Practical AI solutions for any business.
A maintained reference for getting real work done with AI — what to use, what it costs, what it actually does, and where it falls down. For founders, ops leads, support and content teams, builders, and anyone with a specific AI question. No fluff, no hype, no industry assumed, technical background not required.
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What an LLM actually does for a business
What large language models — the technology behind ChatGPT, Claude, and Gemini — really are, what they're genuinely useful for, and where they fail. In plain language, with no jargon, no hype, and no acronyms left undefined.
RAG explained without acronyms
If you're an operator or founder considering a "chat with our docs" tool, this is what retrieval-augmented generation actually is, when you genuinely need it, and what it costs to set up. Written for the person making the decision — not the engineer wiring it up.
When AI is the wrong tool
A clear-eyed catalogue of the categories where AI underperforms simpler approaches — what to use instead, and why most "AI failure" stories are actually "AI used for the wrong thing" stories.
Build a private knowledge base your team can search
A practical setup for "chat with our docs" — a system that lets your team ask plain-language questions across your internal documents and get grounded answers with citations. The framework choice, the cheap-vs-managed vector database call (a database that stores meaning-as-numbers so you can search by similarity), and the hybrid-search-plus-rerank pattern the field has converged on. With cost ranges and the parts that bite teams in production.
What changed in the last 30 days
Ad creative A/B testing at scale
Initial publication.
AI agents for inbound qualification
Initial publication.
AI coding tools for non-engineers
Initial publication.
AI meeting assistants compared (Otter, Fireflies, Granola, Read AI)
Initial publication.
Replies, meetings, and customer conversations
For support teams, sales teams, founders, and anyone whose day is shaped by inbound messages, calls, and meetings. AI that helps your team respond faster without sounding like AI.
AI agents for inbound qualification
A chat-based AI assistant that pre-qualifies inbound leads — capturing role, company size, use case, and timing — before they reach a human sales rep. Without the friction of a 12-field form, and with the structured-capture discipline that stops the agent from inventing qualifying details the prospect never gave.
Auto-tag and route inbound social DMs
A pipeline that watches your inbound DMs across LinkedIn, Twitter / X, Instagram, and TikTok — classifies each one by intent, routes the support cases to support and the press queries to press, and surfaces the partnership and exec asks to whoever should actually see them. So the founder stops reading every DM at 11pm trying to figure out which ones matter.
Detect churn signal from support patterns
A pipeline that reads your support tickets continuously and surfaces the customers whose ticket pattern — frequency, tone, topic, escalation rate — predicts churn around 60 days out. Not "who's complaining today"; who is exhibiting the multi-month pattern that historically precedes cancellation.
Investor updates from BI data
If you're a founder past seed stage and your monthly investor update keeps slipping, this is the pipeline that generates the draft from your BI dashboards rather than from a Sunday-night writing session. It pulls metrics, drafts the narrative, surfaces the right context per investor segment, and lands in your inbox Monday morning for editing.
Writing, headlines, social, and translations
For marketers, content teams, editors, and anyone producing words at any volume. AI assistance that doesn't strip the voice out of your writing.
Ad creative A/B testing at scale
A workflow that generates twenty variants of each ad, tests them programmatically against the ad platforms, and lets performance data pick the winners. With the variant diversity that keeps the test results meaningful, instead of twenty rephrased versions of the same idea.
Brand-voice guardrails for marketing teams
A system that enforces your brand-voice rules across every piece of AI-generated content — captions, ads, social posts, emails, product copy — so the marketing team scales output without producing the "everyone sounds like ChatGPT" homogeneity that's now common at growing companies. The voice spec, the guardrail layer, and the monthly audit that catches drift.
Competitor monitoring with automated alerts
A pipeline that watches your competitors continuously — pricing pages, product launches, hiring posts, marketing campaigns, social tone — and pings the right team when something material changes. So you stop discovering your top competitor's new pricing tier three months after their existing customers started asking your sales reps about it.
Content performance attribution
Content performance attribution is the practice of tracing which blog posts, whitepapers, and videos actually drive pipeline and deals — rather than judging content by page views. A pipeline that joins content engagement to CRM events, gives content fair credit across the customer journey, and tells you which categories are worth more investment.
