AI Digital Marketing Agency

We first identify the job your business needs done. We agree on a starting scope that fits your existing data and team, instead of introducing every available tool at once.

• If customers cannot find you in search: Our SEO and GEO service reviews service pages, technical access and customer questions. Deliverables include a prioritised page list with reasons and proposed improvements for review.
• If advertising results are unclear: In Google Ads management, we review search terms, enquiry measurement and landing pages together. We agree on who owns budget and implementation decisions.
• If following up enquiries takes too much time: Our workflow automation service defines a bounded process from incoming enquiry to CRM record and team notification. Missing information, duplicate records and failures are part of the scope.

DijitalPi develops the marketing systems and web tools used in its AI agency work. The projects below address different needs, from planning and content production to visitor communication and measurement. Each link explains the relevant system's workflow and scope.

PiOS and Piflow: work management and data infrastructure

PiOS, the marketing operating system (page in Turkish), manages SEO, advertising, measurement and reporting work in one place, schedules tasks and routes them to specialist engines. Budget, publishing and customer-message decisions follow defined permissions and approval rules. We use PiOS in our own operations and accept company installation requests through a waiting list.

Piflow, our marketing data platform, brings data from connections such as Google Ads, GA4 and Search Console into a shared working environment. Our team uses this infrastructure for analysis and reporting. PiOS manages the work; Piflow supplies the data that supports it.

Engines for content, advertising and reporting

Each engine supports research, preparation or checks for a specific job. Publishing, advertising changes and customer communication follow the team's review and approval process for that work.

• SEOMACHINE: Examines search data for SEO and GEO, prepares content priorities and drafts, and checks sources to support the editor's publishing decision.
• PPCMACHINE: Analyses Google Ads spending and campaigns, checks ad copy compliance and prepares recommendations for our specialists.
• GTMMACHINE: Supports measurement planning, GTM and GA4 setup, advertising conversion connections and conversion-accuracy audits.
• RAPORMACHINE: Uses period data from its source to help prepare client reports, documents and presentations.
• SMMMACHINE: Uses brand information to support social media planning, visuals and copy. Outputs go through quality checks.
• MAILMACHINE: Turns a content file into a send-ready email package containing HTML, plain text and metadata.
• MARKETPLACEMACHINE: Organises marketplace rules, platform comparisons and seller-account launch preparations.
• CHANNELMACHINE: Scans sources for WhatsApp channels, prepares short news messages and tracks link clicks. The team approves sharing.

Website, visitor communication and sales tools

• WEBMACHINE: Brings website setup and management into one service. Its product page explains the scope, working process and packages.
• PiAIMode: Answers visitor questions using the website's published content and helps them find the relevant page, tool or way to get in touch.
• AloPi: Answers spoken questions using website information and opens the relevant treatment, doctor or contact page. Installation suitability depends on the site's infrastructure.
• CRM & Sales Management System: Supports lead follow-up through the sales process and tracking what happens to enquiries from advertising.

Measurement, research and tool discovery

• PiScan: Provides an initial check of your website's accessibility to AI systems. Its check score is not proof of citations in actual AI answers.
• PiSEO: Helps you examine technical SEO and identify areas for improvement.
• PiAds and Adlizer: Tools we developed to inspect competitors' live ads and support advertising research.
• ai101.tools: Our directory for discovering AI tools from different developers. DijitalPi develops the directory; listed tools belong to their respective makers.
• UsagEX: Our tool for tracking Claude usage and limits.

We start with the job you need to solve. SEOMACHINE may suit content work, PiAIMode or AloPi may help with visitor questions, and PiOS may suit managing fragmented marketing operations. Discuss the installation and service scope with us, or explore our other developments in the product and systems catalogue.

Our email enquiry to quotation preparation application explains how free-text product requests are matched to a technical catalogue. Uncertain matches go to a person, and quotations are not sent automatically. The application is published anonymously; the interface and demo use illustrative data.

Our JEV search-term analysis application shows how brand context informs the review of advertising queries. The workflow description is not a guarantee of advertising return or accuracy. We can use the relevant example to identify the data your own process would need.

For an initial discussion, share your website, the process you want to improve and the systems you use. Do not enter account passwords in the form. Discuss the scope of your AI agency engagement.

An AI digital marketing agency is the type of agency that actively runs artificial intelligence systems at every stage of a marketing operation — from research to content, from advertising management to reporting — but leaves strategic decisions and quality assurance to human experts. Here, artificial intelligence is not a marketing channel but a production and analysis infrastructure running beneath all the channels.

