SEO & AI SEO agents
Machine-like builds and runs custom SEO and AI SEO agents that turn search opportunities into completed work. Research, content, technical improvements, and AI-search optimization, managed for you so more of the right customers can find and choose your business.
Your team sets the direction and approves what ships. We build, operate, and maintain the agents that keep the work moving.
14 days of live work. No credit card. Your trial begins when your agent goes live.
Lake: 47% AI citation share on tracked unbranded lake-rental prompts in 21 days. See the work and the results →
01 / Direct answer
Agentic SEO uses AI agents to carry out the recurring work of search optimization: researching opportunities, preparing content, improving pages, and monitoring results. Agents follow a defined goal, use connected tools, and complete multi-step jobs within the permissions and approval rules you set.
Traditional SEO helps your website compete in search results. AI SEO, also called generative engine optimization (GEO) or answer engine optimization (AEO), helps your business become easier to find, understand, and reference in AI-generated answers. Agentic SEO describes how the work gets done across both.
With Machine-like, you hand off a defined job. We configure the agents around your business, connect the tools, check the output, and keep the operation running. Your people keep the positioning, expertise, and judgment.
02 / The offer
A content gap, a broken page, or an inaccurate AI answer only becomes useful information when someone acts on it. Machine-like connects the finding to the work required to improve it.
Identify the searches, buyer questions, and competitive gaps that matter to your business.
Prepare the page, correction, technical update, or outreach draft. Route it through the agreed review and publishing process.
Monitor what changed, refresh what is going stale, and bring the next priorities back to your team.
Start with the job that is costing your team the most time or leaving the clearest opportunity unaddressed. Add connected workloads as the operation proves its value.
03 / Research & strategy
Build your search plan around the customers you want, the problems you solve, and the buying decisions you can influence.
Your research agents combine keyword data, current search results, competitor coverage, and your own performance data to identify useful opportunities. Volume, difficulty, relevance, and purchase intent inform the order of work.
Group related searches into topics and buyer stages. Identify where you need a service page, comparison, buying guide, product explanation, or a stronger answer on a page you already have.
Study the pages and sources appearing ahead of you. Surface missing topics, weak explanations, and unanswered objections, then build a backlog tied to the offers you want to grow.
Revisit priorities as rankings shift, new questions emerge, and competitors update their content. Keep the roadmap connected to current evidence.
What you get: A prioritized opportunity map with target queries, buyer intent, recommended pages, supporting evidence, and the next action for each.
04 / AI visibility & agent analytics
A buyer can build a shortlist before visiting your site. Your AI SEO agents help you understand where your business appears in those answers and what deserves attention next.
Monitor a defined set of buyer prompts across the answer engines in your scope, such as ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. Organize them by product, service, audience, and buying stage.
See whether an answer names your brand, recommends it, or links to your content. Compare the language used about your business with the competitors that appear alongside it.
Identify cited pages and recurring sources. Turn missing explanations, competitor comparisons, and weak third-party coverage into specific content and authority tasks.
Where access logs are available, monitor identifiable AI crawler activity, frequently requested pages, failed requests, and changes in crawl patterns. Use those signals to prioritize accessibility checks and content refreshes.
What you get: A visibility baseline, recurring competitor comparisons, a record of sampled answers, and a prioritized queue of changes.
AI answers vary by prompt, model, date, and context. Crawler activity shows access to your site; it does not establish a buyer's identity or exact question.
05 / Content creation & refreshes
Give your content operation a consistent path from research to a useful, reviewed page on your website.
Agents research current search results and buyer questions, then build outlines around the information a visitor needs to decide. Your expertise, customer examples, and verified product facts make the answer yours.
Brand guidelines, approved claims, terminology, and examples guide every draft. Agents can prepare articles, comparison pages, service pages, FAQs, and supporting visuals for review.
Clear headings, direct answers, useful comparisons, source references, internal links, metadata, and appropriate structured data help people and machines understand the page.
Identify declining pages, outdated facts, broken references, and missing answers. Improve the assets you already own instead of letting the backlog grow.
