissions, human review and the cost of an error are understood.
Chatbot pilot
Test sourced answers, uncertainty and human escalation.
Representative conversations and reviewer feedback.
Operate and improve
Agree content updates, incident reporting and running costs.
An owner for knowledge quality, monitoring and handoffs.
Example support chatbot acceptance scenariosView an illustrative deliverable
This example illustrates the record format. It is not a client result or a service-level commitment. Your proposal lists the deliverables and responsible people.
| Scenario | Expected behaviour | Acceptance check |
|---|---|---|
| Known support question | Answer from approved, current material with a source | Reviewer verifies relevance and access permissions |
| Unsupported question | Explain the limit and ask for useful context | No invented policy, price or technical instruction |
| Sensitive or urgent request | Route to an appropriate person and contact path | Do not request passwords or promise work has started |
What to bring to the first conversation
Support audience, approved knowledge sources, systems involved, permissions and the team receiving handoffs.
/ 01
Which L1 help desk requests can an AI chatbot resolve?
The routine first-line (L1) requests: how-to questions, policy lookups, ticket status checks, guided troubleshooting and simple requests such as starting a self-service password reset. Anything involving judgement, risk or an exception goes to an L1 or L2 engineer, with the conversation attached.
| Good chatbot candidates | Should go to an engineer |
|---|---|
| "How do I set up the VPN on my Mac?" | "I think I clicked a phishing link" |
| "What's our password policy?" | "I need access to the finance share" |
| "What's happening with my ticket?" | "My laptop was stolen" |
| "Outlook keeps asking for my password" | Anything involving money, HR or legal |
/ 02
How do you reduce incorrect AI chatbot answers?
We connect approved knowledge sources, test representative questions and provide source references where supported. The bot should acknowledge missing information and offer a handoff. Retrieval-augmented generation can improve relevance, but it does not guarantee a correct answer. Testing needs to cover missing sources, conflicting guidance and changes to content or configuration.
/ 03
How do you keep company data safe in an LLM chatbot?
We review source permissions, identity checks, data retention and the selected provider's data-use terms before connecting company information. Retrieval permissions must be enforced by the application and tested with both allowed and denied access. Sensitive actions need approval controls outside the model. Users should not enter passwords, payment details or other secrets into chat.
A chatbot can make existing permission problems easier to discover. Before connecting a source such as SharePoint, check who can read it, remove unintended access and confirm that the retrieval layer preserves the intended restrictions.
/ 04
Should every support chatbot use an LLM?
No. A rule-based bot can suit a defined set of questions and approved responses. Its content and routing still need maintenance and testing. An LLM may help interpret varied questions and retrieve relevant guidance, but requires additional checks for answer quality, permissions and escalation.
/ 05
How does the chatbot hand off to a person?
When confidence is low, the user asks for a person, or the topic is on the never-automate list, the bot opens a ticket in your helpdesk with the conversation, the user's details and what it already tried, then tells the user what happens next. The engineer shouldn't need to ask a single question the bot already asked.
/ 06
How do you measure whether the chatbot is working?
By resolution rate (conversations that ended without a ticket and without the user coming back), handoff rate and reasons, answers marked unhelpful, questions with no good source, and ticket volume in the categories the bot covers. The "no good source" list is the most useful of all, because it tells you which documentation to write next.
Frequently Asked Questions
We're model-agnostic and choose based on your data rules, existing contracts and the job. Many Microsoft 365 organisations prefer Azure OpenAI because it sits inside their Azure agreement; others use Anthropic or OpenAI business APIs directly.
Yes. An internal IT help desk bot works best wherever staff already talk, which is usually Microsoft Teams, Slack or Google Chat. Website and client portal deployments are common for customer-facing support.
The delivery plan depends on the knowledge base, integrations, access controls and testing required. We define the first release and its acceptance checks before confirming the schedule.
There's a build cost and a running cost. Running cost is mostly model usage, which scales with conversation volume, plus hosting. We estimate both during scoping from your expected volume.
Yes, within limits we agree together: opening tickets, checking status, starting a self-service password reset, or requesting access through an approval flow. Anything irreversible or sensitive stays behind a human approval.
Then that's the first job. We usually audit and fix the most-used articles before the build, because a chatbot faithfully repeating outdated guidance is worse than no chatbot at all.
/ Next step
Scope a support chatbot pilot
Support audience, approved knowledge sources, systems involved, permissions and the team receiving handoffs.