Services
Three services, all parts of a single growth foundation. Below you will find, for each one, the problem we run into, how we approach it, the process, the expected outputs, and who it suits.
GEO + SEO
A brand that reads clearly in search results and in AI answers.
- Technical SEO and content foundations
- AI visibility and brand representation analysis
- Content and structure matched to search intent
Outcome: A solid digital foundation for being found.
Problem
Content is being produced but it has no return in search results; the site is not technically crawlable, or what the brand does is described incorrectly or incompletely in AI answers. Most of the time the problem is not the amount of content but its structure and consistency.
How we approach it
First we measure where things stand: technical crawlability, site structure, how well content matches intent, and how the brand is represented in AI answers. Then we reduce all of it to a single priority list. Instead of hundreds of small fixes, we put the few structural jobs that will change the outcome first.
Process
- 01Technical crawl and a baseline visibility measurement
- 02Search intent and content gap analysis
- 03Reworking site structure and content templates
- 04Making structured data and brand information consistent
- 05Periodic measurement and content improvement
Expected work outputs
- A prioritised list of technical fixes
- A content and page structure plan based on search intent
- Structured data implementation
- Visibility measurement setup and a periodic report
Who it suits
Businesses that have a website, can produce content or have it produced, and can give room for medium- and long-term work. The results of this work show up in months, not weeks.
GEO and SEO flow: a four-step diagram running from a user question to the AI answer, to the sources behind that answer, to the brand’s visibility.
How does GEO work create visibility?
GEO is not an abstract concept; it starts from a single user question. The example below shows how we work through one query.
Example — illustrative content. Not a real client result or measurement.
Example user question
“What are the best CRM tools for B2B SaaS teams?”
What we look at through this query
- Which category does the AI associate your brand with?
- On which questions are competitors recommended ahead of you?
- Which sources and pages does the answer draw on?
- Is your brand’s service, expertise and location information consistent across sources?
- Which content and page structures are missing?
What we do next
We draw up the content, technical structure and authority plan that strengthens the contexts in which your brand can appear in AI answers.
What do we see when we look at this question?
This is how we read the picture that appears when we put the question above to five AI engines: which engine you show up in, how mentions shift from one measurement to the next, which sources were drawn on, who gets named, and what comes next.
Illustrative analysis
- 01
Example question
“What are the best CRM tools for B2B SaaS teams?”
- 02
Visibility engine by engine
- ChatGPT
- Gemini
- Claude
- Perplexity
- Google AI
Each engine answers the same question from different sources, which is why we look engine by engine instead of at a single average.
- 03
Brand mentions and tracking
- First measurement
- Second measurement
- Third measurement
The same list of questions is asked again on a regular cadence, and whether the brand appears in the answers is recorded the same way each time. What is being tracked is not a single score but where the mention shows up — in which questions and in which engines.
- 04
Types of sources the answer drew on
- Category comparison and “best …” lists
- Independent review and user-comment platforms
- Brands’ own product, pricing and FAQ pages
- Discussions in industry communities
- 05
Competitor comparison
- Your brandNot mentionedNever appears in the answers
- Competitor AMentionedNamed first in most engines
- Competitor BPartlyAppears in some engines only
- 06
Recommended next action
Category comparison content and a question-and-answer structure are built; product, pricing and use-case information is made consistent across sources. The next measurement uses the same list of questions.
This is an illustrative analysis, not real client data or a real result.
Meta Ads
Spend your budget with more control and clearer measurement.
- Campaign and audience setup
- Ad copy and creative testing
- Conversion and performance tracking
Outcome: Improve decisions with data instead of guesswork.
Problem
Ads are running but it is not clear where the budget goes. Which creative is working, which audience the budget is being wasted on, or whether the conversions shown in the platform correspond to anything real — none of it is known.
How we approach it
First we fix measurement; optimising an account you cannot measure is guesswork. Then we reduce the account to a simple structure, test creatives in a planned way, and make decisions based on test results. Rather than changing many variables at once, we set a pace where we can see what changed what.
Process
- 01Audit of the account, pixel and conversion measurement
- 02Defining the valuable action and wiring it to measurement
- 03Simplifying campaign and audience structure
- 04An ad copy and creative test plan
- 05Periodic review and updates to budget allocation
Expected work outputs
- An account audit note and a fix list
- Conversion measurement setup
- Campaign structure and audience definitions
- A creative test plan and an assessment of the results
- A periodic performance report
Who it suits
Businesses that can set aside an ad budget and can measure their sales or enquiry flow. Where measurement cannot be set up, we suggest closing that gap first.