Clarifying that definition matters, because the phrase “AI agency” is used in the market for two different things. The first is agencies that occasionally add tools such as ChatGPT to their work but run operations by classic methods. The second is agencies that have built their workflows around artificial intelligence from the ground up, using AI systematically from keyword research to competitor analysis, and from ad budget allocation to content review. A genuine AI digital marketing agency is the second — and the difference is felt directly in the speed, depth and consistency of the service you receive.

Nor is this shift a niche trend. According to McKinsey’s The State of AI 2025 research — a survey conducted in June–July 2025 with around 2,000 respondents across 105 countries — 88% of organisations regularly use artificial intelligence in at least one business function; a year earlier that figure was 78% (McKinsey, The State of AI 2025). Marketing and sales are among the functions where artificial intelligence is most widely used. The question is no longer “should artificial intelligence be used”; it is “who is using it, in which process, and under what supervision”.

The critical distinction is this: artificial intelligence is not a strategy on its own. Everyone can access the same tools; what makes the difference is the marketing knowledge, sector experience and quality filter with which you run them. That is why DijitalPi defines itself not as “the agency that has AI do everything” but as an agency using artificial intelligence as a strategic growth infrastructure directed by experienced marketing specialists.

An AI digital marketing agency offers all the services a classic digital agency does — SEO, advertising management, content, data analytics, CRM and automation; the difference lies in the production and analysis layer working behind each of those services. More data is processed on the same budget, more variations are tested, and decision cycles shorten.

Let us summarise in one table how each service area is transformed by artificial intelligence:

What every row in the table has in common is this: artificial intelligence carries the production and analysis load, and the expert team sets the direction. A machine produces ad copy variations in minutes, for example; but a person decides which message fits the brand’s positioning and which promise would fall foul of sector regulation. This division of labour becomes concrete, process by process, in the sections that follow.

The core difference lies in the operating model: a traditional agency scales work with human effort, while an AI-powered agency scales data-heavy work with artificial intelligence and devotes human effort to strategy. That does not mean traditional agencies are bad — in areas such as creative depth and brand intuition, the established agency tradition remains strong.

A balanced comparison looks like this:

Honesty is needed on two points. First, the AI-powered model does not deliver automatic superiority in every task: long-term brand positioning, corporate identity and big creative concepts remain largely human craft. Second, unsupervised AI use can produce worse results than traditional methods — faulty data interpretation, generic content and drift from the brand voice lose trust in the pursuit of speed. The right question is not “traditional or AI”; it is “which team uses AI, and under what model of supervision”.

DijitalPi is not a start-up founded on the wave of artificial intelligence; it is a digital marketing agency that served clients for years by traditional methods and then rebuilt its operation around artificial intelligence by deliberate decision. That order matters: marketing was learned first, and artificial intelligence was added on top of that accumulated knowledge.

The agency’s founder, Selim Çitil, has over 20 years of marketing experience; a background in corporate marketing at Yıldız Holding and the 2019 Stevie Awards achievement are the verifiable milestones of that experience. To date the DijitalPi team has gained advertising management experience across 125 countries and 15 languages, running campaigns in B2B and B2C and in sectors from healthcare to e-commerce.

The observation behind the decision to transform was simple: most of a marketing operation — data gathering, scanning, reporting, producing variations — consists of repetitive, data-heavy work. Running that work on human effort is both slow and expensive; worse, it consumes the time the expert team would otherwise devote to strategy, where it creates real value. DijitalPi handed that work over to artificial intelligence systems step by step, developed its own internal tools, and positioned its human team at the layer of oversight, strategy and client relationships. Today’s model is the result of that transformation: the speed of artificial intelligence and the strategic judgement of an experienced team working together in the same operation.

Our use of artificial intelligence is not a general promise but a way of working defined process by process: in strategy and research, SEO, content, Google Ads, Meta advertising, data analysis and automation, the role of artificial intelligence and the role of the human expert are clearly separated. Below we explain each process concretely.

Strategy and Research

Every engagement begins by taking a photograph of the market. Artificial intelligence scans competitor sites’ content structures, search behaviour data and sector signals to extract patterns — which topics are empty, where a competitor is strong, which way demand is shifting. The strategy team combines that map with the business’s goals and field realities to turn it into a roadmap. Not every opportunity the machine finds is an opportunity to pursue; prioritisation requires commercial judgement, and that judgement stays with people.

