Embedded AI Engineering
Put an AI engineer inside the workflow that needs improving.
Some AI work is best understood beside the people doing it. An embedded engagement places an engineer within your team’s delivery rhythm to own a defined AI outcome, work through real system constraints and leave the implementation maintainable.
DIRECT ANSWER
An embedded AI engineer works within your organisation’s delivery process and owns an agreed implementation outcome from workflow understanding through deployment. Responsibilities, decision rights and handover are defined before the engineer joins.
Best for
- Teams that need hands-on ownership of an AI integration across existing systems
- Organizations where close collaboration with users is essential to implementation
Consider another approach
- An executive role owning the company’s entire technology function
- Additional general development capacity directed entirely by your backlog owner
Name the problem and the authority to solve it
We agree the workflow, the person accountable for its business use and the decisions the engineer can make. The role can cover architecture, implementation, evaluation and operational handover. Company-wide technical leadership stays with a Fractional CTO. General capacity under your existing delivery lead belongs with Staff Augmentation.
Work where the constraints are visible
The engineer joins the relevant planning and review routines, observes the users’ tasks and works in your repositories and agreed environments. Access is limited to the implementation need. Dependencies on data owners, security reviewers and platform teams are named early so an unanswered access request does not quietly become an engineering delay.
Connect model behavior to the whole workflow
The work may include model integration, retrieval, tool interfaces, evaluation and application changes. We use your existing delivery standards where suitable and make any proposed changes explicit. Working demonstrations include failed or ambiguous inputs. The objective is a useful capability in your systems, with humans still responsible for decisions assigned to them.
Illustrative engagement: an assistant inside a support workflow
An engineer studies how support staff gather account information and draft replies. The agreed release retrieves permitted records and prepares a draft, with review before sending. A sample acceptance session follows the task from request to staff approval, including missing permissions. The team receives the code, task tests and instructions for diagnosing a failed run.
Avoid an implementation only one person understands
We document the interfaces, operating decisions and release checks as the work progresses. Pairing and handover sessions prepare internal maintainers before the engagement ends. A continuing backlog, monitoring responsibility or replacement arrangement is agreed explicitly. A tightly bounded first implementation can instead use an AI Delivery Sprint.
Questions before you start
It is a contracted engagement with agreed capacity, duration and responsibilities. Availability and the working pattern are confirmed during scoping; the page does not imply an immediate placement.
We agree remote, on-site or mixed collaboration according to the workflow and practical requirements. Travel, location, access and working hours are settled in the proposal.
A named business owner sets the outcome and priorities with the engineer. The engagement defines technical decision rights and how changes to scope are approved.
You own the work we create for you. The agreement provides full IP transfer with no Wavect retained rights, and code lives in your repository and accounts from the first commit. Documentation and a named maintainer support the handover.
Discuss an embedded AI role
Describe the team, the workflow to improve and the decisions the engineer should own. We confirm the role, access, availability and working arrangement before an engagement begins.