AI SEO/SEM
We build a custom AI workflow to prepare organic-search recommendations and paid-campaign drafts from your approved data. It can review technical SEO, organize keywords and summarize performance; your team reviews the work before publication, campaign activation or budget changes. The workflow fits your CMS and approved analytics access, and reports results from available data.
- Search intent
- SEO and SEM plan
- AI-prepared drafts
- Human approval
- Measured results
Your team approves publication, campaign activation and budget changes.
- Check crawlability, page intent, internal links, sitemaps, canonicals and hreflang.
- Prepare paid-search drafts with agreed keyword exclusions, landing pages and budget caps.
- Review actual GA4, Search Console and ad data, subject to consent and approved access.
AI Content
Forget the CMS admin. Create content by chat.
Clutch Developer designs and builds a custom AI content service around your editorial process. A teammate can turn approved product notes into an article, LinkedIn post and newsletter draft, refine a paragraph by asking in chat, and prepare market-specific versions. Editors review sources, preview changes and approve publishing. The chat workspace can connect to a suitable CMS or use a publishing backend built for your needs.
- Source notes
- Chat draft
- Editor review
- CMS or backend
- Measure
A person checks accuracy and approves each release; the system records versions and publishing status.
- Use approved product notes, briefs and brand guidance as the source for each draft.
- Request a precise edit, a market version or another format, then compare it in preview.
- Require a named editor to approve publishing; retain revision history and a rollback route.
- Track qualified leads and conversions only where consent and analytics rules allow; no ranking or traffic result is guaranteed.
Private AI Computing
Some projects need model inference and document search close to the data. Clutch Developer has private physical computers available for projects that require them. We can configure a project environment for local inference and retrieval, then document what stays inside its boundary, what can leave and how that is verified.
- User
- Permissions
- Local retrieval
- Local model
- Review
Each stage stays inside the agreed project boundary unless a specific external path is approved and verified.
What you get
We agree which project data is in scope, who may access it and which network connections the work needs. That boundary shapes the model, document index, application, logs and backups.
Private physical computers for the work
Clutch has private physical computers available for projects that need local computing. We agree whether a machine or isolated environment is reserved for the project, what hardware is available, and what throughput and response time the work needs. Model size, quantization, concurrency and context length all affect memory, speed and quality; a local model is not a drop-in guarantee of cloud-scale capacity.
Keep inference and project knowledge local
Where the chosen hardware and model allow it, prompts and model inference run on the private computer. Project documents can be parsed, embedded and indexed inside the same agreed boundary, then retrieved for a response with source references. Access controls define who can add, search or remove material. We test retrieval on representative questions, because a confident answer is only useful when the right source was available and selected.
Make the data boundary testable
A fully local setup can keep project data 100% inside the agreed boundary if external model calls, telemetry and cloud sync are disabled. We gate outbound traffic and verify network behavior; this confirms the data path, not universal security.
Choose the model with samples, license and hardware in mind
Open-weight candidates such as Qwen and DeepSeek may fit some private workloads, subject to the exact release license, hardware, latency and quality requirements. The Qwen quickstart describes local inference and deployment options; the DeepSeek-V3 repository publishes its model and local-running information. Those references do not establish that every model fits a given machine. We check the selected weights and license, estimate resource needs, and benchmark the client's own examples before committing to an architecture.
We can shape the workflow in ways analogous to those used with a hosted assistant such as Claude Opus 4.8: retrieve context, call approved tools, check intermediate work and ask a person to review. That is a workflow comparison, not evidence of equal model quality. The chosen local model must pass the agreed sample tasks, and a human remains responsible for consequential output.
- Map List the data, users, actions and external connections in scope.
- Benchmark Test candidate models and retrieval on approved client samples.
- Configure Set permissions, local indexes, logs, backups and gated network routes.
- Verify Inspect the release configuration and test that traffic follows the documented boundary.
Operate it as a real system
A local model still needs updates, access reviews, disk capacity, backup checks and a plan for hardware failure. We configure application and retrieval logs inside the agreed boundary, choose what those logs contain and who can read them, and test restoration from backup. Model updates are evaluated against the acceptance sample before they replace a working version. These operating tasks make the setup understandable after the initial build.
We document the local data boundary and verify the configured network paths. Device security and day-to-day operations still matter.
