AI engineering services for software that already has users.
The hard part of AI engineering is not the demo. It is the eighteen months after, when retrieval quality drifts, costs creep, and nobody owns the evaluation set. We assign AI engineers to that problem. They work in your codebase, on your roadmap, and a service delivery manager answers for what ships.
What are AI engineering services?
AI engineering services means building and maintaining model-backed features inside software that already has users. The work is mostly retrieval quality, evaluation, monitoring, latency and cost per request rather than model training. It is distinct from AI consulting, which advises on strategy, and from data science, which investigates questions.
What our AI engineering services cover.
Scope agreed before delivery starts. Most engagements begin with something already half-built.
That is the honest shape of AI engineering services in 2026. Very few partners arrive with a blank page. Most have a working demo, a proof of concept that impressed someone, and no clear path from there to something that survives real users and a provider deprecation notice.
The work that closes that gap is mostly unglamorous: an evaluation set so prompt changes can be measured, monitoring so quality drift is visible, and a fallback path so a slow provider degrades the product rather than breaking it.
- LLM features in existing products
Retrieval, structured output and tool use, built into software you already ship.
- Retrieval and context engineering
Chunking, embedding choice, reranking and the context assembly around the prompt.
- Evaluation sets
A test set for model behaviour, so a prompt change stops being a guess.
- Monitoring and cost control
Token spend, latency and quality tracked after launch, not assumed.
- Model and vendor migration
Moving between providers without rewriting the product around them.
Assigned to fit the stack you already run.
We match tooling to what you already run rather than arriving with a preferred stack. Models and providers: OpenAI, Anthropic, Google, open-weight models on your own infrastructure. Frameworks: LangChain, LlamaIndex, direct SDK work where a framework adds more than it removes. Vector and retrieval: pgvector, Pinecone, Weaviate, Elasticsearch. Evaluation and observability: Langfuse, Braintrust, custom harnesses.
Model choice is a constraint question before it is a preference. Data that cannot leave your infrastructure points to open-weight models on your own hardware. Latency budgets point one way, cost ceilings another. We scope those first and pick after.
Whatever the choice, we build so switching provider later does not mean rewriting the product around it. Provider lock-in is the most common avoidable mistake we see in inherited AI codebases.
Choosing between providers in this field is harder than in conventional software, which is why there is a separate checklist for selecting an AI development partner.
One fixed fee for a monthly average.
Same arithmetic as every Azendo agreement. Working hours across a year, minus leave and public holidays, divided by twelve. The fee does not move.
Managed AI services after the launch.
A model in production is not finished, it is running. Prompts drift when the underlying model updates. Retrieval quality degrades as the corpus grows. Costs move in the wrong direction quietly.
Our managed AI services cover that ongoing work: evaluation runs, regression checks on prompt changes, cost and latency monitoring, and retraining or migration when a provider deprecates what you built on. It is the least glamorous part of AI engineering and the part most teams have nobody assigned to.
The AI engineering roles we assign.
Take one role, or several as one delivery team. Each role below has its own page describing what it delivers under a service agreement.
Building in-house, a local agency, or Azendo.
Each fits a different situation. An in-house role makes sense when the work is permanent and local; a local agency suits a one-off project with a clear end date. Azendo sits between the two — ongoing capacity for work that keeps coming, with the team, the workplace and the administration behind it handled on our side.
| Comparison | Building it in-house | Local agency | Azendo |
|---|---|---|---|
| Time to productive output | Months — recruit, onboard, ramp up | Fast to start, slow to learn your product | 4–6 weeks |
| Continuity of context | Resets when someone leaves | Rebuilt with each new project | Held by the same delivery team, for years |
| Continuity of product knowledge | Lost when the hire leaves | Ends with the project | Held by the assigned team |
| Who answers for delivery | You do | Account manager, between projects | A service delivery manager, continuously |
| Cost profile | Fixed, whatever the workload | Priced per project | One monthly fee, adjustable each cycle |
| Scaling a discipline | A new hire each time | Re-scoped each engagement | Capacity up or down at the monthly cycle |
Questions about AI engineering services.
What is the difference between AI outsourcing and hiring an AI development partner?
In practice, whether anyone is accountable after launch. Most AI outsourcing engagements deliver a feature and close. As an AI development partner we stay assigned to the product, which matters because AI systems degrade in ways traditional software does not.
Do you build AI products from scratch?
Sometimes, but it is not where we are most useful. Our strongest work is adding AI to software that already has users, revenue and constraints.
Can an outsourced AI team work with our existing engineers?
That is the normal case. The assigned specialists work in your repositories alongside your team, in your sprint rhythm.
How do you handle data privacy?
Scoped before anything starts. Work can run entirely inside your infrastructure with open-weight models where the data cannot leave.
Are your specialists AI-trained?
Every specialist completes AI certification through our Talent Success programme before assignment, regardless of discipline.
Often assigned alongside an AI engineer.
A specialist rarely works alone on a roadmap. These disciplines cover the ground around the role and can be added to the same service agreement.
AI engineering services for the part that comes after the prototype works
The demo is rarely the hard part any more. What separates a feature that ships from one that stalls is evaluation, retrieval quality, cost per request and what happens on the inputs nobody anticipated. That work is continuous, which is why it is assigned rather than scoped.
The shape of the assignment follows the problem. Retrieval, evaluation harnesses and prompt-level behaviour sit with an LLM engineer; where the input is images or video rather than text, a computer vision engineer is the closer fit.
We work in the model providers you already have contracts with, inside your own accounts and your own spend. Model choice is a question we will give you a straight answer on, including when the answer is that you do not need one.
Almost every AI feature is limited by the data behind it rather than the model in front of it, so this capacity is often assigned alongside managed data services under one committed monthly capacity.
Tell us what ai engineering capacity your roadmap needs.
Tell us about your project and the capacity you have in mind. A service delivery manager will get back to you.
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