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Implementation workshop · From a validated use case to a build blueprint

AI implementation workshop.

A structured session for product and tech leads who have already validated an AI use case and now want to make it ready for implementation. No exploratory conversation, no revisiting the strategy question: instead, a focused working block that ends with a blueprint covering architecture, model choice, data requirements, AI Act mapping, cost projection, risk mitigation and role allocation. Enough to request a quote, secure internal budget or draft an RFP.

Arrange an introductory call For execution after the workshop
FormatWorking-block workshop
AudienceProduct and tech leads
OutputImplementation blueprint
PrerequisiteUse case validated
VendorsIndependent
LocationAt your office + remote

What is an AI implementation workshop?

An AI implementation workshop is a focused working session in which we work with your product, tech and compliance teams to make an already validated AI use case ready for implementation, step by step. It is not a brainstorm about "what AI could do for us", nor a reconsideration of the problem; that is what an earlier AI design sprint is for. Nor is it execution; that falls to a follow-on implementation project. The workshop sits precisely between the two: you know what you want to build, but you do not want to start until the architecture, data logic, compliance and costs are laid out together in one coherent blueprint.

Between a go decision and a working production AI lies a phase where teams most often stumble: not over whether it can be done, but over the dozens of sub-decisions that shape the implementation. Will it be a hosted API or a model of your own? Which RAG pattern suits the data? Where will the logging for the AI Act live? Who will own the eval suite and sign off on model cards? Failing to answer those questions before build begins is the standard route to a pilot that never reaches production, or to a system that fails compliance review.

The workshop forces those decisions onto the table within a single working block, in the presence of the people who will carry them forward. At the end, you have an implementation blueprint that serves as the starting point for a quote request, an internal business case or an RFP. We also hand the document over neutrally to another party if you prefer. For those who want to continue with us, the blueprint becomes the build document for an implementation project or, at larger scale, an enterprise AI implementation. The recommended model choice is always tailored to your use case, not to what we sell, as we do not sell licences. That same vendor neutrality runs through our wider AI development approach.

10 modules
From architecture to governance and role allocation, all within one working block
Blueprint
A document with which you can kick off a quote, RFP or internal business case
Vendor-neutral
Independent model choice and architecture recommendation, no reseller deals
Dutch team
Senior Dutch engineers and compliance specialists, no offshore handover

When this workshop is a good fit?

The workshop is intended for teams who have moved past the validation phase and need to make concrete implementation decisions. A few recognisable situations.

01
After a design sprint

You have a go decision on the table

An AI design sprint or your own pilot has produced a well-founded go decision: you know which problem you want to tackle and have seen a working prototype. The question now is how to take this into production responsibly, and which architecture choices to make today so that the second use case fits as well.

02
Before requesting quotes

You want sharp tenders, not sales pitches

You want quotes from several parties or plan to issue an RFP, but the technical scope is not yet clear. Without that scope you receive ten incomparable proposals. The blueprint lets you tender on an equal footing, with the same architecture, data definition and compliance boundaries.

03
Before requesting internal budget

The board wants a substantiated investment case

You need to free up internal budget, and the board wants a substantiated projection, not "somewhere between X and Y" but a figure per phase with the assumptions stated. The workshop supplies the input: architecture choices with their cost implications, a lead-time indication, risks and mitigations, plus a role and responsibility matrix.

04
AI Act risk check

You suspect a high-risk classification

Your use case may fall under Annex III of the AI Act (HR, credit, education, critical infrastructure, law enforcement). High-risk fundamentally changes the architecture: documentation, logging, human oversight, robustness, post-market monitoring. Better to know before the build starts than after an audit.

05
Product organisation

AI feature on the roadmap, vendor choice still open

You are building a SaaS product and have an AI feature planned (assistant, search, classification, summarisation). You don't want to be locked into a single vendor, but want an architecture in which the second and third features can scale alongside the first. The workshop produces that layer plus a neutral model choice for the first feature.