Documents, data, and internal knowledge
For ops teams, internal tools owners, and anyone who needs to extract, organize, or search information that lives across files, archives, or systems.
Audit-trail generation from system logs
A pipeline that turns raw system logs (auth events, access records, change history) into the audit narrative your compliance team can read — not a 50,000-line CSV nobody opens. AI summarisation, anomaly detection, and the workflow that satisfies SOC 2 / ISO 27001 / HIPAA auditors without an eight-week prep cycle.
Auto-generate documentation from PRs and code
A continuous-integration pipeline that keeps your developer docs in sync with the codebase — drafting doc updates the moment a pull request (PR) merges, routing them to the docs owner for review, and stopping the gap between code and docs from growing into the year-long backlog you can't recover from.
Automated invoice and receipt processing
Get invoice and receipt data — vendor, amount, line items, dates, tax — out of PDFs and into your accounting system automatically, without someone keying numbers in by hand at 11pm. The approach that actually works on real vendor invoices, the checks that catch silent mistakes, and the human-review queue that handles the tricky cases.
Compliance evidence collection for SOC 2 / ISO 27001
A pipeline that pulls the screenshots, configurations, access logs, and policy snippets your auditor wants — automatically, on a schedule, organised by control. Turns "SOC 2 evidence collection week" into a continuous background process, and makes the auditor's questions take minutes instead of days.
Pick the right tool for the job
Side-by-side evaluations and decision frameworks. Not "which is best" — "which is best for what." Updated when the answers change.
AI coding tools for non-engineers
For founders, ops leads, marketers, and analysts who need to ship code without a computer-science degree. Which AI tools are safe to learn on, which produce code you can hand off later, and where the line sits between "AI helped me build this" and "AI built something that will haunt me."
AI meeting assistants compared (Otter, Fireflies, Granola, Read AI)
Five AI meeting tools that record your calls, transcribe them, and produce a summary you can paste into a follow-up — and that diverge sharply on output quality, search, integrations, and what happens to your recordings after. Where Otter, Fireflies, Granola, Read AI, and Fathom each fit, and the decision rules per team type.
AI search APIs compared (Perplexity, Tavily, SerpAPI + LLM)
Five APIs (application programming interfaces — the way one piece of software calls another) that give an AI workflow real-time web search. Perplexity, Tavily, SerpAPI plus your own model, Brave, and Exa each take a different approach. Where each fits, the latency and accuracy trade-offs, and the integration cost most pages don't show.
AI video editing tools compared (Descript, Captions, Opus Clip)
Four AI-augmented video tools that solve different parts of the production pipeline. Where Descript's text-based editing earns its place, where Captions wins at vertical-format social, where Opus Clip dominates the long-form-to-short-form workflow, and how Adobe Premiere's AI features fit traditional editors.
Plain-language explainers
For readers without a technical background trying to understand what AI is doing under the hood. No code, no jargon, no acronym soup.
AI procurement checklist for non-technical buyers
A 20-question checklist for evaluating an AI vendor before you sign — the questions that actually predict deployment success, the red flags that should kill a deal, and the contract terms that matter more than the demo. In plain language a non-technical buyer can run without a CTO in every meeting.
AI risk assessment for legal and compliance teams
A framework for evaluating AI deployments against the legal and compliance surface that actually applies to your business — data privacy, regulatory exposure, IP and copyright, bias and discrimination, contractual obligations. Produces operational "approve with these controls" decisions instead of the over-broad "AI is risky everywhere" framing.
AI security risks for businesses
The security risks specific to AI deployments — prompt injection, model exfiltration, training-data poisoning, supply-chain attacks on AI components, employees using consumer AI for confidential work — that the standard security playbook doesn't cover. What's actually exploitable today, what's theoretical, and which controls matter for which business shape.
Vendor lock-in risks with AI
Every AI product you wire into your operation creates a small dependency on the vendor; the cumulative dependency over 18 months becomes a strategic exposure. Where lock-in lives in modern AI stacks, the realistic mitigation patterns, and which lock-in is acceptable vs unacceptable for which workloads.