AI Implementation Services: AI built into the work your team already does

Our AI implementation services cover automation, copilots, knowledge systems and AI features. We find where AI removes expensive work or adds product value, check the data and integrations, then build it.

Discuss an AI opportunity Forward Deployed AI Engineers

AI implementation services: a stone and glass pyramid, the Intelvision method

2-3 weeksto ship AI speech-to-text and drafted replies for a support team

What we can show you

AI attracts more claims than evidence. Our documented AI work is strongest in two places: pricing where AI value actually sits, and senior engineers inside AI product teams.

A delivered business case that mapped more than 300 process steps and priced a phased AI and automation roadmap. Its figures are modeled, and labelled that way.
Senior engineers supplied to AI products our clients own. That is talent delivery, and we do not present it as AI systems we designed.
If you need a partner with a long list of completed AI deployments, ask early. We will tell you plainly whether we are that partner.

€4.19M: Total modelled roadmap value
~947%: Modelled ROI on ~€500k investment
5-6 weeks: Modelled payback after deployment

Read the business case

Our AI implementation services

Most AI implementation engagements start with the opportunity assessment, because most AI budgets are lost on use cases that were never worth building.

AI opportunity and business case
We map the work as it is actually done, find where cost, delay or error concentrate, and price what changing it is worth.
- Process mapped from the real operating environment
- Data and integration constraints checked before scoping
- Phased investment case, with modeled value stated as modeled
Forward Deployed AI Engineers
Senior AI engineers join your team, find high-value use cases in the real operating environment, and ship working systems alongside the people who will use them.
How forward deployment works
Private AI
AI that runs on your own servers or a private cloud you control, so documents, contracts and customer data never leave your environment.
- Models hosted on your infrastructure, not a public AI service
- Answers from your own documents, with permissions respected
- A person signs off wherever the output carries legal or financial weight

Workflow automation and agents
One operational workflow automated end to end. Where money is counted or compliance is recorded, the logic stays explicit and a person signs off.
Internal copilots
An assistant for one team and one job: drafting a response, interrogating a document set, preparing a recurring piece of work.
RAG and knowledge systems
Answers from your own documents, contracts, tickets or records, with citations back to the source and permissions respected at retrieval.
AI product features
AI inside a product you already sell: the feature, the evaluation harness, the cost and latency budget, and a fallback for when the model is wrong.
Data and integrations
Most stalled AI projects are data projects in disguise. We connect the systems and build the pipeline the use case depends on, often read-only first.
AI-assisted SDLC
AI inside your own engineering process: where it measurably helps, where it quietly costs review time, and what tests and standards it needs first.

Our method

An AI use case is a business question before it is a technical one. We examine strategy, operations and technology together, because a use case that is technically sound and operationally impossible is still a failed project.

What measurable business outcome must change?

Strategy: What is worth building?; Business goals, customer needs, product capabilities, investment logic, and roadmap priorities.; You get:Business case, product direction, prioritized roadmap
Operations: How will the work move?; Ownership, prioritization, estimation, flow, release planning, quality, and leadership visibility.; You get:Operating model, delivery system, accountable execution
Technology: What can reliably support it?; Architecture, data, integrations, technical debt, security, deployment, and scalability.; You get:Technical direction, stable architecture, production delivery

How it works

1
Describe the expensive work
The workflow that costs too much, the decision that takes too long, or the product gap you think AI closes.
2
We test whether AI is the answer
Sometimes it is not, and an integration or a process change is. You get that answer before you get a proposal.
3
Validate the data and the constraints
Availability, quality, permissions and integration surface, checked before anything is scoped or priced.
4
Build the first use case properly
One system in production, with the evaluation and human sign-off that make it safe to rely on.

Engagement models

Discovery first in nearly every case. What follows depends on whether you are buying a defined system, engineering capacity inside your team, or ongoing ownership.

Discovery, then Statement of Work: Best for: A defined system with a scope worth fixing; How it works: Paid opportunity and feasibility work, then a Statement of Work for the build
Embedded engineering: Best for: Use cases that have to be found inside the operating environment; How it works: Forward Deployed AI Engineers on a monthly rate, working inside your team
Retained delivery: Best for: A system already live that has to keep improving; How it works: Monthly retained capacity with an agreed backlog and reporting

AI implementation case studies

Senior engineers and dedicated teams working inside AI products our clients own.

All case studies

What clients say

5.014 verified reviews on Clutch

“Intelvision’s project management is consistently strong.”
Christopher WeindlCEO, ING+
“They effectively prioritized our business needs, adhering to the budget, which we found valuable.”
Pavlo PotichnyiCEO, Swiss Scholar
“Intelvision provided solid tech expertise and the ability to come up with an effective software architecture.”
Kyle McLoughlinClient, Dublin
“Intelvision provided us with a full team that joined the project quickly and without delays.”
Andrew DenysovClient, Pennsylvania

Need the business case to hold up to scrutiny?

When an AI or automation investment has to survive a board, a finance review or a due diligence process, the Strike Team builds the case across strategy, operations and technology, and labels modeled value as modeled.

Business Case Development

Talk to us about AI implementation services

The slow, costly or error-prone part of the operation. We will tell you whether AI changes it, what it would take, and what we can and cannot evidence.