AI Applications & Knowledge

Conversational AI

The difference between a useful assistant and an expensive irritation is scope. We build assistants with a defined remit, grounded answers, and a clean handoff to a person the moment the conversation leaves that remit.

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The business problem

Customers learned to type 'agent' immediately

A generation of chatbots taught people that the assistant is an obstacle between them and someone who can help. That reputation was earned: bots that could not see the account, could not act, could not admit ignorance, and would not escalate. Replacing the underlying model does not fix any of that. An assistant that hallucinates a refund policy is worse than the decision tree it replaced, because now it is confident.

What we do

Define the remit, then enforce it

We start by deciding what the assistant is allowed to handle and what it must hand over, then build so that boundary holds under pressure. Answers are grounded in your published content and cite it. Where the assistant needs account context it reads from your systems under the customer's own entitlements. Escalation carries the full conversation and the retrieved context, so the person picking it up does not start from nothing. Every conversation is logged and sampled for review.

Conversational AI

Capabilities

Customer AI Assistants
Employee Assistants
AI Support Assistants
Enterprise Chat
AI Help Desks

Common use cases

Resolve routine customer enquiries end to end, escalating anything outside the defined remit.
Give support agents an assistant that drafts the reply from documentation and account history.
Handle internal service desk requests for access, equipment and policy questions.
Qualify inbound enquiries and route them with the context already gathered.

How we deliver

Shape

Turn the request into a specification: who uses it, what a correct answer is, who decides.
Ground

Connect to the content and systems that hold the answers, respecting existing permissions.
Evaluate

Score against a labelled set built from your own cases, before anyone outside sees it.
Ship & operate

Staged release with monitoring, cost control and a regression suite that guards quality.

Technology

OpenAI
Anthropic
Azure OpenAI
pgvector
Elasticsearch
Microsoft 365
SharePoint
Confluence
Salesforce

Security & governance

Answers are grounded in your own content and carry citations, so a reader can check them. Retrieval respects the permissions already set on the source, which means a user never sees through the application what they could not see directly. Prompts, retrieved context and responses are logged for audit, and evaluation runs continuously rather than once at launch.

Engagement models

AI Project

We take responsibility for designing and delivering a defined AI solution.

Dedicated AI Team

Long-term dedicated engineering capacity built around your stack and delivery model.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

We design the handoff first

Most conversational projects treat escalation as a failure state and design it last. We treat it as a feature and design it first, because a confident wrong answer costs more than an honest transfer. That single decision is usually what separates an assistant customers tolerate from one they route around.

Selected clients

Frequently asked questions

How do you stop it giving wrong policy answers?

Answers come from your published content with citations, not from the model's general knowledge. Where the content does not cover a question, the assistant says so and offers a handoff rather than reasoning its way to an answer.

Can it actually do things, or only talk?

It can act where you allow it, under the customer's own entitlements and with approval gates on anything consequential. Read-only is the usual starting point, with write actions added once accuracy is demonstrated.

What does escalation look like?

The full conversation, the retrieved context and the assistant's assessment go to the agent, so the customer does not repeat themselves. Escalation triggers are configurable and include explicit customer request, low confidence, and any topic outside the remit.

How do we know it is performing?

Resolution rate, escalation rate and reason, customer satisfaction where you already measure it, and a sampled human review of transcripts. Quality is monitored continuously, not signed off once.

Which languages can it handle?

Whichever your content supports. Grounded answers are limited by the languages your source material exists in, so multilingual support is usually a content question before it is a model question.

Related capabilities

AI Applications & Knowledge

Enterprise Knowledge AI

Turn fragmented internal knowledge into secure assistance that respects existing permissions.

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AI Applications & Knowledge

Voice AI

Spoken interfaces for support, scheduling and triage, connected to the systems that hold the answers.

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AI Applications & Knowledge

RAG Development

Retrieval systems that ground answers in your own content, with citations and measurable accuracy.

Learn more

Discuss Your AI Initiative

Customer and employee assistants with controlled scope, grounded answers and clean handoff to people.

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