What are AI implementation services?

They take an AI idea into production inside your own systems. That covers scoping, integration with your data and tools, deployment, and handover to your team. Strategy advice and model research are separate work. Our post on the gap between an AI proof of concept and production AI explains why this step is where most projects stall.

How is AI implementation different from AI consulting?

Consulting decides what to build and whether to build it. Implementation builds it and puts it live. We do both, and many clients start with a short consulting phase. Our AI consulting page covers that work, and the planning guide shows how we scope a roadmap.

How long does AI implementation take?

Our usual delivery framework runs 4 to 5 months from scoping to production. Eona.ae went live in 3 months and the lending platform rebuild took 4. Larger scopes with many systems take longer. Our post on the enterprise AI development timeline shows what drives the range.

How much do AI implementation services cost?

Projects start from $20K, scope-driven. Five things set the price: how long the build runs, its features and integrations, the size of the team, your timeline and priority, and the level of polish. Calls and scoping are free until you get a proposal. See the pricing page for how we reach a fixed number.

Do we need a finished pilot before we start?

No. Some clients arrive with a working pilot and some with only an idea. We scope both the same way, around one use case and one success number. If a pilot exists, we check what can be reused and what must be rebuilt for production.

Who owns the code and who runs the system afterward?

You own the code, the documentation, and all credentials from handover. We train your team in live sessions and give you runbooks. We include 30 days of post-launch support, and you can hire us for more if you want it.

AI Implementation

AI implementation services from idea to production in your systems

You have an AI idea, a pilot, or a budget. We take it live inside your own systems. That means scoping the work, connecting your data and tools, deploying to production, and handing over code and runbooks your team can run. We shipped a WhatsApp chatbot at Eona.ae in 3 months, and a rebuilt lending platform in 4. You own the code.

Book Strategy Call See Case Studies

Engagement · Scope

Fixed price. Shipped in 4 to 5 months.

What you get

Fixed scope and price
Integration with your systems
Weekly demos on real data
Team training and handover

Timeline

4 to 5 months

Typical scope

1 to 5 integrations

Payback

Modeled before you sign

Scoped before we buildIncluded

Live in your own systemsIncluded

Handover and code ownershipIncluded

Why it matters

Why AI Ideas Stall Before Production

Most AI work does not fail in the model. It stalls in the gap between a pilot and your real systems:

A Pilot That Never Ships

A demo on sample data proves little. Real data, real users, and real security reviews are where the work starts.

No Path Into Your Systems

The AI needs your CRM, ERP, or databases. If nobody owns that connection, the pilot stays on the side.

A Scope That Keeps Growing

Without a clear first use case and a fixed end point, the project drifts and the budget with it.

Nobody Left to Run It

When the vendor leaves, your team inherits code and no runbook. The system slowly decays.

What we build

What AI Implementation CoversFour stages, from the first scoping call to the day your team takes over:

1 · Scoping

One Use Case, One Success Number

Audit of your data, systems, and access
A written success metric the build is judged against
A build, buy, or partner call before any code
A fixed scope and price

2 · Integration

Connected to the Systems You Already Run

Links to your CRM, ERP, databases, and internal tools
Delivered: a WhatsApp chatbot at Eona.ae wired into live order and delivery data
Permissions and data access reviewed with your IT team
No rip-and-replace of working software

3 · Deployment

Production From the First Release

Production code, not throwaway prototypes
Weekly demos on real data
Automated tests and a staged rollout
Evaluation sets and monitoring from the first week

4 · Handover

Your Team Can Run It

Documentation and architecture diagrams
Live training sessions with recordings
Credentials, admin access, and full IP transfer
30 days of post-launch support

What we measure

What we measureset against your baseline in discovery

Every engagement is scoped to a fixed price and an outcome you can measure, not billable hours.

Support

About 90%

of queries automated at Eona.ae

Result

Delivery

3 and 4 months

Eona.ae chatbot and lending platform

Result

Framework

4 to 5 months

from scoping to production

ResultSee case studies

The comparison

THE EDGEFIRM DIFFERENCE

Unlike big-firm implementation teams:

Engineers who write the code are on your calls
Fixed price for a defined scope
Delivery in months, not a multi-year program

Unlike pilot-only vendors:

Built for production from week one
Integration with your real systems
Handover your team can run

Unlike a DIY build with an API key:

Scoping and architecture done before coding
Testing and monitoring included
Edge cases found before your users find them

Common questions

Common Questions About AI Implementation Services

: They take an AI idea into production inside your own systems. That covers scoping, integration with your data and tools, deployment, and handover to your team. Strategy advice and model research are separate work. Our post on the gap between an AI proof of concept and production AI explains why this step is where most projects stall.
: Consulting decides what to build and whether to build it. Implementation builds it and puts it live. We do both, and many clients start with a short consulting phase. Our AI consulting page covers that work, and the planning guide shows how we scope a roadmap.
: Our usual delivery framework runs 4 to 5 months from scoping to production. Eona.ae went live in 3 months and the lending platform rebuild took 4. Larger scopes with many systems take longer. Our post on the enterprise AI development timeline shows what drives the range.
: Projects start from $20K, scope-driven. Five things set the price: how long the build runs, its features and integrations, the size of the team, your timeline and priority, and the level of polish. Calls and scoping are free until you get a proposal. See the pricing page for how we reach a fixed number.
: No. Some clients arrive with a working pilot and some with only an idea. We scope both the same way, around one use case and one success number. If a pilot exists, we check what can be reused and what must be rebuilt for production.
: You own the code, the documentation, and all credentials from handover. We train your team in live sessions and give you runbooks. We include 30 days of post-launch support, and you can hire us for more if you want it.