Deployment
We embed a deployment strategist, forward-deployed engineers and an applied researcher in your team, on site or virtually. Together we pick one workflow, measure it, build what it needs and stay accountable until it hits the target you signed off.
A first deployment usually has three to six Addo AI people working alongside one named owner from your business.
Embedded means your tools, your stand-ups, your hours and your security rules, wherever the engineer sits. Four offices cover every time zone, so a virtual team is awake when yours is.
Engineers at your desks for discovery, the build and the go-live, from the nearest of our offices in San Francisco, Singapore, Dubai and Lahore.
In your chat channels, your stand-ups and your code repositories, under identities you issue and access you control. Nothing leaves your estate, and the team can start within days.
A lead on site, the squad embedded virtually, scaled up and down as the work demands. The team travels for the moments that need people in the same room: discovery, go-live and handover.
Wherever they sit, engineers work under accounts your IT team creates, through access you control, and nothing is copied to Addo AI machines. How embedded engineers reach your data
Every phase ends with a decision your team makes, on numbers both sides can see. Your workflow owner signs off every exit.
Phase 1, about two weeks
Shadow your team, score candidate workflows on value and feasibility, pick one, and measure how it performs today.
Exit: a signed baseline and target.
Phase 2, build phase
Connect your systems, build what the workflow needs (agents, models or the data platform under them) and test it on real past cases.
Exit: it passes the test set in shadow mode.
Phase 3, staged
It takes real volume in steps, each unlocked only when accuracy is proven on your cases.
Exit: the target met four weeks running.
Phase 4, every quarter
Reuse the data, integrations and controls for the next workflow, and train your team to run what is live.
Exit: your team runs it day to day.
Our commitment: if what we build misses the target you signed off, we keep working on that workflow until it hits it.
We staff from the hub closest to you and travel to wherever your operations run. Working languages include English, Arabic, Urdu, Bahasa and Malay.
North America
Middle East and Africa
Asia Pacific
South Asia, and our engineering hub
Three people, read access to the systems involved and a few hours a week. We do the rest.
After launch the same numbers appear in our reports and in your own, so nobody has to take our word for it.
At least two weeks of normal operations, measured and signed off by your workflow owner before we build.
One main metric plus up to two safeguards, such as accuracy or complaint rate, agreed before go-live.
A weekly dashboard review with your team and a monthly steering meeting with leadership.
Every automated decision links back to its inputs, the rules applied and who approved it.
Start with one workflow, hand us the running of it, or have us build a programme and transfer it to your team.
Best for a first project
One workflow, taken from selection to live operation.
Best for operations leaders
We operate, monitor and improve your agents for you.
Best for governments and large enterprises
We build your AI programme, run it with your team, then hand it over.
Choose the setup that matches your infrastructure and compliance rules. The deployment method is the same in all four.
The fastest start. We host, update and monitor the agents and everything they run on, with your data kept separate from every other client.
Single-tenant infrastructure reserved for you, in the region you choose, run by our operations team.
Everything runs inside your own AWS, Azure or Google Cloud account. Data stays in your perimeter and you control upgrades.
The full stack on your hardware with open-weight models, maintained by our engineers.
A short version of the scorecard our strategists use in week one. Describe one workflow with five sliders.
How many cases each month?
Could you write down most of the decisions as rules?
Can the systems and documents involved be reached?
Is cost, speed or quality tracked today?
What happens if one case is handled wrongly?
A deployment strategist shadows your team, scores your candidate workflows and leaves you with a ranked shortlist and a measured baseline.