AI Operations
Voice and chat agents, agent assist and workflow automation: designed, deployed and run by a team that has managed customer operations since 2000. Built on the platform that suits the work, because we have none of our own to sell.
Why operators
An AI agent is easy to switch on. Running one is the same job as running a team: scope, training, quality, and someone accountable.
That is the job we have done every day for 26 years. We bring it to AI: we decide what the agent may do, test it against real contacts, watch its quality the way we watch a new hire, and stay responsible for the result.
We select from established platforms and models for each task. Your data rules and your approval decide which.
If your current platform can do the job, we work inside it. Our interest is the outcome, not a licence.
The team that scopes the agent supervises it in production, alongside the people it works with.
What we run
Take one, or all three. Each is scoped to a defined piece of work, with a named owner and a way to check the result.
01AI-Run
Agents that answer on the phone and in chat, complete routine requests from start to finish and tell the customer what happens next. Every agent has a written scope, approved knowledge and a clear route to a person.
02AI-Assisted
A specialist leads the conversation. AI finds the right guidance, prepares the case summary and drafts the next step, so attention stays on the customer. The same tools let supervisors review far more conversations and coach on what they find.
03Behind the contact
Much of the delay a customer feels happens after the conversation ends. We automate the sorting, routing and checking between people and systems, and give every automated step an owner and a recovery path.
DirecOne Relay
Relay is how we decide who leads each kind of conversation, and where a person steps in. The same three parts every time: the customer, the AI and a person. What changes is who leads.
Voice and chat agents take the contact, resolve the routine and the repetitive, and close the loop. A trained person is one handover away, and the handover is a designed moment, not a fallback.
Best for High-volume care, order status, appointment setting, first-line technical support, outbound qualification.
Customer to AI to resolved. Judgment cases go to a person.
Your customer talks to a person from the first second. Beside that person sit real-time guidance, the right knowledge and an automatic summary, so the conversation is faster and the record is complete.
Best for Complex product support, retention, collections, regulated conversations.
Customer to person, with AI beside them the whole time.
For the conversations where the answer depends on reading the person in front of you: a sale, an escalation, a customer whose circumstances have changed. AI stays in the background, on quality, coaching and forecasting.
Best for B2B sales, key accounts, escalations, sensitive and brand-critical moments.
Customer to person. AI in the background.
Settings are not fixed. Contacts move between them as the AI learns and the team specialises.
See it handled
The same operation handles all three. What changes is who is in front, and how much the person behind them already knows.
Hi, my order was meant to arrive yesterday. Can you tell me where it is?
I can. It left the depot this morning and is out for delivery today, between 2 and 5 pm. Would you like a text when the driver is close?
Yes please.
Done. You will get a message about 30 minutes before arrival. Is there anything else I can help with?
Why this lead
The answer is in one system, the risk is low and the customer wants speed. A person adds nothing here except a wait.
Resolved by AI
No queue, no transfer, and the customer is told what happens next.
My bill is double what it normally is and I have not changed anything. I cannot afford this.
I am sorry, that is a worrying thing to open. I can see the increase came from a plan change on the 3rd. I am bringing in a specialist who can put this right, and I am passing on everything you have told me.
Passed to a specialist with the account history, the disputed charge and a suggested correction. The customer does not repeat anything.
Hello, I am one of the billing specialists and I have your account open. You are right, the plan was changed in error. I have reversed it and credited the difference. You will see the corrected bill today.
Why this lead
Money, worry and a possible error on our side. AI does the finding. A person decides, apologises and puts it right.
Resolved by a specialist, with AI beside her
The customer explains once. The specialist starts with the facts and the authority to fix it.
This is the fourth time I am calling. My service drops every evening and I work from home.
I am sorry. Four calls is too many, and I can see the notes from each one. I am not going to ask you to restart anything. Tell me what the evenings look like.
It goes around seven, most nights, for about an hour.
