These illustrate the approach. They are not invented brands. When a client allows publication, we replace the scenario with facts: timeline, stack, and a measurable result.
Problem. The catalog went down during campaigns, nobody had tested a backup, deploys were SSH by hand.
What we did. Move to AWS, CloudFront, a separate staging environment, a pipeline, restore tests.
Timeline. 4–6 weeks depending on catalog size and integrations.
Stack. S3 / CloudFront or ECS, RDS, GitHub Actions, Route 53.
Target outcome: the site holds peak traffic, a release is not scary, there is a way back.
Problem. The bill keeps climbing, keys are shared, a bucket is public, nothing is tagged, nobody knows what can be turned off.
What we did. IAM and MFA, close the leftovers, budget alerts, a resource map, a backup policy.
Timeline. 2–3 weeks for the audit and baseline cleanup.
Stack. IAM, CloudWatch, Budgets, Secrets Manager, S3, RDS snapshots.
Target outcome: a readable bill, personal access, fewer surprises.
Problem. A small press wanted to sell original books online — author pages, covers, samples, and checkout — not a magazine archive. The old site was a brochure with PDFs in a folder. Google barely indexed the titles. A new release day stalled the pages, and there was no staging to preview a drop before it went live.
What we built. We stood the store up on AWS: CloudFront in front of the catalog, object storage for covers, interiors, and sample chapters, a managed database for books and authors, separate staging and production, and a deploy pipeline so a new title could go live without SSH. Each book and author got a stable URL. Product, author, and “buy now” pages were treated as commercial pages, not afterthoughts — cart, tax, and print vs. ebook in the same flow.
What we optimized. Technical SEO first: sitemap by book and author, Book and Person schema, canonicals for hardcover vs. ebook, image compression so cover-heavy pages hit Core Web Vitals. Then demand work: title and author pages mapped to how people search for the books, internal links from author to buy, Search Console and GA4 on product views and checkout starts. After launch we kept a retainer for cache, index coverage, and release-day load.
Timeline. About 8–10 weeks to launch. SEO compounding over the following quarter.
Stack. S3, CloudFront, ECS or Elastic Beanstalk, RDS, Route 53, GitHub Actions, Schema.org, Search Console, GA4.
Outcome: the bookstore is live on a stack that survives a new-title drop, Google can crawl books and authors, and sales are not limited to people who already knew the press.
Problem. An auto shop’s site was a static list of services and a phone number. People bounced when they could not get a number on the screen. Staff quoted jobs in the bay with half the facts. Photos arrived on WhatsApp with no vehicle, no mileage, no preferred slot. There was nothing to calculate against — and no record of the lead if the call dropped.
What we built. A public site plus a dynamic intake module that runs before any estimate. The customer picks the vehicle (year, make, model, engine), the concern, mileage, photos of the damage or dash, and a time window. That packet hits an API on AWS, lands in a queue for the advisor, and only then does the shop return a calculation or a “bring it in” slot. No quote without a complete job file. The shop dashboard shows new requests, status, and a one-click reply with the range.
What we optimized. Local service pages (brakes, diagnostics, AC) for “repair + city” queries, click-to-intake from every service block, speed on photo upload, and events in GA4 for start → complete intake → quote sent. After launch we tuned the required fields so the form did not kill conversion, and we kept index coverage on the service URLs.
Timeline. About 6–8 weeks to launch the site and intake. Quote-flow tweaks in the first month live.
Stack. CloudFront, S3 (photos), Lambda / API Gateway, DynamoDB or RDS, SES for advisor alerts, GitHub Actions, GA4, Search Console, local landing pages.
Outcome: the shop gets a structured job before it quotes, photos are not lost in chat, and search traffic lands on an intake — not a dead phone number.
Problem. Ads produce expensive leads, organic search is almost empty, service pages cannibalize each other.
What we did. Technical foundation, keyword architecture, page tree, commercial copy, analytics goals.
Timeline. The first technical layer is weeks. Demand growth is months.
Stack. Search Console, GA4, Schema.org, template edits on the site.
Target outcome: leads from organic search, not only visits to the homepage.
AWS Management