AI-assisted Engineering
Overview
Who it is for. CTOs, heads of engineering and QA leads whose developers already use AI tools, and who need that use to show up in delivery, not only in individual speed.
The problem. AI use among developers is the norm, and trust has not kept up: in the 2025 survey by Google's DORA research programme, 90% of software professionals had adopted AI, while 30% trusted its output a little or not at all. Returns depend on foundations. Research cited in DORA's 2026 report finds productivity gains of 35-40% on simple greenfield tasks and 10% or less on complex legacy code. Without fast tests, clean environments and a review policy, agents amplify instability and move the bottleneck to code review.
What we do. We roll out coding agents as an engineering change: repository-level instructions and reusable skills, sandboxed execution with scoped credentials, review rules for agent-authored changes, and measurement with delivery and stability metrics. In testing, our Manual Test Agent turns plain-language test cases into agent-driven browser runs and then into deterministic Playwright scripts that a test engineer reviews before they run in CI. For legacy systems we first capture existing behaviour in tests, then migrate feature by feature with automated equivalence checks, leaving the complex features to engineers.
What you get. A governed agent workflow that your teams own, a test suite that grows without adding manual effort, and numbers that show what changed.
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
We work this way ourselves: in our own delivery, coding agents write, while separate verifier agents and people check. The outputs below bring the same discipline to your teams. They can be delivered as one programme or one at a time, starting with a baseline, so that every later claim about productivity can be tested.
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.