(03)Service

03 / 07

Give your systems a more natural way to interact.

We build AI experiences that can understand, respond, and take the next step: voice agents, assistants and agents that use your knowledge and your tools — within limits you set, and with a clear path to a person.

Explore AI agents See the specializations

(01)The problem

Conversations that don't scale

Customers call after hours, ask the same questions every day, and wait in queues for answers that already exist somewhere in your documents. Inside the business, people search shared drives and interrupt colleagues for information that should be one question away.

Calls go unanswered outside office hours or at busy times.

Staff answer the same handful of questions every day.

The answers exist in documents, but finding them takes too long.

Customers repeat themselves each time they're passed to someone new.

Simple requests — a booking, a status check, a change of address — need a person to complete.

(02)The service

Agents, chatbots and automation

These words get used loosely, so here's how we use them. Automation follows a path you define. A chatbot answers inside a conversation. An agent understands a request, decides which steps to take and uses tools — your calendar, CRM or order system — to complete them.

More autonomy isn't always better. For a short list of stable questions, a simple assistant is cheaper and more predictable. An agent is worth it when requests vary and finishing them means acting in other systems.

Every agent we build has boundaries: what it can read, which actions it can take, which actions need someone's approval, and when it hands the conversation to a person.

Which one do you need?

Which one do you need?
	Automation	Chatbot	AI agent
Starts with	An event: a form, a file, a schedule	Someone asking a question	Someone, or a system, making a request
What it does	Runs fixed steps	Answers within the conversation	Plans steps and uses tools to finish them
Good for	Predictable, repeatable processes	Questions with known answers	Varied requests that need action in other systems
Main risk	Breaks on unexpected input	Wrong or invented answers	Wrong actions — so permissions and approvals matter

(03)Use cases

Where conversational AI helps

Phone lines

Answering, booking, routing and taking messages when nobody can pick up.

Customer questions

Answering from your help content, policies and order data — and handing over cleanly when it can't.

Internal knowledge

Letting staff ask questions of manuals, policies and past work instead of hunting for them.

Where to hold back

Conversations where a mistake is costly and can't be checked — medical, legal or financial advice — or where people strongly expect a human. There, AI can prepare and summarize, and a person responds.

(04)Specializations

What it covers

Each area below sets out the problem it addresses, representative capabilities, an example and the outcome we aim for.

AI Voice Agents

Phone agents that answer calls, understand what callers need and handle routine requests.

Problem: Calls are missed after hours and at busy times, and many of them are routine bookings, questions or messages.
Capabilities: Answering and routing by what the caller needs
Booking and changing appointments
Taking messages, with summaries
Handing over to a person with the context passed on
Example: A caller asks to move an appointment. The agent confirms who they are, checks the calendar, offers two times, books the one they pick and sends a confirmation text.
Intended outcome: Routine calls are handled at any hour, and staff take the ones that need them.

AI Customer Support Agents

Agents that resolve support requests over chat and email, using your help content and account data.

Problem: Support teams spend much of the day on questions whose answers already exist.
Capabilities: Answers grounded in your help content
Order and account lookups
Simple actions, such as resending an invoice
Escalation with a summary for the human agent
Example: A customer asks where their order is. The agent looks it up, explains the delay and offers to message them when it ships. When they ask for a refund, it hands over to a person with the conversation summarized.
Intended outcome: Customers get answers straight away, and people pick up escalations with the full story.

AI Sales Assistants

Assistants that answer buyers' questions, qualify interest and book the next step.

Problem: Prospects arrive outside office hours with questions, and leave before anyone replies.
Capabilities: Product and pricing answers from approved content
Qualifying questions
Meetings booked with the right person
Conversation notes in the CRM
Example: A visitor asks whether the product works with their accounting software. The assistant answers from the documentation, asks two qualifying questions, books a call with the right specialist and logs the conversation in the CRM.
Intended outcome: Interest turns into booked conversations, and sales can see what was asked.

Internal AI Assistants

Assistants for your own team: answering questions, drafting documents and handling routine requests.

Problem: People lose time looking for information and doing small tasks that could be asked for in a sentence.
Capabilities: Answers from internal documents
Drafts from templates and past work
Routine requests such as leave or IT tickets
Access limited to what each person may see
Example: A new starter asks how travel expenses work. The assistant answers from the current policy, links the source and offers to start the claim.
Intended outcome: Answers in seconds, instead of a search or a message to a busy colleague.

Custom AI Agents

Agents designed for one specific job in your business, with their own tools and rules.

Problem: Some multi-step tasks — research, triage, preparing a case — follow a pattern but need judgment at each step.
Capabilities: Task-specific planning and tool use
Structured output for the next system
Approval steps for consequential actions
A log of every step and decision
Example: An agent prepares a supplier review: it gathers recent orders, delivery problems and correspondence, drafts a summary with sources, and leaves the decision to the buyer.
Intended outcome: Hours of gathering and preparing become a draft ready for review.

AI Chatbots & Conversational Interfaces

Conversational interfaces on your website or inside your product.