06
Not for

Who still needs to decide whether it should be AI at all

This isn't the right choice if you are still unsure whether the problem is suited to AI at all, or which use case should take priority. For that, a design sprint or a broader AI business training is more logical. The workshop explicitly starts from the premise "the idea has been validated, now the execution".

Three principles that make the workshop different.

Many "implementation workshops" deliver a slide deck full of good intentions. We work from a different logic: three commitments that make the output usable rather than decorative.

Principle 01

The blueprint is the deliverable, not the slides

At the end of the session, a document lies on the table that records concrete choices: this model family, this RAG pattern, these data sources, this AI Act classification, this cost range, this role in governance. Not "we discussed it", but a document an external reader can understand without you being present.

Principle 02

Compliance is a design variable, not an afterthought

We factor the AI Act classification and the relevant regimes (GDPR, the need for a DPIA, DORA, NEN 7510, sector-specific rules) into every architecture decision. High-risk changes the architecture; it is not a paragraph written afterwards. For compliance-heavy sectors, we bring in a compliance specialist where needed.

Principle 03

You are under no obligation to award us the build

The workshop is a standalone service, not a sales vehicle. We deliver the blueprint neutrally so you can pass it to another party, carry it forward internally, or bring it to us for implementation. No mandatory follow-on means our advice can genuinely remain neutral.

The ten modules in the workshop.

Ten themes that are covered in one concentrated working block. Not every module carries the same weight; depending on your use case, we shift emphasis between architecture, data and governance. The overall scope remains the same.

01

Use-case architecture

The end-to-end architecture: where the AI system connects to your existing process, which services talk to which, and which contracts sit between the layers.

02

Model and stack choice

Which model type fits (LLM, classical ML, vector search, hybrid), which model family, and which fallback. Including the trade-off between cost and latency.

03

Data foundation

Which data is needed, in what quality and structure, where that data currently sits, and what work is required for cleaning or canonicalisation before the build.

04

AI Act high-risk check

The risk classification under the AI Act and, where an Annex III presumption applies, an initial mapping of Articles 9 to 15: documentation, logging, oversight, robustness.

05

Prototype architecture

The bridge from validation prototype to production architecture: which components stay, which must be rebuilt, and which will be replaced.

06

MLOps and observability

Evaluation suite, prompt versioning, logging, cost dashboard, drift monitoring and rollback plan. The tooling choices that will not need to be overturned later.

07

Integration with the existing stack

How the AI system fits into your IAM, data pipelines, monitoring and deployment practice. Where you integrate via API, where via event bus, and where via ETL.

08

Governance & Ethics

Use-case ownership, model card holder, escalation path, ethical review moments and how to handle bias signals. For compliance-heavy sectors, an enhanced variant.

09

IT Security & GDPR

Access model, data sharing with third parties, need for a DPIA, prompt injection mitigation and secrets management. The security layer the system is permitted to run on.

10

Roles & Change Management

Who becomes the owner, which business role signs off, what change is required for end users, and what training and communication should accompany it.

How the workshop runs.

The workshop is all about focus. We work through six blocks in a fixed order, and each block produces a section of the blueprint, so that by the end you have the complete document rather than spending a week chasing answers.

01

Preparation & Briefing

Beforehand, we share a short intake questionnaire and review the validation output (sprint report, your own pilot, scoping document): the problem, the user group, the intended effect, any model choices already made and the data context. We then send a draft agenda, which we adjust during the session to focus on what is most needed.

Intake questionnaireReview of validation outputDraft agenda
02

Architecture Block

We open with use-case architecture and the choice of model and stack. We sketch the end-to-end system on a whiteboard or in Excalidraw, weigh model types against each other (LLM, classical ML, embedding search, hybrid), and select a model family plus fallback, weighing cost and latency. The outcome of this block is an architecture diagram and a reasoned model-selection document.