Meta Ads flow: a five-step diagram running from campaign to audience, click, incoming enquiry and measurement.
Digital Agents
Digital coworkers that handle the repetitive work for you.
- Round-the-clock replies within a defined scope
- Appointment, quote and enquiry flows
- WhatsApp, e-mail and CRM processes
Outcome: Routine work runs itself so your team can focus elsewhere.
Problem
Incoming messages go unanswered outside working hours, the same questions get answered again every day, and enquiries spread across different channels so nothing can be followed up. A real share of the team’s time goes into copy-and-paste work.
How we approach it
First we work out which jobs genuinely repeat and whether their rules are clear. We automate only the jobs whose rules are clear. Every flow gets a handover point to a person; when the agent is unsure it does not decide, it hands over. We build the system simply enough that your team can read it.
Process
- 01Mapping the repetitive workflows
- 02Separating what gets automated from what stays with a person
- 03Defining the agent’s knowledge source and answer boundaries
- 04Setting up channel and CRM connections
- 05A limited launch, observation, and rule corrections
Expected work outputs
- A workflow map and the automation scope
- A document of the agent’s knowledge source and answer rules
- Channel and CRM integrations
- Handover-to-human rules
- A post-launch observation note and an improvement list
Who it suits
Businesses that regularly receive messages, appointment or quote requests. If the volume is low, we may suggest working on visibility or advertising before automation.
Digital agent flow: a five-step diagram running from an incoming message to the agent’s answer, the CRM record, follow-up, and handover to a person where needed.
What does this look like in practice?
These are not real client stories; they are examples built on situations we run into often. Their purpose is to make concrete how the three services work together.
Illustrative scenario — not a real client, result or measurement.
A local business that runs on appointments
Situation
Messages arriving through social media and WhatsApp go unanswered in the evenings and at weekends. Most people ask the same things: opening hours, price range, how to book a first appointment. The business listing exists, but competitors come up ahead of it in “near me” searches.
The system we build
Business information, service pages and frequently asked questions are organised for local search and map visibility. A digital agent connected to WhatsApp answers opening hours, price range and booking conditions; it routes suitable conversations into the appointment flow and hands every question it is unsure about back to the business.
Expected work output
Questions arriving outside working hours do not go unanswered, appointment requests collect in one place, and it becomes visible which question comes up how often.
A service business that requires expertise
Situation
People looking for the service ask an AI first; the business never appears in those answers. Incoming enquiries spread across phone, social media and the web form, so it is unclear who is being followed up.
The system we build
Service pages are reshaped into a question-and-answer structure; expertise, team and location information is made consistent across sources. Enquiries collect in a single flow. The agent only gives general information and directions; every question that requires expertise is handed to a person.
Expected work output
Brand information becomes consistent across sources, the load of answering basic questions drops, and the questions that need judgement stay with the team.
B2B with a long decision cycle
Situation
Buyers ask an AI about the category and the alternatives; the product is not mentioned in those comparisons. Because the quality of enquiries from advertising is not measured, nobody knows which campaign is working.
The system we build
Content and an asset structure are built to match category, comparison and use-case questions. On the Meta Ads side the valuable action is defined and wired to measurement. Incoming enquiries are qualified by the agent and written into the CRM.
Expected work output
It becomes possible to tell which content and which campaign brings qualified demand; the sales team goes into conversations already informed.
E-commerce selling products
Situation
Product pages show up in search results but not on “which one is right for me” questions. Post-purchase shipping and return messages take a real part of the team’s day.
The system we build
Category and comparison content is built and product data is structured. Ad budget is wired to conversion measurement. Common post-purchase questions are handed to the agent; exceptions are routed to a person.
Expected work output
Repeat order questions are answered automatically, the payoff from ad spend becomes measurable, and the team focuses on returns and exceptions.
Which one should you start with?
You do not have to decide that on your own. In the first conversation we listen to where you are now and work out together which of the three areas would give the most in return right now.
Let us design the right digital system for your business, together.
The first conversation is free and comes with no obligation. We listen to where you are now and work out together which step should come first.