Every draft passes through your agreed approval process before publication. Machine-like connects the workflow to your CMS, checks the published result, and maintains the agent as your requirements evolve.
What you get: Research-backed briefs, on-brand drafts, reviewed updates, and published pages with the supporting metadata and internal links.
06 / Technical foundations
Good content still needs to be accessible, coherent, and easy to navigate. Your agents help find and resolve the issues that get in the way.
Crawlability and indexing: Check important pages for access problems, broken links, redirect issues, conflicting canonicals, and indexing directives.
Page structure: Improve titles, descriptions, heading structure, internal linking, and the relationship between supporting and commercial pages.
Structured data: Prepare and validate relevant schema that reflects the visible content and verified facts on the page.
Performance checks: Surface slow pages and recurring technical regressions, with a clear route to approved fixes or development handoffs.
Programmatic pages: Build repeatable page workflows from reliable data for distinct products, use cases, or locations, with checks for duplication and thin content.
Changes ship within the permissions and review rules agreed for your site. Larger engineering work is identified and scoped before it becomes a surprise.
What you get: A ranked issue queue, approved corrections, validation evidence, and a clear record of anything needing a developer.
07 / Authority & brand accuracy
What the web says about you can be incomplete, outdated, or wrong. Your agents help strengthen the facts and sources that customers and AI systems encounter.
Compare sampled answers with approved facts about your pricing, capabilities, locations, and policies. Flag discrepancies and inspect cited sources or other likely origins when available.
Update the pages you control. Prepare correction requests for those you do not. Recheck answers over time to see whether the information changes.
Research credible directories, industry publications, review sites, and other sources relevant to your category. Prepare accurate profiles, contributed-content ideas, and outreach drafts.
Identify useful community discussions and creator opportunities where your expertise can add value. Your team approves external communication and participation.
What you get: A factual issue log, proposed corrections, researched authority opportunities, and approved outreach materials.
Third-party editors control their sites, and AI providers control their answers. We can improve the available evidence and pursue corrections; placement and response changes are not guaranteed.
08 / Built around what you sell
A software buyer, a local customer, and an online shopper need different answers. We scope the agents around the decision your business needs to win.
Product agents can identify missing attributes, normalize inconsistent information, and prepare richer descriptions, FAQs, comparison content, and structured product data. Verified materials, dimensions, compatibility, use cases, and limitations give buyers more reasons to understand the fit.
Map verified product facts to buyer language: who the product suits, which situations it fits, and which comparisons it answers. Score the gaps against the questions you want each product to cover. Prepare enriched data for the supported feed, API, structured-data, or publishing path available in your stack.
Connect product pages to helpful collection pages and buying guides. Track visibility by product or collection, then connect identifiable AI referrals to shopping activity where your analytics supports it.
Illustrative example: A backpack listed only as “Everyday Pack” becomes a clearer product record: verified capacity, dimensions, laptop fit, material, weight, care instructions, and intended uses. Missing facts return to your team for confirmation.
Keep business details consistent, prepare Google Business Profile updates, improve service and location pages, and connect verified credentials and customer evidence to the right offering.
Build the use-case pages, comparisons, implementation explanations, and FAQs that help buyers assess fit. Give each page a clear route to a demo, consultation, or inquiry.
What you get: More complete product or service information, a reviewable change set, and a content plan grounded in how your customers decide.
09 / Reporting & conversion
Measure the path from discovery to business value, with enough detail to decide what should happen next.
| Layer | What your agents report | What it helps you decide |
|---|---|---|
| Execution | Pages published, updates completed, issues fixed, and items awaiting approval. | Is the agreed work getting done? |
| Search visibility | Search impressions, clicks, target-query performance, and page trends. | Where are we gaining or losing discovery? |
| AI visibility | Mentions, citations, recommendation patterns, and competitor presence in tracked answers. | Which questions and sources deserve attention? |
| Business outcomes | Identifiable referrals, inquiries, signups, purchases, and attributed revenue where tracking permits. | Which pages and channels bring useful demand? |
Agents can also surface weak conversion paths and prepare improvements to page messaging, proof, forms, and calls to action. Your team approves the changes and the experiments used to evaluate them.
