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AI implementation services
Most AI projects stall between a promising demo and something the team relies on. The gap is integration, evaluation, monitoring and ownership. We implement chosen use cases end to end and stay until they run without us in the room.
Trusted by teams at






You have picked a use case, or a pilot exists. Implementation means connecting it to the systems where the work happens, testing it against real examples, deciding what a person still approves, and putting monitoring around it.
We measure before we launch. A test set built from your real cases tells us if the system is good enough, and tells you what wrong looks like. Launching without that is guessing.
Adoption is part of the job. We put results inside the tool people already use so nobody has to open a new tab.
A pilot that works but nobody uses
We find why it stalled, usually integration or trust, and fix that instead of rebuilding.
A decided use case with no engineering capacity
Your team knows what it wants. We provide senior engineers to build it properly.
Regulated teams that need a human in the loop
Automate tracking and follow-up without automating the decision that needs a professional.
Build against real cases early. Everything else follows from how the system behaves on them.
Baseline and access
Week 1Collect real examples, confirm system access and define what good looks like.
DeliverableEvaluation set and success measure
Integration build
Week 2 to 4Connect the model to your systems and put results where people already work.
DeliverableIntegrated first version
Evaluate and tune
Week 4 to 5Run the evaluation set, study the failures and tune prompts, tools and guardrails.
DeliverableEvaluation report
Controlled rollout
Week 5 to 8Release to a small group, watch usage and errors, then widen.
DeliverableLive system with monitoring
Handoff or operate
Your team owns it with a runbook, or we operate it under a maintenance plan.
DeliverableRunbook and support agreement
Typical timeline
Four to eight weeks from a chosen use case to a monitored rollout, scoped after the audit call
Stack we build with
Claude · OpenAI · Python · TypeScript · PostgreSQL · Supabase · n8n
Patient or customer chatbots in production
Bilingual assistants running alongside operational tracking.
Meeting to text to filed record
Recordings transcribed, documents read and filed, updates routed to the right person.
Inbound support at scale
An agent handling most requests around the clock, escalating the rest.
A measured first version, not a slide about one.
Week 1
Evaluation set
Real examples from your work with the answers you consider correct.
Week 2 to 3
Integrated first version
Running inside the tool people already use, on sample or sandbox data.
Week 4
Evaluation report
Where it is right, where it fails and what we changed because of it.
Day 30
Rollout plan
Pilot group, monitoring and the approval steps agreed before release.
Achieved results only. Clients that have not agreed to be named are described instead.
A cardiology practice
About 120 patients on implantable heart monitors moved from several disconnected spreadsheets into one operational cockpit. Tracking and follow-up are automated without automating the medical decision, and a bilingual patient chatbot runs in production alongside it.
Qatar Tourism, WhatsApp customer support
A multilingual WhatsApp AI agent now handles 80% of requests without a human touching them. Response time had averaged over 4 hours. The client reports roughly $18,000 a year saved and support workload down 40%.
Lexi, notary platform
Meetings are recorded and turned into text automatically, documents are read and filed without re-typing, and client updates reach the right notary the moment they happen.
We do not sell fixed packages sight unseen. The number depends on how many systems the work touches, and the audit call tells you that number before you commit to anything.
Single workflow
Low four figures
One focused workflow live in one to two weeks, with error handling, retries and monitoring built in from day one.
Full system
Scoped after an audit call
AI steps, monitoring and integrations across several systems, typically four to eight weeks. No template price, because scope drives the cost.
Embedded engineer
From $60,000/year
A dedicated engineer building and maintaining the work inside your team, instead of a scoped project.
A 30 minute call. We look at what exists, what is blocking it and what it would take to run in production.
In this call, we'll walk through your project scope, timeline, and goals - so we can both check if we're a fit. No obligation, no slide deck, just a working session.
Don't want a call? Email walid@ayautomate.com
“The team is super fast - sometimes we had to slow them down. We managed to scale the company without investing into hiring.”

Elie Salame
COO, Adstronaut.io
We've created products featured in
Walid Boulanouar
View LinkedInThis call is for teams ready to move. If that's you, pick a time.
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FAQ
Consulting decides what to build. Implementation builds a decided use case and puts it into production. Many clients do both with us, but you can start at either.
We build an evaluation set from your real cases before launch and score the system against it. You see the failures, not just the average.
Yes, and often you should. We automate tracking, drafting and follow-up and leave the decision that needs professional judgment with your people.
Focused builds start in the low four figures. Larger systems across several tools are scoped after an audit call, typically four to eight weeks of work.
We review it, keep what works and rebuild only what does not. Most stalled pilots need integration and monitoring, not a restart.