That pattern matters. It points away from your equipment. I am opening this with our network team now and I will call you tomorrow at ten with what they find. This stays with me until it is fixed.
Why this lead
A repeat contact, a frustrated customer and a problem that spans systems. This is where judgment and patience earn the relationship.
Owned by one person to the end
One named owner, a next step with a time on it, and a customer who is no longer starting from the beginning.
Who leads what
This is the first thing we produce in a design sprint: your contact types, each assigned a lead, with the reason written down. An example of how it tends to fall.
One system holds the answer and the customer wants it now.
A defined task with a clear finish that can be checked.
Scripted diagnosis, with a person one step away when the script runs out.
AI assembles the facts. A person decides, and explains the decision.
The specialist investigates with the right knowledge already on screen.
Offers have limits, and the reason a customer wants to leave matters more than the script.
The customer has already been let down once. Someone has to own it.
This calls for listening and discretion, inside procedures you approve.
A relationship is being built, and trust is the product.
An illustration, not a rule. Your scope, your risk and your customers decide where each contact sits, and contacts move as the evidence comes in.
Where a person steps in
An AI agent needs to know when to stop. We write the triggers with you and test them against real contacts before anything goes live.
The request, the information or the answer falls outside what has been verified.
Distress, frustration or vulnerability. The customer needs to be heard, not processed.
A commitment, a concession or a decision the relationship depends on.
The program's rules call for a person to review, or for your approval.
When a trigger fires, the conversation arrives with its history, the reason and a suggested next step. The customer does not start again.
How it starts
You decide at every step, on what the last one showed. Nothing here commits you to the next.
2 weeks
We map your contacts with you and decide who should lead each one.
90 days
One or two contact types, live, with AI agents and a trained team side by side.
Then
Extend what worked. Retire what did not. Move contacts between settings as results allow.
Timings are typical and depend on the complexity of the program.
See the starter podsOur toolbox, your requirements
We do not build models and we have no software to sell. We choose, configure and run established platforms around your systems, your data rules and the work itself.
01
The contact platforms we have run day to day: voice, routing, chat and messaging. We work inside yours, or host one for you.
NICE
Five9
Genesys
Cisco Webex
02
Models chosen task by task, for knowledge, drafting, summaries and automation. Which one depends on the job, your data rules and your approval.
Claude
Gemini
ChatGPT
03
Where performance, case detail and the team's own conversation meet, so a supervisor sees the day as it happens.
Microsoft
Power BI
Tableau
Slack
04
Secure access, telephony and the systems of record behind the floor, set up to your security requirements.
Palo Alto Networks
Citrix
Grandstream
Oracle
Tools are chosen for each program, with your approval and inside your data rules. The names show where we have hands-on delivery experience. They do not imply endorsement, certification or a formal partnership.
Governance
The questions a risk or compliance team will ask, answered before launch rather than after.
We agree the environment, the licences and what information may be used before any data is touched. Client approvals stay with the client.
Every agent is run against real contact types and edge cases first. Scope, knowledge and limits on what it may promise are written down.
We track completed work, quality, repeat contacts and cost inside your program. We quote no AI results from anywhere else, because they would not be yours.
Practical questions
No. We select from established platforms and models, then design, configure and run them around your work. That keeps the choice open: if a better tool appears, we can move without a rebuild.
Usually, yes. We have delivery experience across NICE, Five9, Genesys and others, and we work either in a client-owned stack or host the technology ourselves.
The conversation passes to a trained person together with its history, the reason for the handover and a suggested next step. The triggers for that are agreed with you and tested before launch.
Yes. AI Operations stands on its own. We can run the AI layer alongside your in-house team, alongside ours, or both.
We start from one task and its risks. An automated step needs an approved action, a way to verify the result and a route for exceptions. We look at how it performs before widening what it may do.
By completed work, quality, repeat contacts and cost, compared with a baseline taken before the pilot. You see the same figures we do, every week.
Start here
A direct conversation with the people who run the operation. Expect a view on scope, a pilot design and a timeline.