Talk to us about AI in your agency.
A non-committal introductory conversation of half an hour. We listen to how your agency works, which disciplines you have and where the pressure is greatest — and give direction you can actually use.
Service · AI development
AI for marketing agencies.
From content generation to creative AI, from pitch decks to reporting automation. We help marketing, communications and digital agencies deploy AI structurally, without client data leaking, without the work becoming interchangeable, and without downplaying legal risks.
AI touches every hour of your agency.
The client base of a marketing or communications agency is broad: full-service, content, design, branding, PR, performance, SEO, social, video, podcast, event and B2B. Every type of agency now feels the same pressure. Clients expect AI to add something to the output, competitors already claim they use it, and your own staff are experimenting regardless, whether or not there is a policy.
The heart of our work is that AI plays two roles at an agency at once. First, it changes how the work is made: copy, imagery, video, audio, translation, scheduling and reporting are all planned differently. Second, AI itself becomes the product an agency delivers to its clients, whether as a sub-project, a white-label tool or an integrated part of a larger campaign. We build for both sides and make sure the two connect. For agencies we deliver an in-house AI implementation programme, custom LLM integrations and generative AI solutions that agencies sell on to their clients.
What we see is that agencies that now invest seriously in their own AI layer build a lasting advantage, not because AI gives them more creativity, but because it takes away the tedious half of the work. Time that used to go into repeatable reporting or variant generation now goes into strategy, concepts and client contact. Agencies that skip this moment risk having their output labelled cheaper by clients' procurement AI than it is worth. That is no longer a distant scenario; it is already happening with brands expanding their in-house creative teams with generative tools.
Three ways we work with agencies.
The right format depends on where you stand: whether you need an internal AI layer, client projects with an AI component, or training and governance for your team. We often combine them in a programme spanning several sprints.
AI layer for your own team
A secure working environment where staff can use ChatGPT, Claude, image generators and in-house models on client data, without that data ending up in public models. Includes your own prompt library, a brand voice system and integrations with your existing tools.
Private LLM accessBrand-voice presetsPrompt libraryAudit log
AI as part of campaigns and products
You pitch your client on an AI element, such as personalised landing pages, a campaign bot, dynamic ads or a knowledge assistant, and we build it behind the scenes. You keep the client relationship; we deliver the technology, including documentation and handover.
RAG assistantsPersonalisationCreative AIIntegrations
AI tools you resell yourself
A white-label SEO tool, a dashboard generator or a campaign reporting AI that your agency delivers to clients under its own brand. Complemented by AI literacy training for your team, so that the whole agency understands what the tools are and are not meant for.
White-label appsSaaS licencesTeam trainingClient onboarding
Use cases we see working in agencies.
Not every use case suits every agency. We always begin with an audit that looks at which processes consume the most time, which output is most easily standardised and which parts touch client data.
- Content generationBlogs, social posts, emails and ad copy based on briefs and brand voice. Claude and GPT-4 as the core model, with human editing as the final step.
- Image and video generationMidjourney, Stable Diffusion, DALL-E and Adobe Firefly for stills; Runway, Sora and Veo for moving image. Tuned to brand style in advance.
- Voice and audio AIElevenLabs for voice-overs and podcast edits, Suno for jingles and soundtracks, and Anthropic voice for client call scripts and demos.
- Translation and localisationDeepL and LLM translation for campaigns across multiple markets, with automated QA against the brand glossary and regional tone guidelines.
- SEO AIKeyword clustering, intent classification, schema generation and content optimisation. Often connected to your own SEO stack rather than as a standalone tool.
- Ads AIPerformance Max, Meta Advantage+ and our own creative AI for generating variants. Includes monitoring of what the algorithms actually decide.
- PersonalisationCDP AI, dynamic segmentation and page personalisation for clients with enough volume to make conversion gains worthwhile.
- Email AISubject line optimisation, A/B test generation, send-time prediction and personalised body blocks based on engagement history.
- Social listening and sentimentLLM classification of brand mentions, crisis detection and topic trend reporting. Particularly relevant for PR and social media agencies.
- Reporting AIAutomatic summaries of campaign data from GA4, Meta, LinkedIn and Search Console. A final report in your own template, ready for review.
- Brief and pitch AIGeneration of creative briefs, client research decks and pitch pages. Speeds up new business without outsourcing the strategy.
- Internal knowledge baseRAG built on your agency's own IP: cases, frameworks, templates and brand guides. Answers questions from new staff and speeds up onboarding.
Which agencies this is relevant for.
We work with a wide range of agency types. The use cases differ, but the pattern, wanting to speed up without losing quality or brand consistency, comes up everywhere.
