AI Readiness Assessment
Whether your environment can support AI, and what it would take.
Enterprise AI implementation that reaches production
We build and deploy production AI systems, end to end or alongside your existing AI, data and engineering teams. The work runs inside your environment, and the code, prompts and system remain yours.
Built and run by us
What we do
From the first decision to a system that keeps working
AI Strategy and Roadmap
The order things get built in, and the list of what you are not building.
AI Integration Services
Retrieval that reaches the right documents, and nothing the asker could not open.
AI Implementation
One workflow live with real users, handed over to your team, with code you own.
AI Governance
What AI is running, who owns it, and what it can reach.
AI Operations and Support
Evaluation, permissions, model migrations and cost, after go live.
How we engage
Hand us the whole project, or one part of it
End-to-end implementation
We take the project from assessment to a running system with real users, then hand it over to your team.
We own the whole project
Defined project delivery
Your team runs the program. We deliver one scoped workstream inside it, such as an integration or an evaluation build.
We own one workstream
Embedded engineering support
Our senior engineers work inside your team, on your backlog and to your standards, until a named system is in production.
We own a named release
In every model there is a named outcome we answer for, and the work lands in your environment as your asset.
Why Alppoint
We build the system and leave it running
Alppoint AI works with firms in financial services, professional services and legal. Most can see what AI could do. The hard part is getting it into production.
We map the problem, build the system on your infrastructure and put it in front of real users. It keeps running in your environment and remains your asset after handover.
Systems that go live
The deliverable is a running system with real users, not a slide deck or a prototype that never ships.
Accountable for the outcome, not the hours
We commit to a system in production, whether we own the whole project or one part of it inside your team.
Your environment, your asset
Code, prompts, configuration and evaluation sets sit in your own accounts and remain yours on exit.
Senior people, no account layer
You work directly with the people building the system, with only a few engagements running at once.
Selected work
Systems running in client environments
Three examples of work that reached real users and remained with the organization that commissioned it.
Blog
AI insights
Read all posts
Decide, September 19, 2026
Embedded AI engineers or a scoped project: what each commits your team to
A scoped project and embedded engineering support are not the same purchase at different sizes. Here is what...
Decide, September 19, 2026
How to choose the first AI workflow to automate at a professional services firm
Every firm has three or four AI candidates on the list at once. The decision that matters is...
Run, September 17, 2026
Deploying AI into proposal and RFP responses
An RFP that goes out well takes the same eleven days whether the deadline is generous or brutal....
Put one valuable AI workflow into production
Tell us which workflow matters, who runs it today, and what good would look like. We will tell you what it would take to put it in front of real users.