AI Data Engineering Services: the pipeline the use case depends on
Our AI data engineering services connect the systems and build the pipeline an AI use case depends on, often read-only first. Most stalled AI projects are data projects in disguise.
85%reduction in inventory discrepancies for a food distributor
When to start with data engineering for AI
It is the right service when the situation sounds like this.
- The data we need is split across three systems that do not talk to each other.
- Our AI pilot stalled on the data, not on the model.
- IT will not let anything write into the ERP.
What our AI data engineering services include
- The systems the use case needs, connected
- The data pipeline the use case depends on
- Read-only connections first, so the systems you run stay untouched
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
Case studies
Data connected and put to work: a live cold-chain platform, one operating view across 12 companies, and a read-only data layer designed in a priced roadmap.
85% Reduction in inventory discrepanciesBuilt an IoT cold-chain monitoring platform with predictive restocking, with a six-person team formed in three weeks.Food Distributor, Germany- 64% manual workload removedReplaced a legacy ERP with one operating system across 12 companies, with all 34 critical processes documented and digitised.Single-family office (SFO), Germany
- €4.19M total modelled roadmap valueMapped 300+ process steps and designed a phased roadmap of three automation engines, including locally hosted AI, with a business case behind each.Construction project management consultancy (Tier 2 Mittelstand), Germany
What clients say
“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
More AI implementation services
AI implementation overviewAI 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.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.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.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.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.
Ask about AI data engineering 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.
Talk to an expert
Yurii Kotula
CEO and Engineering Leader
Your first call is with Yurii. Send the form, or pick a time in his calendar.
Book a call with Yurii