Blog
Things we've shipped — and how.
Practical AI engineering. Architecture, guardrails, eval loops, and the production gotchas no one writes about. Filter by tag below.
- 10 min readrag · chatbots
RAG Chatbot ROI for Mid-Market Media Brands: Cost vs Time Savings
Calculate RAG chatbot ROI for mid-market media brands. Detailed cost breakdowns, token economics, and content ops time savings across editorial workflows.
Read → - 11 min readmanufacturing · agents
Automated Quality Control with Computer Vision and AI Agents: ERP Integration Cost Breakdown
Break down the true computer vision quality control cost, including hardware, multi-modal inference, agent orchestration, and enterprise ERP integration.
Read → - 9 min readn8n · automation
n8n Self-Hosted vs Cloud for Media Pipelines: Cost Breakdown
An engineer's guide to n8n self-hosted vs cloud cost dynamics for media automation pipelines handling high execution volume and large file processing.
Read → - 9 min readchatbots · evals
D2C AI Chatbot Resolution Evals: A CFO Playbook for Reducing Hallucinations and Escalation Costs
Learn how D2C brands use automated resolution evals to eliminate LLM hallucinations, lower support ticket costs, and improve AI customer service accuracy.
Read → - 7 min readrag · agents
Media Engineering RAG Readiness Checklist: 6 Gates Before Launch
Ensure your media RAG knowledge chatbot architecture is production-ready. 6 essential gates covering ingestion, chunking, security, and access control.
Read → - 9 min readops · agents
Mid-Market SaaS Ops Readiness Checklist: 8 Gateways Before Deploying Autonomous Workflow Agents
Evaluate your architecture against these 8 operational gateways before granting autonomous AI agents write access to production databases.
Read → - 7 min readchatbots · enterprise
Enterprise Real Estate AI Assistants: Custom RAG Architecture vs Off the Shelf Property Management Bots
Evaluate custom RAG architectures against off-the-shelf property management bots for enterprise real estate ops, document parsing, and leasing security.
Read → - 11 min readecommerce · automation
AI Merchandising on Shopify: Collections, Bundles, and Product Sorting That Update Themselves
Most Shopify stores are merchandised once and then left alone. Collections built at launch still show last season's stock, bestsellers sit on page three, bundles never change, and sold-out products lead the grid. Keeping all of that fresh by hand doesn't scale past a few dozen products. AI merchandising lets collections, bundles, and product sorting update themselves from your real sales, stock, and customer behaviour, while your team keeps control of the brand and the margin. Here's what Shopify already does natively, what AI adds on top, where automated merchandising goes wrong, the guardrails that keep it on brand, and how to measure the result.
Read → - 12 min readagents · ecommerce
WhatsApp AI Agents for Indian and Gulf Businesses: Orders, Support, and Payments in One Chat
In India and across the Gulf, customers don't want to fill in your contact form or wait for an email. They want to message you on WhatsApp, the app already open on their phone. An AI agent on WhatsApp can answer them in seconds, share the catalogue, take the order, send the payment link, and track delivery, at any hour and in their own language. But WhatsApp isn't a website chat widget. It has its own rules on when you can message, what you pay per message, and what kind of AI is allowed at all. Here's what a WhatsApp AI agent handles well, the platform rules you have to design around, where these agents go wrong, and how to measure whether yours is paying off.
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What we write about, and what we don't
Everything here comes out of engagement work rather than a content calendar, which shapes both the subjects and the tone.
- Numbers with baselines attached
- Where a post quotes a result, the before and after are both there. A percentage improvement with no starting point is unfalsifiable, and the AI industry currently runs on a great deal of it.
- The parts that were harder than expected
- Posts about builds include what went wrong — the retrieval that worked on curated documents and fell over on the real knowledge base, the eval case nobody thought to write. Those details are the reusable part; the happy path rarely is.
- Written for the person who has to fund it
- Most of these sit between a purely technical write-up and a business case, because that's where the decisions actually get made. Enough architecture to judge whether the approach is sound, enough cost and timeline to judge whether it's worth doing.
- No model-release commentary
- We don't publish reactions to every model launch. That writing ages badly and it's not what we know better than anyone else — what we know is what happens when these systems meet a real business process for six months.
