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
AI hallucinations explained
Model names refreshed to the September 2026 lineup. The benchmark figures deliberately keep the model they were measured on, because those tests have not been re-run on the newer models; they are now dated so the age is visible.
AI translation services compared
Cost rows updated to Claude Sonnet 5 ($2/$10) and GPT-5.6 Terra ($2/$12), replacing Sonnet 4.6 and the deprecated GPT-4o. The Sonnet per-character estimate only fell to ~$3.50 rather than by the full price cut, because Claude 5's tokenizer produces roughly 30% more tokens for the same text.
AI writing tools compared
Verified against claude.com/pricing. Corrected the Claude Team price — it is $20/seat annually, not $25 — and added the Team Premium tier at $100/seat. Updated the model-under-the-hood row from Sonnet 4.6 / Opus 4.7 to Sonnet 5 / Opus 5, noting that Opus requires a paid plan. Jasper, Copy.ai and Writer pricing was not re-verified this round.
Auto-categorize support tickets by topic and urgency
Model names in the inference-cost row updated to Claude Sonnet 5 and GPT-5.6 Terra. GPT-4o was named here and is deprecated, superseded by GPT-5.5. The cost range is unchanged. Added referenced-model links for models this page already names: Claude Sonnet 5, GPT-5.6 Terra.
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.
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.
Draft customer support replies that hold up to scrutiny
**AI-assisted support reply drafting** means an AI model produces the first draft of each support response, grounded in your knowledge base, while a human agent reviews and sends. The workflow that keeps drafts in company voice, escalates the right tickets to humans, and stops hallucinated facts about your product from going out — including the confidence thresholds, the prompt shape, and the audit loop that keeps it honest.
Meeting summaries people actually read
If your team runs enough meetings that "remember what we agreed" has become a real cost, this is the workable system for turning recordings into short, scannable summaries with real action items. The tool choice, the prompt that matters, and the distribution step most teams skip.
Reply templates learning from past conversations
If your support team has the same five questions arriving every week, this is the suggestion system that learns from the conversations you've already had — so similar inbound questions get answered with the same care without anyone re-typing the answer for the 50th time. Retrieval based on embeddings (the way AI represents meaning as numbers), context-aware suggestion, and a feedback loop that keeps the suggestions current.
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.
AI translation services compared
Five AI translation services and the human translator on retainer. What each is good for, what each costs per million characters, and the kind of content where AI translation will quietly betray you in ways a monolingual editor can't catch.
Generate alt text and image descriptions at scale
**Alt text** is a short written description of an image — read aloud by screen readers, shown when an image fails to load, and required for accessibility compliance. This is a practical pipeline for generating it across hundreds or thousands of images, with the prompt that produces good descriptions, the editorial pass that keeps them honest, and the empty-alt rule most pipelines get wrong.
Generate FAQ content from existing docs
A workflow for building an **FAQ** — frequently asked questions — that reflects what your users actually ask, not what a marketer guessed at three years ago. AI clusters real questions from your support tickets, KB articles, and chat logs, then writes the answers from your existing docs. Includes the clustering pass, the grounding rule, and the monthly refresh that keeps it useful.
Hook generation for short-form video
A **hook** is the opening line of a short-form video — the first three seconds that decide whether the viewer keeps watching or scrolls on. This piece is the workflow that generates 30 hook variants for one topic, scores them against the patterns that survive the algorithm's scroll-stop test, A/B tests the survivors, and builds a library of winners. Includes the patterns that consistently work, the ones that don't, and why "Hey guys, today I'm going to talk about…" is the most expensive opener on the platform.
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.
Auto-categorize support tickets by topic and urgency
A workflow that tags inbound tickets reliably enough to route them automatically — combining classical machine learning (cheap, fast, explainable) and LLMs (flexible but expensive). With confidence thresholds, a human-review queue for the ambiguous cases, and the evaluation loop that keeps the system honest as your product evolves.
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.
Contract review and clause extraction
**Clause extraction** means pulling out the parts of a contract that matter — termination, liability, IP, renewal, governing law — and comparing each one to the terms your company normally accepts. Not legal advice, not legal review. A first-pass triage so your lawyer spends an hour on the contract that needs an hour, not an hour reading every routine NDA.
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 writing tools compared
Five AI writing platforms compared — Jasper, Copy.ai, Writer, ChatGPT Team, and Claude Team. What each is genuinely good at, what each costs per seat, and the honest case for skipping the wrapper tools entirely and using raw GPT or Claude with a shared prompt library.
ChatGPT Team vs Claude Pro vs Microsoft Copilot for small business
Three flagship AI subscriptions that look similar on the marketing pages and actually serve different teams well. Where ChatGPT Team's broader ecosystem pays off, where Claude's long-context work (handling book-length documents in one prompt) and stronger coding shines, and where Microsoft 365 Copilot's deep Office integration earns its price.
Cursor vs Copilot vs Claude Code for coding assistance
An **AI coding assistant** is a tool that helps developers write, edit, and debug code by suggesting lines, refactoring files, or completing whole tasks on its own. This is a side-by-side of the three most teams are choosing between — what each is genuinely best at, where each falls down, and the hybrid setups working developers actually use.
Embeddings models compared for semantic search
An **embedding** is the way AI represents the meaning of a piece of text as a list of numbers, so similar things can be compared mathematically. An **embedding model** is the model that produces those numbers. This page compares five model lines — OpenAI text-embedding-3, Cohere embed v3, Voyage AI, Jina, and the open-source sentence-transformers family — on retrieval quality, dimension-and-cost trade-offs, and how to test them on your actual data before committing.
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 hallucinations explained
Why LLMs — the technology behind ChatGPT, Claude, and Gemini — confidently produce wrong answers, how often it happens in 2026 (the honest answer is "more than vendors imply"), and the four mitigations that actually move the needle in production systems.
Embeddings explained without math
How AI systems turn text into positions on an invisible map of meaning — what they're used for, when they fail, and what you actually need to know to make decisions about them.
Open-source vs proprietary AI — practical tradeoffs
If you're choosing AI infrastructure for a workload that actually matters, the "open vs closed" debate is the wrong frame. The real questions are capability, control, cost at scale, and vendor risk. For most teams the right answer is a hybrid — each model serves the workload it's best at. Where each side wins, where each side loses, and the decision framework that beats religious-war framing.
Tokens, context windows, and what they cost
The bill of materials for any AI feature — what a token is, what a context window does, how the input-vs-output pricing works, and the worked examples that turn vendor pricing pages into a sensible budget.