Embedded delivery
Embedded with your engineers on site or remote. Typical duration from roughly three months to two years: shared backlog, agreed milestones, and artefacts in your systems. Formal handover when the assignment ends.
Loading...
Boutique AI and ML delivery
Small roster. Named owners. Shipped outcomes.
Adamass AB, Malmö, Sweden (est. 2019). Boutique practice: generative AI, machine learning engineering, legacy modernisation, and technical due diligence for investors. Mostly team augmentation with written scope and milestones — engagements have run from a few months to about two years.
Most failures happen at handoff: between strategy and engineering, prototype and production, or documentation and operations. We close that gap with explicit scope, written artefacts, and a single accountable lead. We do not introduce process for its own sake.
01
We usually augment your team in your repos, tools, and ceremonies — not as a separate vendor silo. One named technical lead per engagement.
02
Engagements close with transfer of source, configuration, deployment notes, and operational documentation as agreed in scope.
Context
Clients include AI product companies, teams modernising legacy platforms, and investors requiring technical review of ML and software assets.
3–24months
Typical engagement span
Embedded
Augmented with client engineering teams
100%
Senior delivery
Capability types
Three standard models
Technical due diligence
Structured report for investors or boards: architecture, team, data and model practices, risk register. Typical turnaround two to three weeks.
Strategic advisory
Fixed cadence calls for leadership teams without a full-time ML lead. Agenda-driven; minutes and action items issued after each session.