SEO

In our SEO operation, artificial intelligence carries out work such as keyword clustering, search intent classification, semantic coverage analysis of competitor content, and detection of performance decay in existing pages. Decisions about site architecture, topic cluster structure and which page serves which commercial goal belong to our SEO specialists. We also structure content not only for Google but in a format that answer engines such as ChatGPT and Perplexity can cite as a source — a separate layer of expertise beyond classic SEO. All of our SEO services work on this dual-target model.

Content Production

In content, artificial intelligence produces the draft; a person decides on publication. The process works like this: a brief is drawn from research data, artificial intelligence produces the draft in line with that brief, and every draft then passes editorial review for source verification, brand language alignment and sector rules. Unverified statistics, fabricated sources and generic filler text are eliminated in that review. The result: capacity far above classic production speed, but a human signature on every published text.

Google Ads

In Google Ads management, artificial intelligence is a continuously running analysis layer: it scans search term reports, flags where budget is flowing inefficiently, produces ad copy variations and monitors changes in conversion signals. The expert team assesses those signals and intervenes in the account structure — changes to bidding strategy, campaign restructuring and budget shifts always pass through human approval. Rather than switching on Google’s own automation (smart bidding, for instance) blindly, we build a second analysis layer that audits it from outside. Details are on our Google Ads management page.

Meta Advertising

On the Meta side the biggest lever is creative volume: you cannot know in advance which image or message will work; you test. Artificial intelligence speeds up the production of creative variations and their performance comparison, and detects ad fatigue (the same creative losing its effect) early. Audience strategy, funnel structure and brand safety decisions remain with our social media advertising team. Test volume rises while the brand’s face stays consistent.

Data Analysis and Reporting

Most businesses have data but no insight; the data sits scattered across different platforms. Artificial intelligence brings advertising, analytics, search and CRM data under one roof, flags anomalies and produces the raw answer to “what changed versus last week, and why”. Our analysts translate that output into business language: which metric matters, which deviation requires action, where budget should shift. Our data analytics service builds the report as a decision-support system, not a file. That layer is also the data source for conversion rate optimisation work: patterns in user behaviour turn into test hypotheses.

Automation and CRM

The process from the moment a lead enters a form to the moment it converts into a sale is where automation produces the highest return. With AI-supported flows, leads are scored automatically, assigned to the right segment and passed to the sales team in priority order; follow-up scenarios on channels such as email and WhatsApp run without manual intervention. Our CRM setup and customer relationship management service designs that infrastructure around the business’s sales process — automation is built to your sales flow, not to a template.

In our model human oversight is not a marketing phrase but a defined operational layer: no output produced by artificial intelligence — content, ad copy, strategic recommendation or report interpretation — goes to the client or to publication without passing expert review. This layer is the most frequently overlooked and most critical part of an AI-powered model.

The concrete scope of editorial control is: accuracy review (statistics, dates and source claims are verified; unverifiable data is removed from the text), brand alignment (consistency of tone, positioning and discourse), sector rules (regulatory compliance, especially in regulated sectors such as healthcare), and strategic fit (does the content or campaign genuinely serve that month’s goal). Artificial intelligence’s known weaknesses — fabricating sources, missing context, becoming generic — are caught precisely in this layer.

There is a quality assurance dimension to this too: the producing system and the reviewing eye are separate. The mechanism that produces the draft does not approve its own output; review runs independently. This principle resembles code review in software, and it is what rescues quality in AI production from chance. The honest answer to “is the content produced entirely by AI?” also lies here: we take the speed of production from the machine and its reliability from people — the two are not alternatives but two stations on the same line.

The advantages an AI-powered agency model brings a business gather under four headings: speed, scale, depth of data and cost efficiency. None of these is an abstract promise; each is a direct result of the processes described above and can be tracked through regular reporting.

Speed. Work that takes weeks — market research, competitor analysis, campaign setup — comes down to days. More importantly, so does speed of response: a drop in performance is noticed when the signal appears, not at the monthly report meeting, and intervention happens in a short cycle.

Scale. Content architecture, ad variations and test volume are no longer limited by headcount. A hundred-page topic cluster or dozens of creative variations become manageable without quality dropping — because the bottleneck is not production but review, and review is systematic.

