AI does the work. Your people approve it.
We build one job end to end inside your own tools and accounts, then keep it running and improving.
What custom AI solutions are
Custom AI solutions, also called AI implementation, is how Stennir puts AI to work in your operations. We build one job end to end inside your own tools and accounts. AI does the work, a person approves the output, every action is logged, and we keep the job running and improving.
One job, built end to end
A working job in the tools your team already uses, not a demo.
The job, built
One job taken from the first request to the finished output, working on your real cases.
Inside your tools
It runs in the accounts and tools you already own, so your people do not learn a new system.
A written agent spec
What it does, what it can read, what it cannot touch and who signs off, in one page.
A person approves
AI does the volume. Your people check the output before it goes anywhere.
A named owner
One person on our side who answers for the job from launch onwards.
Kept improving
We watch it, test it and improve it as your work changes.
You pay for finished work
You pay for outputs, not for effort. AI does the volume and a person checks it.
A sorted inbox
Every message read, labelled and routed, with replies drafted for a person to approve.
A drafted weekly report
Numbers pulled from your systems and written up, ready for a person to review.
Updated records
Fields filled and records kept current, with every change logged and easy to reverse.
Checked documents
Forms and invoices read and compared, with exceptions flagged for a person.
We agree the details on the 30-minute call.
Every automation ships with a spec
One page, written before anything runs. You read it and you sign it off.
- Does
- Sorts incoming messages and drafts replies
- Can read
- The shared inbox and the help centre
- Cannot touch
- Payments, contracts and personal files
- A person signs off
- Every reply, before it is sent
- Pauses if unsure
- Hands the message to a named person
You stay in control
Your accounts
It runs inside the accounts you already own.
Your sign-off
Nothing goes out until your people approve it.
Your off switch
You can pause or stop it whenever you decide to.
Your data
Your data stays yours, hosted where you need it.
Works with the tools your team already uses
Answers come from your own data, knowledge and context. Where a tool supports MCP, we use it.
- Slack
- Google Drive
- Microsoft Teams
- Gmail
- Notion
- HR tools
- Communication tools
Managed operations
Monitoring
We watch it run and catch problems before your team does.
Evaluations
We test it against real cases, so quality is measured and not assumed.
Improvements
We fix what the checks show and adapt it as your work changes.
A named owner
One person on our side who answers for it.
Forward deployed engineers
A forward deployed engineer is a senior engineer from Stennir who works inside your team, in your tools and on your data, and ships custom AI into your live systems. Choose it when the blocker is integration, not ideas. Your people approve the output, and the code stays in your repository.
When to choose an embedded engineer
Instead of project delivery
A fixed project suits one clearly scoped job. An embedded engineer suits work that spans several systems and shifts as you learn.
After AI consulting, not instead of it
Consulting finds and ranks the jobs. The engineer builds them. If you are still deciding whether AI pays, start with consulting.
Instead of hiring in-house
A hire is a long-term bet on steady AI work. The engineer gets the first jobs live and leaves a runbook. Many teams hire once they know the shape of the work.
Instead of staff augmentation
Augmentation adds hands for tasks you assign. The engineer answers for a working result and the handover.
What they do inside your team
Work in your tools
They build in your accounts and your repository, behind a written spec that you sign off.
Connect your systems
CRM, ERP, ticketing, drives and chat, wired to the AI so answers come from your own data.
Handle the exceptions
They find the cases nobody documented and add the checks and approvals that catch them.
Report against the baseline
Hours freed and money saved are measured against where you started and reported separately.
What you get at each step
A scoped job
A written scope and spec: what the AI does, what it can read, what it cannot touch, who signs off.
A working build
The job running in your tools, approved by your people on real cases.
A job that keeps improving
Monitoring, evaluations and improvements, with a named owner.
A team that runs it
Code in your repository, a runbook and trained people.
Pair the engineer with AI training. The people who will run the job learn on what is being built, so the skill stays with your team.
What is a forward deployed engineer?Safe by design
Safety plan
What the job may and may not do, and who is told when it pauses.
Audit log
A record of what it did and who approved it.
Rollback plan
A way back to how things worked before, rehearsed with your team.
Your code, your repository
Anything custom we write lives in your repository, with a runbook.
Teams we work best with
- Teams that have chosen the first job and need it working, not a demo.
- Companies where a person must stay in the loop: finance, support, operations, compliance.
- IT leads who need work to run in the company's own accounts, with a clear way to switch it off.
- Companies with an AI consulting summary in hand, or a clear job in mind.
From spec to a job that runs
- 01
Write the spec
We write down what the job does, what it can read and what it cannot touch.
- 02
Build in your tools
We build inside your own accounts, connected to the tools your team uses.
- 03
Check with your people
Your people test it on real cases and approve it before it goes live.
- 04
Run and improve
We monitor it, evaluate it and improve it, with a named owner.
Questions we hear a lot
What are custom AI solutions?
One job built end to end inside your own tools and accounts. AI does the work, your people approve it, and we keep it running and improving. It is also called AI implementation.
Which job should we start with?
The one with the clearest cost today and the least risk if it gets something wrong. Typical first jobs are customer messages, document checks, reports and record updates. AI consulting ranks them if you are not sure.
What does "you pay for finished work" mean?
You pay for outputs such as a sorted inbox, a drafted weekly report or updated records. AI does the volume and a person checks it. We agree the details on a 30-minute call.
Does it work in our language?
We speak your language and adapt the solution to you. Arabic and English are fully supported, and other languages on request.
Where is it hosted?
Where you need it: in the country, cloud or system that fits your rules. We agree it in writing before the build starts.
Do we own the code?
Yes. Anything custom we write lives in your repository, with a runbook.
What happens after launch?
We keep it running: monitoring, evaluations and improvements, with a named owner on our side.
Is this a platform we have to adopt?
No. We build a job into the tools you already use. There is no new platform to adopt.
Can an engineer work inside our team?
Yes. A forward deployed engineer from Stennir works inside your team, in your tools and on your data, and builds the job with your people. It is the same custom AI solutions service, run as an embedded engagement.
Pick the job. We will build it.
30 minutes, no preparation. Tell us the job and we will tell you how we would build it, with a person approving every output.