Ready-made Solutions
Overview
Who it is for. Leaders who know which problem they want solved and do not want to fund a research project to solve it: COOs, CTOs, heads of operations and engineering, energy and infrastructure managers.
The problem. Many first AI systems start from a blank page, although the hard parts repeat from one organisation to the next: permission-aware retrieval, evaluation sets, review screens, approval steps, audit logs. Teams spend the budget rediscovering them, and the pilot ends before anything reaches production.
What we do. We maintain six accelerators, each a reusable core with a defined architecture:
Boardroom Simulator - a board-ready portfolio of AI bets, risks, dependencies and first moves.
Sovereign Knowledge Core - private retrieval with citations, permissions and evaluation, in EU cloud, private cloud or on-prem.
Agentic Operations Desk - multi-step agent workflows across tools, with approvals, escalation and monitoring.
Document Intelligence Factory - classification, extraction, comparison, review screens and an audit trail.
Manual Test Agent - plain-language manual test cases turned into agent-driven browser runs, then into deterministic Playwright scripts.
Energy & Compute Orchestrator - schedules flexible compute and storage against on-site generation and tariffs, with a safety layer that holds without it.
We deploy the accelerator in your environment, connect it to your data and systems, and adapt it to your controls.
What you get. A working system in weeks, documentation, an evaluation set built from your own cases and a clear view of what scaling would take.
How we work
At Keter AI, every engagement moves through the same four layers, in the order of our mark: strategy, architecture, delivery, governance. Each layer ends in something you can inspect and keep: a decision, a design, a working system, a set of controls. The depth changes with the service; the order does not.
Strategy
01
We start from the business problem: which decisions and workflows AI should improve, what that is worth and what must be true first.
Architecture
02
We design the system around your constraints: data boundaries, model strategy, integrations, evaluation and where it will run.
Delivery
03
We build with real data and real users, in short increments, until the system passes its evaluation set and is ready for production.
Governance
04
We leave controls that last: named owners, monitoring, an audit trail, documentation and a clear view of AI Act duties.
Deliverables
An accelerator is a starting point, not a boxed product. Every deployment ends with the same set of outputs, so the result can be judged on evidence and the next decision is clear: scale it, extend it or stop. A typical first deployment fits a Production Sprint of 6-10 weeks.
Technologies we work with
Docker
Containers
Portable, reproducible packaging, so the same AI workload can move between EU cloud, private cloud and on-prem.
Microsoft Azure
Enterprise cloud
Azure AI services for enterprise estates, and Azure Local disconnected operations where systems have to run offline.
GitHub
Code and CI
Repositories, pull-request review and CI pipelines: the place where agent-written changes are tested and approved.
Hugging Face
Open-weight model hub
Hub for open-weight models and datasets. We evaluate, adapt and serve selected models inside private environments.