End-to-end architectureModel trade-offsCost & latency
03

Data & Compliance Block

We work out the data foundation and the AI Act classification together, as the two are more closely linked than organisations tend to expect. Which data is needed, where it lives, and its quality and structure. At the same time, the AI Act mapping: which risk level applies and, for high-risk systems, an initial translation of Articles 9 to 15 into your context. The GDPR link and the need for a DPIA come along directly.

Data requirementsAI Act mappingAnnex III checkDPIA
04

Integration & MLOps Block

How the system connects to your IAM, monitoring, deployment practice and existing data pipelines. Which MLOps tooling we recommend (evaluation suite, logging, cost dashboard, drift monitoring, rollback), suited to what you already use so that it does not become a second platform layer. We also map the path from prototype to production: what stays, what must go, and what gets replaced.

Integration patternsEvaluation suiteObservabilityRollback plan
05

Governance & Roles Block

Who owns the system, who keeps the model cards up to date, who signs off on new versions, and how escalation works when output deviates. A first draft of the roles and responsibilities matrix covering business, IT, legal, risk and security roles. Plus change considerations for end users: what training, what communication, and what level of transparency about AI involvement.

RACI matrixModel card holderEscalation pathChange approach
06

Synthesis: Blueprint & Cost Projection

At the end, we bring the blocks together in a single implementation blueprint. We add the cost projection: build-phase investment, recurring model and infrastructure costs, and governance overhead. We also add a risk-mitigation list: for each architecture choice, the risks, the mitigation and the trigger for reconsidering. The blueprint is designed to be shared, whether with your board, in a quote request or in an RFP.

Implementation blueprintCost projectionRisk mitigationNext-steps plan

Which architecture choices we work with.

The recommendation is always tailored: the range of choices we work with is recognisable. Three layers that we weigh systematically, rather than starting from a fixed stack.

Model layer

Frontier, specialised or on-prem

For most implementations we weigh Anthropic Claude, OpenAI and Google Gemini against each other. Where data residency or latency calls for it, we look at Mistral, Llama 3 or Qwen on-premises or via Azure OpenAI, AWS Bedrock or Vertex AI.

  • Frontier API — broader capability, rapid iteration
  • Specialised model — domain-specific (vision, embeddings)
  • On-premises / private — where data residency or confidentiality is required
Architectural pattern

RAG, agent, classifier or hybrid

The pattern determines most of the architecture. We weigh retrieval-augmented generation, agentic flows with tool use, classic classifier approaches or hybrid combinations.

  • RAG — unlocking your own knowledge
  • Agentic — multi-step actions with tool use
  • Classifier — repeatable decisions on patterns
  • Hybrid — often the right choice in production
MLOps layer

Evals, logging, costs and governance

Tooling that keeps the system running and accountable: evaluation frameworks, logging for AI Act compliance, cost dashboards and observability for drift. Where possible, we align with your existing stack.

  • Eval suite — regression testing on prompt changes
  • Audit log — AI Act-compliant history
  • Cost dashboard — per use case, per team
  • Drift alerts — quality monitoring in production

What sets this workshop apart.

Three qualities that make the difference between an implementation workshop with a usable deliverable and a session that ends in slides.

Difference

Builders, not PowerPoint strategists

The workshop is led by people who build production AI. The architectural choices we recommend are the same ones we make in our own implementation projects — not a theoretical ideal that falls apart in practice.

Difference

Vendor-independent advice

No reseller deals, no partner commissions. The model we recommend is the one that suits your use case. Where on-premises is necessary, we advise open source; where speed and capability matter, often a frontier API. The choice depends on the use case, not on what we sell.

Difference

Compliance built in

The AI Act mapping and relevant frameworks (GDPR, DPIA, DORA, NEN 7510) are part of every architecture block. For compliance-heavy sectors (healthcare, financial, public), we bring in a compliance specialist where needed. You can present the blueprint directly to your legal or risk officer.