Problem: People can't find what they need in menus and documentation, or give up halfway through a long form.
Capabilities: Website and in-product chat
Guided conversations that collect information
Conversations in several languages
Handover to email, a form or a person
Example: Instead of a long form, a visitor describes their project in their own words. The interface asks for the missing details and sends the team a structured summary.
Intended outcome: Easier to ask, easier to answer, with organized information at the end.

Knowledge & Retrieval Systems

Systems that find the right information in your documents and answer with their sources.

Problem: Knowledge is spread across documents, wikis and tickets — and a general AI tool can't see it, or fills the gaps with guesses.
Capabilities: Search across documents, wikis and tickets
Answers that cite their sources
Access rules applied before anything is retrieved
Saying so when the sources don't support an answer
Example: A support agent asks how a feature behaved in an older version. The system finds the relevant release notes and tickets, answers in two sentences and links both.
Intended outcome: Answers people can check, drawn only from information they're allowed to see.

Agent Integration & Orchestration

Connecting agents to your systems, and coordinating agents and workflows reliably.

Problem: An agent is only useful if it can act in your systems — and that access has to be controlled.
Capabilities: Tool and API connections for agents
Scoped credentials and permissions
Coordination between agents and workflows
Monitoring of actions and running costs
Example: A support agent can read orders and issue small credits. Anything above a limit becomes an approval request to a team lead, with the agent's reasoning attached.
Intended outcome: Agents that do real work, inside limits you control.

Voice & Telephony Integration

Connecting voice agents to phone numbers, call centers and the systems behind them.

Problem: The AI is only part of a phone system: numbers, transfers, recordings and customer records all have to work together.
Capabilities: Phone number and SIP connections
Warm transfers to people or queues
Recordings, transcripts and summaries
Call outcomes written to your CRM
Example: After each call, the transcript and a summary are saved to the customer's record. When the agent transfers a call, the caller's details arrive on the receiving person's screen.
Intended outcome: Voice agents that fit your existing phone setup and records.

(05)In practice

A call, start to finish

How a voice agent might handle a rescheduling call — and where it stops.

01
Answer
The agent picks up, says it's an automated assistant and asks how it can help.
02
Understand
It recognizes a rescheduling request and confirms the caller's identity against your records.
03
Check
It looks at the calendar through its connection and offers available times.
04
Act
It books the chosen time and sends a confirmation message.
05
Hand over
If the caller asks something outside its scope, it transfers them to a person with a summary.
06
Record
The transcript, summary and outcome are saved to the customer's record, ready for review.

Illustrative. What the agent may do, and when it hands over, is set for each business.

(06)Implementation

What goes into a dependable agent

Knowledge it can use

We decide which sources the agent may draw on, keep them current, and make it cite them.

Context

The agent keeps track of the conversation and the relevant history — only what it needs, for as long as it needs it.

Tools and permissions

Each connection has a defined scope. Read-only access comes first; actions are added one at a time, with approval for anything consequential.

Escalation

We define when the agent hands over — certain topics, low confidence, frustration, or simply being asked — and make sure the person receiving it gets the full context.

Voice specifics

On the phone, speed and turn-taking matter as much as accuracy. We test with realistic calls, including interruptions and background noise.

(07)Quality & reliability

Testing, and honest limits

Evaluated before launch

We build test sets of real and awkward requests, and check answers, actions and refusals before anyone outside the team uses the agent.

Tested against misuse

We try to make it ignore its instructions or reach data it shouldn't, and fix what we find.

Reviewed in use

Conversations, actions and handovers are reviewed regularly; problems become fixes and new test cases.

Clear that it's AI

Agents say they're automated, and people can always reach a person.

Limits stated plainly

Language models can misunderstand, and can be wrong with confidence. That's why answers point to sources and consequential actions need approval.

(08)Related work

Related work

Client projects, and concept builds where they're labelled as such.

01Concept build · Healthea

Healthea: AI Knowledge Brain

An AI assistant that lets healthcare staff ask questions in plain English and get answers from approved internal documents, with sources attached.

02Concept build · Niasus

AI Customer Support Intelligence

Turning thousands of support tickets into answers about what's really going wrong, and why.

(10)FAQ

Questions about AI Agents & Voice

A chatbot answers within the conversation. An agent can also act — look up an order, book a slot, update a record — using the tools we connect and within the limits we define.

Yes. Our agents say they're automated, and they hand over to a person when asked or when a request is beyond them.

Answers are grounded in your approved sources and cite them, the agent is instructed to say when it doesn't know, and we test for this before launch. That reduces the risk rather than removing it, which is why consequential actions need approval.

Usually, yes — through call forwarding or a SIP connection with your phone provider. We check your setup early.

Only what we connect it to, at the access level we agree. We normally start read-only and add actions one at a time.

We track resolved requests, handovers and the reasons for them, and review samples of conversations. What we find there becomes fixes and new test cases.

(11)Contact

Start with oneconversation.

Tell us which calls, questions or requests take up your team's time. We'll suggest where an agent could help — and where a person should stay.

Explore AI agents

contact@overc.io