Built it in-house to answer a question that kept coming up on client work: can AI answer engines actually read this site? Turned out useful enough to let out of the kitchen.
Paste a URL → 0–100 score in ~20 seconds → a category breakdown across security, SEO, AEO/content, structured data, AI-crawler access and links, with live Core Web Vitals on top. It's the PageSpeed model — clear findings, not just copy-paste fixes.
Stack: Astro + Cloudflare Workers, ~35 scored checks off a single honest fetch (we only read what the bots read). Handy for auditing your own projects before a handoff.
What if anyone on your team could ask your data a question and get a live dashboard back in under a minute?
That was the brief. Business users were locked out of their own data. Every question waited on someone who could write SQL, data sat across disconnected systems, and security teams refused to send sensitive records to third-party AI tools.
So we flipped the model. Instead of moving enterprise data to an AI product, we moved the AI analytics product into the customer's AWS account.
What we built:
• Natural-language querying that turns plain-English questions into governed dashboards in under 60 seconds
• Federated queries across SQL and NoSQL sources through Trino
• AI query generation on AWS Bedrock Agents, with Bedrock Guardrails keeping model output in bounds
• Dashboards generated with Apache Superset, plus proactive anomaly and trend alerts
• A white-label React widget that embeds in any product
• Role-based access, row-level security and enterprise SSO enforced at every layer
The result:
Zero data egress. The whole platform deploys inside the customer's VPC and is live on AWS Marketplace.
Building AI features for a data-heavy or regulated product? Let's talk about doing it without your data leaving your cloud.
Automating pull requests and navigating millions of lines of code shouldn't feel like a black box. With Mainline, we designed an intuitive dark-mode landing page built to prove reliability through transparent metrics, step-by-step agent workflows, and seamless dev-stack integration. ⚡️💻
Here is a look at what we staged across the 4 frames:
🖥️ 5K Hero View: Sleek display framing the AI pull request engine alongside live code-execution windows and instant CTA pathways.
🛠️ Autonomous Process Breakdown: Step-by-step containers detailing codebase indexing, multi-file edits, and automated terminal command execution.
📈 Verified Performance Metrics: Clean benchmark grid showcasing a 77.2% SWE-bench resolution rate and 40-minute average time savings per task.
🔗 Tech Stack Compatibility: Interactive tool matrix demonstrating effortless integration with VS Code, GitHub, Linear, Slack, and any MCP server.
Swipe through the frames and let us know which visual component stands out to you most! Feedback and support are greatly appreciated. ❤️👇