Difference

No sales vehicle

The workshop is separate from the implementation project. Afterwards, you can choose: take the blueprint forward internally, draft an RFP, request quotes from several parties, or have us carry out the implementation. There is no obligatory follow-on — which is precisely what keeps the advice neutral.

What you receive at the end?

The workshop produces a single deliverable: the implementation blueprint. It is built to stand on its own, so you can take it to your board, use it in a quote request or include it in an RFP.

  • Implementation blueprintThe central document — architecture drawing, model choice, data logic, AI Act classification, governance layer and cost projection in one coherent piece that an external reader can understand.
  • Architecture diagramThe end-to-end drawing with components, data flows, integration points and boundaries between layers — independently readable and suitable for inclusion in an RFP.
  • Model and stack selection rationaleRecommended model family with a fallback option, with arguments on capability, latency, cost and data residency. Plus a comparison with the other options considered so that the choice is traceable.
  • Data requirements overviewWhich data sources the system needs in production, which quality adjustments are required beforehand, which canonicalisation or cleansing is needed, and which contracts between systems are fixed.
  • AI Act mapping and risk classificationThe risk level under the AI Act, for high-risk systems a mapping of Articles 9 to 15, and the link with the GDPR, the need for a DPIA and, for financial institutions, DORA.
  • Risk mitigation registerFor each architectural choice, the risks, the mitigation and the trigger for reconsideration. Focused on the decisions where getting it wrong is expensive.
  • Cost projectionBuild-phase investment, recurring model and infrastructure costs, governance overhead — with the underlying assumptions included so the board can read a genuine business case.
  • Roles and responsibilities matrixAn initial allocation of owner, model card holder, sign-off, governance role, security role and change owner — with business, IT, legal and risk kept separate.
  • Tender / RFP templateA template that lets you request proposals from several parties on an equal footing, based on the same architecture, the same data definition and the same compliance boundaries. Tailored to any implementation project or enterprise AI rollout.

How we land the AI Act in the workshop.

The EU AI Act has direct consequences for the architecture of an AI system, not just for the paperwork around it. Organisations that only encounter the Act during an audit often find the architecture has to be redone. A workshop that ignores the Act delivers a blueprint that cannot legally be built.

In the data and compliance block we determine the risk classification. We explicitly check whether the use case falls under Annex III (HR decisions, credit decisions, education allocation, critical infrastructure, law enforcement, migration and asylum, administration of justice, democratic processes) or under a prohibited practice from Article 5. For a high-risk classification, we work out the initial mapping against Articles 9 to 15: the risk management system, data and data governance requirements, technical documentation, logging, transparency, human oversight and robustness. That mapping feeds directly into a different logging layer, a different oversight flow and a different model card discipline.

We also work out the link with the GDPR, the need for a DPIA and, for financial institutions, DORA. For sector-specific regimes we bring in the relevant references: NEN 7510 in healthcare, the algorithm assessment framework for public bodies, and sector guidelines in financial services. The workshop does not replace a formal DPIA or audit; those belong to implementation. However, the mapping in the blueprint is complete enough for you to build your AI development approach on, and for your legal or compliance officer to form an initial view. For sectors where that distinction is critical (healthcare, financial, public), a compliance specialist with sector expertise joins the table.

Three scenarios where this workshop lands.

Scenario · Service organisation

AI-triaged incoming enquiries, production-ready

A service organisation has validated a prototype in which an AI agent pre-sorts incoming customer enquiries. Workshop question: which architecture, which model, which human handover and which compliance layer. Output: a blueprint with a retrieval approach, an escalation flow, an evaluation suite for classification precision and an AI Act mapping for customer interaction.

Scenario · Product organisation

AI feature on the SaaS roadmap, vendor choice open

A SaaS product has a planned assistant feature to be built into the product. Workshop question: which model layer, which RAG pattern over product data, what the cost implications are, and which architecture allows the second feature to scale as well. Output: a blueprint with a layered architecture, model selection with a fallback, and an MLOps approach.