Full-service and content
Higher content volume
Full-service agencies, content agencies and B2B marketing agencies that want to deliver more output per FTE without the work becoming stale. Brand voice systems and editorial guardrails are central here.
Design and branding
Creative AI in production
Design and branding agencies that want to use generative image and video tools alongside their in-house creative team. Copyright checks and style consistency are crucial.
Performance and SEO
Handling large data volumes
Performance marketing and SEO agencies using LLMs for reporting, keyword work, content briefs and schema generation. This is where most time is saved per employee.
Social, PR, video
Real-time monitoring
Social media, PR and video/podcast agencies that combine social listening, sentiment analysis, transcription and automated edits. Often linked to a dashboard of their own.
Events and experience
Client onboarding bots
Event, experience and adtech agencies using conversational AI for registration, answering questions during events and post-event reporting to sponsors.
Local and niche
Standardisation without loss
Local marketing agencies and niche players who want to offer a broad range of services with a small team. AI helps scale production without losing the personal approach.
Adtech and data
Pipeline automation
Adtech agencies and data-driven specialists who connect LLMs and in-house models to campaign data, feed management and attribution models. Here AI becomes part of the delivery chain, not just the output.
B2B and account-based
Client-specific content
B2B marketing agencies that want to deliver dedicated landing pages, propositions and outreach flows for each target account. Account-level personalisation instead of segment-level only becomes feasible with an AI layer underneath.
How we build an AI programme with an agency.
1
Audit
We map out which processes take the most time, which output can be standardised, and which parts touch client data. By team and by discipline. The result: a list of use cases with estimated impact and risk.
2
Choosing tooling
Off-the-shelf where possible (Claude Teams, ChatGPT Enterprise, Copilot, Midjourney), custom where necessary (your own RAG, your own brand voice engine, your own integrations with your PM and CRM systems). We include total cost of ownership honestly in the calculation.
3
Quick wins
We roll out the first tools to one or two teams so the impact in production is visible before we scale further. Brand voice presets, a prompt library and initial reporting automation.
4
Custom layer
Deeper integrations: a proprietary LLM layer with client data isolation, RAG on your agency IP, and white-label tooling for your clients. This is where our AI strategy and build expertise really comes into its own.
5
Training and governance
AI literacy for all staff, internal policy for client data, documented in line with the AI Act. We tie training to tooling so people understand where the models are reliable and where they are not.
6
Ongoing development
Models change every quarter, and so do pricing models. We stay involved to help your stack evolve and to add new use cases once they are stable enough for client work.
Not yet sure about a large project?
Test your idea first: a working prototype in 1 day
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →What sets a good agency AI programme apart.
Most agencies that approach us have already done something with AI: a team using ChatGPT on business accounts, a freelancer using Midjourney for moodboards, an SEO stack getting LLM features. That is a good start, but it lacks three things: coherence across disciplines, control over client data, and a shared brand voice. The result is a patchwork in which each team invents its own prompts and nobody knows exactly where client data ends up.
We turn that patchwork into a single stack. Not by forcing everything into one central tool, because creatives need freedom, but by putting a shared layer beneath the tools that handles brand voice, client data isolation and logging. On top of that, each team keeps working with the tools that suit it best, whether that is Claude, GPT, Firefly, Runway, DeepL or a custom RAG assistant. The result: more speed and less risk at the same time, with a traceable overview for management.
The biggest difference lies in the handover. Many AI projects are pitched as a standalone tool and end up as one: nobody takes ownership, and after a few months it is forgotten in a drawer. We always deliver on two levels: the technology and the way of working. A tool without a way of working is abandoned within three months; a way of working without a tool is a loaded gun with no ammunition. Delivering both together, with training, is what separates an AI programme from an AI experiment.
Compliance is not optional.
For an agency, AI compliance is weightier than for most clients, as you handle client data, creative work and sensitive campaigns. From the very first conversation, we build with these frameworks in mind.
GDPR
Client data remains your client data
Client information, briefings, audience files and creative work must never end up in public training sets. We choose models with enterprise contracts and data isolation, and we document how data is stored, for how long, and who has access to it.
AI Act Art. 4
Literacy is mandatory
Since 2 February 2025, an AI literacy obligation has applied to everyone working with AI. For an agency, this means demonstrable training for all staff who use AI in client work. We arrange this as part of the rollout.
Copyright
Training data risk
Image generators trained on copyrighted work carry legal risk, especially when the output closely resembles existing styles. We advise which tools are commercially safe and which are for inspiration or moodboards only.