What you get: A recurring report connecting completed work, observed changes, unresolved issues, and the next priorities.
An AI mention is not a visit, and a visit is not a sale. Reports distinguish observed referrals from inferred influence and identify gaps in attribution.
10 / A concrete workflow
A prospective customer asks which provider is best for their situation. Competitors appear. Your business does not. Here is how a scoped agent workflow can respond.
Identify the gap. Save the sampled answer, the cited sources, and the buying criteria it emphasizes.
Check your evidence. Compare your existing pages with verified product facts, customer proof, and those criteria.
Prepare the improvement. Draft the missing explanation or comparison, update relevant metadata and links, and propose appropriate schema.
Get approval and publish. Your reviewer checks the claims and positioning. The agent publishes through the approved workflow and verifies the result.
Watch what happens. Recheck the tracked question, inspect search and referral trends, and bring the next improvement back to the queue.
Illustrative workflow, not a customer result or a promise that a specific change will earn a citation.
11 / Machine-like customer results
Machine-like's agents mapped traveler questions, restructured pages around direct answers, reinforced entity facts, deployed structured data, and tracked citations. Lake continued creating its own content.
On the tracked unbranded lake-rental prompts, Lake reached 47.0% citation share, compared with Airbnb at 41.9%, measured with Profound. The published case also reports U.S. answer share rising from 0.9% to 13.5%.
Read Lake's story →Agents consolidated entity facts, verified people and programs, rewrote high-intent pages, and distributed authority signals. Machine-like reports a 102% increase in month-over-month enrolled users, with results reported at 120 days.
Read CourseCareers' story →Agents unified brand facts and schema, rewrote product pages as useful guidance, and published connected topic clusters. The published result: mentions in 18% of AI answers in its measured sensitive-scalp category after five months.
Read Better Scalp's story →“What stands out is Machine-like's technical approach to automation. They've got this down to a science.”
Troy Buckholdt · CEO, CourseCareers · Source
Selected customer results reported by Machine-like. Each reflects a specific scope, measurement set, and time period; results vary.
12 / Comparison
The useful comparison is what happens after an opportunity is identified: who produces the work, who checks it, and who maintains the process.
| Responsibility | SEO / AI visibility tool | Agency or freelancer | Machine-like agents |
|---|---|---|---|
| Research and monitoring | Provides data and recommendations. | Included according to the engagement. | Agents gather evidence and maintain the scoped monitoring workflow. |
| Content and updates | Your team executes or connects separate tools. | Delivery depends on the agreed scope and cadence. | Agents prepare and deliver the recurring work through defined approval gates. |
| Business judgment | Your team owns it. | Shared with the provider as agreed. | Your team owns strategy, claims, approvals, and exceptions. |
| Workflow maintenance | Your team owns the process around the software. | Depends on the service agreement. | Machine-like builds, hosts, monitors, and fixes the agents. |
| Commercial model | Software subscription; execution is a separate cost. | Retainer or project fee. | Monthly pricing per defined workload, after a live trial. |
Comparison of typical operating models, not an audit of every vendor. Agencies and tools vary; your agreed scope determines delivery.
13 / Fit assessment
Bring us the recurring job you would most like off your team's plate. We will help define a workable scope and tell you if agents are a poor fit.
Assess your SEO workload with us →14 / Getting started
Scope the work. Share your goals, site, tools, approved facts, and examples of work done well. Agree on outputs, cadence, success criteria, and approvals.
We build and test. Machine-like configures the agents, connects authorized systems, and validates the workflow. Timing depends on the scope and access required.
Your live trial begins. Review actual completed work for 14 days. Check its quality, how the approvals work, and whether the handoff saves your team effort.
Keep the operation running. Continue on a monthly plan if the workload earns its place. We maintain the agents and make included adjustments as your rules evolve.