Integrations without a fixed catalog
External integrations are optional. We can connect Slack, Telegram, WhatsApp, email, Jira and your own systems through approved connectors, without a fixed catalog. Sensitive documents, model processing and local search stay in the private environment. You choose which outputs may leave it; external channels receive only those approved outputs. For a fully local setup, communication and integrations remain inside the agreed private network.
For other product and workflow options, return to the AI Factory Agency. To discuss a specific data boundary or workload, contact Clutch Developer.
Consulting
Intro to AI for Teams & Leadership
A focused session that gives your team and leadership a shared, realistic picture of AI. We look at what today's models genuinely do well, where they fall short, and which of your problems are worth pointing them at first.
AI Adoption & Rollout Consulting
Getting your team to actually use AI every day is a change problem as much as a tooling one. I help you pick the right tools, set sensible data policies, and run the hands-on workshops that turn curiosity into daily habit.
AI Product Development
I build LLM features that survive contact with real users — not just demos. From scoping the right feature to shipping it with evals, guardrails and a cost budget, I've done this on my own products and can do it on yours.
AI Assistants & Copilots
Custom AI assistants and copilots that actually know your business. I build support agents, internal knowledge Q&A and ops copilots — grounded on your own data, wired to your tools, and delivered on the channels your people already use, like web, WhatsApp and Slack.
AI is useful when it helps someone complete a real task in a product they already understand. That takes more than choosing a model: it takes clear interaction design, reliable data, sensible permissions and a safe route for work the system cannot handle. Our AI Factory Agency works across the feature and the operation around it, so the pieces fit together.
Start with the person and the job
We map what a customer or colleague is trying to do, where the current process slows down, and what information the software is allowed to use. Then we shape the experience: what the AI suggests, what it can do, what it must explain, and how a person corrects or declines its work. A useful interface makes uncertainty visible and keeps important decisions with the right person.
Design, build and connect the whole product
The work can include UI/UX research and prototyping, a mobile app for iOS or Android, a web product, APIs, integrations and the operations behind them. We make the AI fit existing accounts, permissions and data flows, or build a focused product around a new use case. Monitoring, error handling and a clear hand-off matter as much as the model call: they determine whether a feature remains usable on an ordinary Tuesday.
Give useful content a path to discovery
For products that need an audience, we can create and structure reviewed content alongside the software. That can support SEO for organic search, SEM for paid campaigns, GEO for discovery in generative answers, and ASO for app-store listings. The work starts with a real audience, accurate source material and pages worth finding. Search and answer systems change, so no agency can promise a ranking, indexing or traffic result; we report what was published and what can actually be observed.
Prove the behavior before widening access
Before building, we agree on representative inputs, the expected result, unacceptable errors and the cases that must go to a person. We test against those examples, record where the system succeeds or fails, and decide together whether the evidence supports a pilot or a wider release. Outcomes depend on the quality and coverage of your inputs, the model, integrations and review process. We do not promise a metric in advance; we make the acceptance test explicit.
- Define Choose one real workflow, its users and a measurable acceptance test.
- Build Design the experience and connect only the data and actions it needs.
- Review Run agreed examples, inspect failures and keep human approvals where they matter.
- Extend Use evidence from the pilot to set the next scope and operating capacity.
- User task
- Product design
- AI + your systems
- Human review
A person stays in the loop wherever an error, permission or business decision calls for judgment.
A good AI brief names the input, the expected outcome, the person responsible for approval and the failure path. We scope from that evidence, then size the build to the capacity you need.
If the work needs a tighter data boundary, see Private AI Computing. For a first conversation about a product or workflow, tell us what you need.
FAQ
How much does an AI project cost?
The starting prices are €10,000 for a project and €1,000/month for monthly capacity rental. Your quote depends on scope, capacity, setup, hardware, external provider costs and applicable taxes.
What kind of company or team is this for?
It suits teams with a specific product or operational task, access to representative examples and a person who can approve the result. We first check whether AI is useful for that task; a conventional rule or software change may be simpler.
Will our data be sent to an external AI provider?
That depends on the agreed setup. We document data paths and access before choosing an external model or a private local setup.
How do you decide whether the AI is good enough?
We agree on representative inputs, acceptable outputs, failure limits and the human approval points before the pilot. Results depend on your data and workflow, so we measure against that agreed sample instead of guaranteeing a general accuracy figure.