AI Implementation & Engineering
AI systems built, measured and improved by engineers.
DeClouder designs, builds and optimizes AI systems for customers — alongside our cloud, DevOps and platform engineering work. For teams exploring a first practical application, and for teams already running AI products who need specialist depth or extra delivery capacity.
Two different things
AI we build for you, and AI we use to do our work.
Elsewhere on this site we describe AI-assisted engineering: our own engineers use AI coding and operations tooling to deliver cloud and platform work. That is a delivery method, and it applies to every engagement.
This page is about something separate — AI systems we design, build, evaluate and optimize for you, as the deliverable.
Both are engineering work, held to the same standards: measured rather than assumed, integrated into systems that already exist, and handed over in a state your team can maintain.
Who this tends to suit
Exploring a first application
You can see that AI should be useful somewhere in the business and want a clear-eyed read on where, what it would take, and whether it is worth doing.
Already building with AI
You have systems in production and an engineering team, and want specialist depth on evaluation, retrieval, cost and latency — or simply more hands on a defined piece of work.
Somewhere in between
A prototype exists, it is promising, and the gap between it and something you would put in front of customers needs engineering rather than enthusiasm.
What we do
Capabilities, selected to fit the engagement.
Not a fixed architecture or a bundle. Most engagements use a few of these; which ones depends on what you are building and what already exists.
Assessment and architecture
Work out where AI is worth applying and how it should be built: candidate use cases, data and access requirements, technical architecture, and the constraints that will decide whether it holds up in production.
Prototypes and production implementation
A focused prototype to test whether an approach works on your data, then the engineering to take a proven one into production — integrated, observable and maintainable by your team.
Assistants and knowledge retrieval
Assistants that answer from approved business information rather than from general knowledge, with retrieval scoped to what a given user is allowed to see and answers that cite where they came from.
Workflow automation and integrations
Connecting AI into the applications, APIs and tools you already run, including MCP where it is the right way to expose a system to a model, so the work lands in the systems people already use.
Evaluation of output quality
Measuring whether a system is actually good: accuracy against agreed criteria, source attribution, behaviour on edge cases, and regression checks so a change to a prompt or a model does not quietly make things worse.
Cost, latency and observability
Improvements to model selection, context handling and caching, with the tracing and metrics needed to see what a system is doing, what it costs per request, and where the time goes.
Engineering support alongside your team
Scoped capacity for teams that already have AI products and engineers, and need a specialist alongside them or additional delivery for a defined piece of work.
Illustrative examples
The shape of the work.
These are examples of what is possible, written to make the service concrete. They are not descriptions of completed customer projects.
A support assistant over product documentation
Retrieves the relevant documentation for an incoming question and drafts a response with links to its sources, for a staff member to review and send.
Notes and transcripts into structured output
Turns meeting notes or call transcripts into structured summaries and the specific items someone needs to act on, in the format the downstream system expects.
Natural-language access to business data
Lets people ask questions of business data in plain language, with the same permissions they already have, so answers never include data the person could not otherwise see.
Evaluation and optimization of an existing workflow
Takes an AI workflow already in use, measures it against agreed quality and performance targets, and improves what the measurements show is worth improving.
How we approach it
Measured, permissioned, reviewable.
Data access is scoped
A system reads the sources we agree it should read, through your own access controls, and respects the permissions the user already has.
Humans stay in the loop where it matters
Where output goes to a customer or drives a consequential action, the default is that a person reviews it. Where that is not needed, we say so rather than adding ceremony.
Quality is measured, not asserted
Accuracy and attribution are checked against criteria agreed with you, and re-checked when prompts, models or data change.
Built to be operated
Tracing, cost and latency visibility, and documentation, so the system can be run and changed after we hand it over.
Engagement
Three practical ways to start.
Which one fits depends on how well defined the problem already is. We will tell you which we think it is.
Start here
Focused assessment
A short engagement on an agreed scope that ends in findings and prioritized recommendations you can act on — including whether a given use case is worth building at all.
- Agreed scope and access defined up front
- Findings and prioritized recommendations
- Useful whether or not you continue with us
Prove it
Prototype or pilot
A focused build against success criteria agreed before we start, so there is a clear answer at the end about whether the approach works on your data and is worth taking further.
- Success criteria agreed in advance
- Built on your data and constraints
- A decision at the end, not an open-ended project
Build and run
Implementation or ongoing support
A defined implementation project, or ongoing engineering support alongside your existing product and AI teams for a scope we agree together.
- Defined deliverables, or an agreed ongoing scope
- Works alongside your engineers rather than around them
- Coverage and responsibilities defined per engagement
One way to begin
A two-week assessment of one agreed workflow
Subject to scope and the access we can get, a two-week assessment of a single workflow is a reasonable first step. Potential deliverables:
- A cost, latency and quality baseline for the workflow in scope
- Prioritized improvements, with the reasoning behind the order
- One tested optimization where the scope and access allow it
Two weeks is enough to understand a workflow, baseline it and test one change. It is not enough to deliver a production system, and we would not describe it that way. Improvements depend on what the measurements show, so we do not quote savings or accuracy figures before we have looked.
Common questions
Before you ask.
- Can you work alongside our existing engineering or AI team?
- Yes, and it is a common arrangement. We take an agreed piece of work and coordinate with your engineers on interfaces, review and deployment rather than working around them.
- Can you help if we are still identifying a use case?
- Yes. An assessment that ends in "this one, for these reasons — and not those" is a legitimate outcome, and cheaper than building the wrong thing.
- Can you improve an AI workflow we already run?
- Yes. That usually starts with measurement: a baseline for quality, cost and latency, then improvements prioritized against what the numbers show.
Tell us what you are building
Describe the workflow, the product or the idea, and what you have already tried. We will come back with how we would approach it and which kind of engagement fits.