What the trial proves: That the agent can complete the agreed work to your standards. Rankings, citations, and revenue require a longer measurement window.
15 / Pricing
Start with a recurring SEO or AI SEO job. A flat monthly rate covers the agent built to do it and the operation that keeps it running.
1-2 workloads
$249
per workload / month
Email + Slack support
3-5 workloads
$199
per workload / month
Email + Slack + video support
6+ workloads
Custom
Scoped to your operation
Dedicated success manager
A workload is a recurring job, not every individual step. For example, an agreed content workflow can include briefs, drafts, schema, and publishing. The complete set of capabilities on this page can span multiple workloads; we define the scope and price before building.
Every workload includes configuration, testing, hosting, monitoring, fixes, approval gates, and run records. Published pricing includes no setup or integration fees, unlimited seats and connectors, and monthly billing with cancellation anytime.
No credit card or upfront payment. Your 14-day trial starts when the agent is live.
Hand off the research, production, updates, and reporting that keep slipping down the list. Give your people more time for the expertise and decisions that make your business worth choosing.
Tell us the first SEO job you want done. We will scope it, build the agent, and put it to work.
14 days of real work. No credit card. Built and operated by Machine-like.
17 / FAQs
A chat tool helps you produce an output when you prompt it. A scoped agent workflow connects the surrounding steps: research, approved business context, drafting, review, publishing, verification, and reporting. Machine-like builds and operates that recurring process.
SEO focuses on discovery through search engines. AI SEO, GEO, and AEO describe overlapping work to make your business easier to retrieve and accurately represent in generated answers. Agentic SEO is the execution model: agents perform defined work across those channels.
Yes. We scope the operation around the channels that matter to your business. Traditional search work can include research, page optimization, content, and technical checks. AI-search work can include prompt tracking, entity clarity, answer-focused content, and citation monitoring. The supported engines and measurement methods are agreed during scoping.
No. Search engines, AI providers, competitors, and buyers influence those outcomes. Machine-like's commitment is to successful completion of the agreed work to your standards. We measure visibility and business results to guide the next round of improvements.
Completed work can be evaluated as soon as the agent is live. Search and AI visibility changes take longer and depend on your starting position, competition, and how quickly updates are approved and discovered. The 14-day trial evaluates delivery, not a guaranteed ranking or revenue lift.
The GEO and content workflow includes approval of every draft before publication. We define the review owners, permitted actions, and escalation rules during scoping, then configure the agents around those rules.
We build around your existing stack and access you authorize. The scoping process confirms your CMS, analytics, search data, and any product or CRM connections the job needs. Deeper integrations and legacy systems are assessed before the workload is agreed.
Catalog enrichment can be scoped as a dedicated workload: identifying missing information, preparing verified attributes and product copy, adding useful FAQs, and supporting structured product data. We first confirm your catalog system, available facts, product volume, and review requirements.
Agents can flag discrepancies against approved facts, inspect available citations, prepare corrections to your own pages, and draft requests to third-party publishers. We cannot directly edit an AI model's knowledge or guarantee when an answer will change.
We track visibility in sampled answers separately from identifiable referral traffic and conversions. A mention can be useful evidence of visibility without proving a visit or sale. Reports make that distinction and use the analytics available in your stack.
Not necessarily. One workload covers one agreed recurring job and its steps. A broader operation involving research, publishing, technical work, catalog enrichment, and reporting may require several workloads. We quote that scope before building.
Provide business context, accurate source material, tool access, and an owner for approvals. Your people make the judgment calls. Machine-like builds, operates, monitors, and maintains the agents; exceptions reach the right reviewer.
Yes. An existing team can own strategy and creative direction while agents take on defined production, upkeep, and reporting jobs. Scope the handoffs and approval responsibilities so everyone knows who owns the next step.
Approval gates and exception handling are configured before launch. If a run fails or an output is wrong, Machine-like investigates and fixes the issue as part of the managed service. The run record makes the work reviewable.