Transparency
AI disclosure and deepfake labels
The AI Act requires labelling of AI-generated or manipulated content in certain cases. We help you set out a policy on when and how you communicate AI use to end clients and consumers.
Why an agency chooses Appfront.
We are not a reseller of AI licences. We build, integrate and train, and we do so with clients who are strong in their own creative and strategic work. That interplay works best for us.
- We build it ourselvesNo white-label resale. Our engineers write the code, integrate the models and do the fine-tuning where needed. You always know who is at the table.
- Model-agnosticAnthropic Claude, OpenAI GPT, Google Gemini, Mistral, open source: we choose based on the use case, not on vendor loyalty. You are never locked in.
- We understand agency economicsCapacity planning, billable hours, client mix and pitch pressure: we have worked inside and alongside agencies ourselves. Our advice fits how you make money.
- Honest about what AI can't doWe say so plainly when a use case is still too shaky, or when the time saving doesn't outweigh the risk. We'd rather deliver late than fail early.
- Compliance from day oneGDPR, the EU AI Act, copyright and transparency are built into our checklists. We won't let you pitch something that won't be permitted in six months' time.
- Ongoing developmentAI models change constantly. We offer maintenance contracts in which we keep developing your stack, adding new use cases as they become ready for client work.
- Training on what we buildA tool without literacy stays on the shelf. We tie every rollout to a training programme, so the whole agency understands when a model is making a good guess and when it is off the mark.
- Strategy and engineering in one teamWe work with you at pitch level and right down into the architecture. Our AI strategy approach and our engineers are on the same project, with no handover moment in between.
Frequently asked questions.
Questions that agency owners and strategy directors ask us before an AI project begins.
What does AI look like in practice at a marketing or communications agency?
At its core, it's a combination of four things: a secure working environment with enterprise access to LLMs, your own library of prompts and brand voice, integrations with the tools you already use (Slack, Notion, Figma, GA4, Asana), and clear agreements on which client data may and may not go into models. On top of that come specific use cases for each discipline: content, design, performance and social.
Which AI tools are now standard in an agency's stack?
For most agencies, an enterprise LLM account (Claude Teams, ChatGPT Enterprise or Copilot) plus an image generator (Midjourney, Firefly, Stable Diffusion) is a good starting point. Beyond that it depends on your focus: SEO tooling, email AI, translation, video generation, voice. We always advise starting with two or three quick wins before rolling out a broader stack.
Should we train or fine-tune our own LLM?
Almost never from scratch. For most agency applications, RAG (retrieval-augmented generation) on your own agency IP (case studies, frameworks, brand guides, past campaigns) is a more effective route than fine-tuning. It's cheaper, easier to maintain and delivers comparable quality. Genuine fine-tuning only comes into play for very specific output requirements.
What about GDPR if we pass client data through an LLM?
Two things are crucial: an enterprise contract with the vendor stating that your data will not be used for training, and a data classification within the agency so staff know which data may and may not go into a prompt. For sensitive projects we opt for private deployments (Azure OpenAI, AWS Bedrock) in which client data never leaves your own cloud.
Is generative image AI safe from a copyright perspective?
It's nuanced. Models trained on copyrighted work without a licence carry a risk, particularly when the output visibly resembles the style of an existing creator. Adobe Firefly was deliberately trained on licensed material and is therefore commercially safer. For other tools, we advise using them for moodboards and internal concepts, and human creation for the final campaign output.
What determines the cost of such a project?
Mainly the scope: how many teams are involved, how many custom integrations are needed, and whether we are also building white-label tooling for your clients. Licence costs (LLM tokens, image credits) are billed separately and are usually predictable once the first few months have been measured. We work in sprint budgets so that scope and costs are transparent every two weeks.
How do we measure the ROI of AI in an agency?
Three yardsticks used in combination: time saved per unit of output (report, blog post, ad variant), quality as assessed by account directors and clients, and commercial impact — more pitches won, more scope sold, higher margins per project. We help set up these measurements and include them in the agency's management information.
How do white-label AI tools compare with custom client projects?
A white-label tool generates repeatable revenue under your own brand, with low marginal costs per client. Custom client projects yield higher margins per project but cannot scale without additional people. Most agencies we work with do both: one or two white-label products as a base load, alongside individual AI components in larger campaigns. We help you choose which route suits your positioning.
What do we do if models or regulation change?
We replace models when a newer generation is demonstrably better or cheaper for your use case — not because a new name appears in the press. We monitor regulation (the AI Act, GDPR case law) on an ongoing basis and translate relevant changes into concrete adjustments to policy and tooling. This is standard in a managed services contract. Outside a managed services contract, we get back to you with an update periodically.