Alien Road uses Claude, Anthropic’s family of large language models, as a working tool inside defined campaign steps: research synthesis, content briefs, long-document analysis, quality checks and report drafting. Every output has a named human reviewer, and client data goes only through accounts and settings that meet our data handling rules. Claude speeds up the work around decisions; it does not make the decisions.
What is Claude and why choose it for agency work?
Claude is a set of AI models built by Anthropic, available through the Claude apps, team and enterprise plans, and the Claude API, including through Amazon Bedrock and Google Cloud Vertex AI. It handles text and images, works with long documents, and can use tools when connected to them. Anthropic releases models in tiers, with larger models for harder tasks and smaller ones for speed and cost.
For agency work, three traits matter. Claude handles long inputs well, so a full brand guideline or a year of campaign exports fits in one conversation. It follows detailed instructions closely, which helps with house style and compliance rules. And its writing tends to need less clean-up for tone, though it still needs fact checks.
Where does Claude sit in a campaign?
We map each campaign into stages and decide where AI helps. The list below shows the stages where we use Claude most often in a typical engagement.
- Discovery: summarising client documents, past reports, interview notes and competitor pages
- Strategy: drafting audience and message options for the team to debate
- Content: building briefs, outlines and first drafts for writers and editors
- Paid media: generating ad copy variations within character limits and policy rules
- Analysis: interpreting exported data from Google Ads, Meta, GA4 and CRMs
- Quality checks: reviewing pages against a checklist before launch
- Reporting: drafting the narrative around verified numbers
How we use Claude in discovery
Discovery used to mean days of reading. Now a strategist uploads the client’s brand book, sales deck, past campaign reports and transcripts of stakeholder interviews into a Claude project. We ask for a structured summary: stated goals, audiences named, claims the brand makes, and contradictions between documents.
Contradictions are the most useful part. When the sales deck targets one audience and the brand book another, that becomes a question for the kickoff meeting. The strategist still reads the key documents personally; Claude makes sure nothing in the pile is missed.
How we use Claude for content and ads
For content, Claude drafts briefs from a keyword cluster, search results our team has reviewed, and the client’s service pages. Briefs follow a fixed template so writers always know where to find intent, questions and internal links. Writers then produce the article, sometimes using Claude for an outline or a rewrite of a weak section.
For paid search and social, we give Claude the product facts, the offer, character limits and the platform’s policy points that apply. It drafts variations, which a paid media specialist edits and approves. We never let generated claims about pricing, guarantees or results go live without checking them against the client’s own materials.
How we use Claude for data analysis
Claude is good at explaining a dataset once the numbers are correct. Our analysts export data, build calculations in spreadsheets or BigQuery, and then give Claude the cleaned tables. We ask it to point out unusual movements, suggest causes to investigate, and draft questions for the client.
We keep the arithmetic outside the model. Language models can still misread tables, so any figure that appears in a report is traced to a formula or query, not to generated text. When Claude is connected to a code execution tool, we review the code it writes before trusting its output.
What rules govern our use of Claude?
These rules apply on every account. They exist because speed is only useful if the work stays accurate and the client’s data stays protected.
- Client data goes only into business accounts or API setups whose terms exclude training on our inputs.
- Personal data of the client’s customers is removed or minimised before analysis.
- Every output has a named reviewer before it reaches the client or the public.
- Factual claims are checked against a source the client has approved.
- Prompts and templates are stored in a shared library, versioned, with an owner.
- We tell clients which parts of their work used AI tools when they ask, and many ask.
How do we measure whether it helps?
We track two things per task type: time spent and edit depth. Time is logged before and after introducing a Claude step. Edit depth is a simple reviewer rating of how much the output had to change. A step that saves time but needs heavy editing gets reworked or dropped.
At the campaign level, AI use is judged by normal results: rankings, leads, cost per acquisition and revenue. Claude does not get credit for results on its own. It gets credit if the team ships more tested ideas, faster, with the same or better quality.
Where Claude does not help
Claude does not know the client’s market the way the client does. It cannot see live ad accounts unless connected, and even then we prefer people to make bid and budget changes. It can write a confident paragraph about a statistic that does not exist, which is why fact checks are mandatory. Creative concepts that need a real insight about customers still come from people talking to customers.
Where to start this week
If you want to try a similar setup, pick one recurring document your team produces, such as a monthly report or a content brief. Write a template, create a Claude project with your style guide and one strong past example, and draft the next one with it. Time it, have someone rate the edits needed, and decide from those two numbers whether the step stays.
Repeat the same test on a second task only after the first one is stable. Teams that add AI to five processes at once usually cannot tell which change helped and which one created extra review work, and they end up abandoning all of them.
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