Scenario · HR tooling with AI Act impact

HR system with an AI component, high-risk route

An HR tooling vendor adds an AI component that falls under Annex III. Workshop question: what does the architecture look like if Articles 9 to 15 are the guiding principles? Output: a blueprint with a human oversight flow, a logging layer, model card discipline and the GDPR link.

Fabian van Dijk Business Developer · Appfront

Frequently asked questions.

What is the difference from an AI design sprint?
An AI design sprint is validation: should this idea receive investment or not, backed by a prototype and user testing as evidence. An implementation workshop starts after that go decision and addresses the delivery question: how do we bring this into production responsibly. The sprint asks whether the idea holds up; the workshop asks how it should be built. Both are standalone services, and you can go straight to the workshop if you have already completed the validation elsewhere.
And what's the difference with an implementation project?
An AI implementation project is the delivery: build, governance set-up, change management and handover. A workshop produces the blueprint that such a project takes as its starting point. Many clients choose the workshop as a standalone step to keep their freedom in choosing a vendor: they use the blueprint to request quotes from several parties, or to build the internal business case before selecting a partner.
Do I have to hand the build to you after the workshop?
No. The workshop is a standalone service, and the blueprint you receive is written neutrally: suitable for passing to another provider, taking forward in-house, or bringing to us for delivery. There is deliberately no obligatory follow-on built into the format; that is what allows our advice to be genuinely neutral.
When is this workshop not useful?
If you are not yet sure whether the problem suits AI, or which use case should take priority. In that case a design sprint or a broader AI business training is the more logical choice. The workshop explicitly starts from "the idea has been validated, now the delivery"; anyone still needing to choose first needs that validation.
How do you handle the AI Act during the workshop?
AI Act classification is a fixed block in the session. We explicitly check whether the use case falls under Annex III, and for high-risk classification we work through Articles 9 to 15 — not as legal advice, but as a design constraint for the blueprint. GDPR, the need for a DPIA and, for financial institutions, DORA are brought in directly. For compliance-heavy sectors (healthcare, financial, public) we bring in a compliance specialist so that the advice fits the sector.
Which models and vendors do you weigh up?
For most use cases we weigh Anthropic Claude, OpenAI and Google Gemini against each other. Where data residency or sensitivity matters, we weigh Mistral, Llama 3 or Qwen on-premises or via Azure OpenAI, AWS Bedrock or Vertex AI. No reseller deal, no mandatory integration: the recommendation is tailored to your use case.
Who should be at the table?
Ideally the product or business owner of the use case, a technical lead, a data or integration representative, and a legal or compliance role. For compliance-heavy sectors, a security or risk role as well. We help with the composition in advance during the intake.
Do you work with our data and systems?
For the workshop we work from descriptions of your data and systems, not live access, which keeps the barrier low. When moving into delivery (in an implementation project), access is set up in line with your security practice and under NDA, with data tenancy where classification requires it.
What if we discover during the workshop that the idea needs to be rethought?
That can happen, usually when the data reality turns out to differ from what was assumed during validation, or when the AI Act classification unexpectedly comes out as high-risk. We say so openly and adjust the scope. If the deviation is large, we first recommend a short return to validation (a light design sprint) before it makes sense to complete the blueprint.
Does this suit enterprises or scale-ups?
Both, with a different emphasis. For scale-ups the architecture and data modules weigh more heavily; for enterprises the weight shifts towards governance, change and integration with existing architecture. For multi-business-unit organisations the blueprint is often part of a wider enterprise AI implementation track, which we discuss during the intake.

Talk to us about your implementation workshop.

A thirty-minute introductory call in which we go through which use case you have validated, which implementation questions remain open and who should sit at the workshop table. No obligation; afterwards we send an outline with scope and transparent pricing.

Schedule an introductory call Email fabian.vandijk@appfront.nl
Response within 1 working day
No-obligation conversation
Westerdoksdijk 599, Amsterdam
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