AI agents that do the work between your tools.
We build, train and run agents that take the answering, booking and follow-up off your team, day and night, and stop for a person wherever it matters.
Aurora reads a week of requests and finds the patterns.
This week’s requests
- CallWater heater leaking in the garageMon 6:42 pmUrgent
- FormQuote for a new AC unit, 3 bedroom homeMon 9:15 pmEstimate
- TextCan someone come before noon?Tue 7:03 amScheduling
- EmailInvoice question on last week’s visitTue 11:20 amBilling
- CallNo cold air upstairsTue 5:58 pmUrgent
- FormDo you service Brandon?Wed 10:34 pmService area
- TextRunning late? Tech not here yetThu 8:11 amScheduling
What Aurora found
- Most urgent calls arrive after 5 pmFour of the seven requests this week came in after the office closed.
- Scheduling questions repeatArrival windows and running late account for two of seven messages.
- Service area is unclear onlinePeople ask whether Brandon and Riverview are covered.
Simulated with a fictional business.
Built on the best model for each job, running inside our own harness.
- OpenAI
- Anthropic
- Google Gemini
- DeepSeek
- Mistral
- xAI
- Z.ai
- MiniMax
- Meta Llama
- Qwen
The tools, memory, permissions and logs stay the same whichever model runs underneath, so switching models never changes what an agent is allowed to do.
Aurora works inside the tools you already use.
No new software for your team to learn. The agent reads and prepares work in the apps you run today, with only the access the job needs.
Google. Reads the shared calendar and drafts replies in Gmail for you to approve.
Watch one request, start to finish.
Every step an agent takes is written down: what it read, which rule it followed, what it did and what it held for a person. You can read the log the next morning, or while it happens.
- 6:42:03Message inText at 6:42 pm: “Water heater leaking in the garage.”
- 6:42:04Read historyReturning customer. Heater installed by Brightwater in 2021, still under parts warranty.
- 6:42:04Check the rulesLeak means urgent. Urgent means text the on-call tech before replying.
- 6:42:09Act inside the rulesTexted Marcus (on call). He can arrive 7:30 to 8:15.
- 6:42:11Reply and holdTold the customer how to shut off the supply and offered the window. Booking waits for a yes.
- 6:42:12Log itWrote the summary to the job board so the office sees it in the morning.
Start with a specialist.
Ten packaged agents, each built for one job and ready to set up in your tools. If none fits, build your own.
Every inquiry gets a first look
The agent asks your intake questions and hands the lead to its owner with the answers attached.
Lead capture
Repeat questions, answered from your playbook
The agent drafts replies from answers you approved. Anything unusual goes to a person with the conversation attached.
Customer support
Records that stay current
The agent updates the CRM, calendar and weekly report, so nobody has to rebuild the picture from several tools.
Operations
Humans stay in control
The agent gets only the access the job needs and logs every step. Sends, payments and promises wait for a person to approve them.
Safety & oversight
- 1
Pick one workflow
We avoid broad assistant promises and start with the job that has the clearest business value.
One useful job
- 2
Connect trusted sources
The agent only uses the systems, documents, and fields approved in scope.
No mystery data
- 3
Add approval gates
Risky sends, deletes, account updates, and customer promises stop for human review.
Humans stay in control
- 4
Measure and tune
After launch, we watch output quality, handoffs, response time, and missed edge cases.
Improve with use
Agents pay off when there is a specific job to hand over. If that job is not there yet, we will say so.
A good fit
- A manual process eats hours every week: follow-up, the inbox, scheduling or data entry.
- There is a role you need covered but cannot justify a full salary for yet.
- You want a person to approve what matters before it goes out.
- You want the price quoted in writing before any work starts.
Not a fit yet
- You want AI because it is trendy, with no process in mind.
- You are not ready to change how the work gets done.
- An off-the-shelf tool already does the job. If so, we will tell you.
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent does the work: it drafts the reply, updates the CRM, books the visit, and waits for a person where you say it should.
Can agents work with our current CRM?
Usually yes. We scope around your current tools and only connect what the workflow actually needs.
Will customers know when AI is involved?
That depends on the workflow. We recommend clear disclosure for customer-facing agents and human approval for sensitive communication.
How long does the first agent take?
A written plan follows the first call in 3 to 5 days. Most builds take 4 to 8 weeks once the plan is approved. Workflows with many integrations or compliance needs take longer, and the exact dates go in the plan.
How much does an AI agent cost?
It depends on how many tools the agent connects to and how much it may do on its own. We scope one focused agent first, and the price is quoted in writing before any work starts. Once you approve the quote, the number holds unless the scope changes.
Which AI model runs the agents?
We pick the best model for each job from providers such as OpenAI, Anthropic and Google. The tools, permissions and logs stay the same whichever model runs underneath, so switching models never changes what an agent is allowed to do.
What is agent-to-agent (A2A) and why does it matter?
A2A describes the future case where both sides of a transaction have agents. That would require a customer request, business context, permissions, and approval rules to be exchanged safely. We are researching those protocol requirements, not presenting a finished A2A product.
Give the first agent one job you can measure. When it earns its keep, give it the next one.