Yournextcolleaguejoinsthechatyoualreadyuse
Many enterprise artificial intelligence (AI) tools go unused because they ask people to go somewhere else. Digital colleagues work where the work already happens. They are named team members with a job description, a real identity, and a rule to reply only when a message is about their job.
Most AI tools are separate from where people work
Most AI tools ask you to open a new tab, type into a box and learn a new habit. But the work happens somewhere else: in the group chat where an incident is being dealt with, or the channel where a client asks for an update. People try the tool for a few weeks and then stop using it, and the pilot ends. We put the colleague in the chat room instead. It joins the way a person does, with a name, an account and a job description. When somebody writes a message, each colleague in the room decides whether the message is about its job. One may reply. Often none does, because the message is not about any of their jobs.
The same colleague everywhere
A digital colleague is the same colleague in chat, on the web, on a schedule, on a call and in a meeting. Each room keeps its history, so the colleague knows what has been said. There is nothing to learn and nothing to install. People use the chat they already have open.
The platform remembers
Memory works at three levels: the conversation, the colleague's own experience, and the team's shared knowledge. If you ask a four-word follow-up question, you get an answer without repeating yourself. A colleague added today does not answer a question the team already resolved last week.
Skills you build once
A skill is a capability you build once and any colleague can use. Each skill is tracked by version, checked for permission, and every use is logged. This means you can add capabilities without building each colleague by hand.
Work you can audit
A digital colleague follows a defined process and leaves records you can read. Approvals apply to AI in the same way as to people: if a change needs two sign-offs, it needs two sign-offs whoever proposed it. A colleague passes a matter to a person for the same reasons a person would.
Knowledge with a source
Colleagues use knowledge that has been checked and organised, with a source for every entry. Our Atlas platform does this, but you do not have to use it. You can use your own sources, start with a small set of checked documents, or use Atlas in full. The colleague uses the knowledge in the same way each time.
How a digital colleague behaves
Replies only when relevant
There is no catch-all bot. Each colleague compares every message with its own job description. If a message does not match any job, nobody replies. We designed this first, because a specialist that answers everything is the most common way these tools go wrong.
It remembers the conversation
Chat has no sessions, so the platform keeps the memory. For each message it prepares a short summary for the colleague: the most recent messages, a running summary, earlier messages that relate to the question, and the facts worth keeping.
Starts work by itself
Colleagues follow their own schedule and react to events from your systems. When they need a decision, they ask and wait. If you answer in 10 minutes or tomorrow, the work continues from where it stopped.
A job first, then a name
A colleague is defined by its job, such as first line advisor, not by a bot name. Several colleagues can do the same job, each with its own name and account. Shared work is assigned once, so two colleagues never take the same task.
Jobs a colleague can do
Every digital colleague starts as one job in one room. These are the jobs we build most often.
Colleagues that deal with your customers, in voice and chat, connected to the systems needed to resolve the request.
Contact centre colleague
A voice and chat colleague on Amazon Connect that understands the request, acts on live customer information and brings in a human advisor only when the situation needs one. Proven in production with published results.
First line advisor
A first line advisor in the room where the questions already arrive. It answers what it knows, passes on what it does not, and ignores everything else.
Colleagues that deal with your customers, in voice and chat, connected to the systems needed to resolve the request.
Contact centre colleague
A voice and chat colleague on Amazon Connect that understands the request, acts on live customer information and brings in a human advisor only when the situation needs one. Proven in production with published results.
First line advisor
A first line advisor in the room where the questions already arrive. It answers what it knows, passes on what it does not, and ignores everything else.
Governance you can check
Digital colleagues are full members of the team, so they are accountable in the same way as everyone else. Governance is part of how the team works. It is not a separate set of controls for the AI.
Every colleague has its own chat account and its own client in your identity provider. The platform works out who is calling from the credential, so a colleague cannot pretend to be another.
Each colleague is scored continuously against 31 practice rules covering model choice, tools, knowledge, safety, cost and lifecycle. Every finding names the rule, the consequence and the fix.
Before every call, the platform checks how many colleagues are chained together, whether a colleague is calling itself in a loop, how much the conversation has already cost, and whether one colleague is allowed to call another at all.
A colleague acts on your behalf only in a direct message, only when your identity is linked, and only after you confirm. The credential covers one action and expires in minutes, and every action taken on your behalf is recorded.
What a colleague can reach is set before it makes a request, so a request outside its scope cannot happen. The rules are settings: you can change the weight of a rule or remove it without a software release.
Bring your own agent using the open agent-to-agent protocol, or run one on Amazon Bedrock. Routing, memory, governance and identity stay as they are when you change the model.
Start with one job
Begin with one job in one room, this month.
- 1
Pick the room
Choose a room where the same question is asked every week, by a different person each time.
- 2
Give the job to a colleague
Write the job description, connect the knowledge it needs, and add the colleague to the room as you would add a person.
- 3
Read what it did
Every answer appears in the room, in front of the team. Read what the colleague did, including the messages it ignored.
- 4
Add a second colleague
By then everybody has seen the first colleague work, so the second is an easy decision.
Put a colleague in the room
We will help you choose the first job, connect the knowledge and read the results with your team. There is nothing to install and no new tool to learn. People use the chat they already have.