AI Copilot Development for one team and one job
Our AI copilot development builds an internal assistant for one team and one job: drafting a response, interrogating a document set, preparing a recurring piece of work.
2-3 weeksto ship AI speech-to-text and drafted replies for a support team
When to choose an internal copilot
It is the right service when the situation sounds like this.
- Support spends half the day writing the same answers.
- We have years of documents and nobody can find anything in them.
- A general chatbot is not going to know how we work.
What AI copilot development includes
- Built for one team and one job
- Drafts the response, searches the document set or prepares the recurring work
- Evaluation and human sign-off before anyone relies on it
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
Internal AI assistants built into one team’s daily work.
~1 month full infrastructure migrationThree engineers migrated the infrastructure, rebuilt checkout in Next.js and shipped AI speech-to-text for support agents.Licence EdTech Platform, Germany
~3–4 hours a day freed up for the principalAnalysed the office’s most time-consuming processes and built no-code AI agents for meetings, the knowledge base and financial reporting.No-code AI agents for a lean investment office, 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.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.
Talk to us about AI copilot development
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