Depth of data. Where a human analyst looks at a sample, the machine scans all the data. Every search query, every ad placement and every user behaviour flow is within the scope of analysis. Decisions rest on complete data in addition to intuition.

Cost efficiency. Automating repetitive work shifts agency effort towards strategy; the same budget buys more production and more analysis. An honest note here: an AI-powered model does not mean a “cheap agency” — an experienced review layer is costly. The correct reading is that the value per unit of work increases.

The AI-powered agency model suits businesses of any size that want to make data-driven decisions and see marketing as a system requiring continuity; the greatest benefit is seen by businesses whose production volume, channel variety or data complexity has reached a point that cannot be managed by effort alone. To make it concrete by segment:

• SMEs: Continuous optimisation and automation of repetitive work carry critical value in stopping a limited marketing budget from leaking away. A business that cannot build a corporate-scale team gains corporate-level analytical power through the AI-powered model.
• Corporates: In multi-channel, multi-stakeholder operations, data consolidation, reporting automation and brand consistency review come to the fore. DijitalPi's corporate marketing experience rooted in Yıldız Holding knows this segment’s language.
• E-commerce: Large product catalogues, a continuous campaign cycle and feed management are AI's most natural working ground; creative test volume works directly on ROAS.
• Healthcare and dental clinics: In this regulated area, oversight is as critical as speed; the human editorial layer makes regulatory compliance part of the production process. DijitalPi's healthcare sector experience knows that balance.
• B2B and SaaS: In long sales cycles, lead quality, scoring and CRM integration are decisive; building content authority requires being visible during the buying committee’s research process.

Who is the model not for? For businesses that see marketing as a one-off project, do not want to share data, or expect definitive results in the first week, no agency model — AI-powered or not — works well.

When assessing an AI digital marketing agency, what matters is not its list of tools but the model of oversight and experience surrounding those tools. The checklist below consists of objective criteria you can apply to every agency you speak with — including DijitalPi:

1. Is the human oversight layer defined? Saying “we use AI” is not enough; they should be able to explain clearly which output is checked, at which stage, and by whom.
2. Is there marketing experience predating AI? Artificial intelligence does not replace marketing knowledge; it multiplies it. Ask about the team’s career history and sector experience independent of AI.
3. Does it provide process transparency? An agency that says openly what runs automatically and what runs manually establishes the basis of a trusting relationship. “Trust us, the system handles it” is a warning sign.
4. Is the data security policy concrete? Ask which tools your data enters, whether any of those tools use your data as training data for a model, and how KVKK compliance is handled.
5. What is the measurement and reporting rhythm? The interpretation matters as much as the contents of the report; look for reporting that answers “which decision did this data change”, not “how many impressions did we get”.
6. Does it know your sector? Especially in regulated sectors (healthcare, finance), an agency that has embedded sector rules into its production process protects you from the cost of fixing things afterwards.
7. Is expectation management realistic? Stay away from an agency that promises “guaranteed first place” or commits to a definite outcome — with or without AI; a serious agency talks to you about scenarios and probabilities, not guarantees.
8. Is there a content quality policy? Ask an agency producing at scale how it combats fabricated sources, unverified statistics and generic text; an agency with a written editorial policy will be glad to answer.

Working with DijitalPi is a seven-step system beginning with a free preliminary analysis before any proposal and continuing with a cycle of ongoing optimisation. The output of each step is concrete; you know from the outset what will be done, when, and how it will be measured.

1. Discovery call: We listen to your business, your goals, your current marketing setup and your sales process. At this stage we carry out a needs analysis, not a sales conversation.
2. Current-state audit: Your ad accounts, site, analytics infrastructure and content assets are scanned with our AI-supported audit tools; strengths, losses and opportunities are reported.
3. Strategy and roadmap: Audit findings are combined with your business goals; channel priorities, budget allocation and period targets are clarified. Nothing moves to implementation without your approval.
4. Infrastructure setup: Measurement (conversion tracking, analytics), CRM connections and automation flows are built. We do not open campaigns without solid measurement infrastructure; what you cannot measure, you cannot manage.
5. Production and launch: Content, campaigns and creative are produced, pass editorial review and go live.
6. Continuous optimisation: The artificial intelligence layer monitors performance without interruption; the expert team assesses signals and intervenes. This cycle never stops for the duration of the campaign.
7. Reporting and strategy update: Regular reports cover what was done, what was learned and what will change in the next period; the strategy is updated as a living document.