Airtable AI

Airtable AI AI

Airtable AI AI

Airtable AI AI

Airtable AI AI

Airtable AI AI

Airtable AI AI

Airtable AI AI

Airtable AI AI

Airtable AI AI

Airtable AI AI

Claude for Small Business launched this week with 8 integrations. Most SMBs use 20+. What does that mean for the rest of the stack?

Anthropic launched Claude for Small Business on Tuesday. The package includes 15 prebuilt agentic workflows and 8 named integrations: Intuit QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, Microsoft 365, and Slack. The workflows handle things like invoice chasing, payroll planning, month-end close, sales campaigns, contract routing, and cash-flow forecasting. Owners approve before anything sends or pays. The basic facts are not in dispute. What's interesting is the math. Most small businesses use more than 8 tools. The common ones not on that list: Shopify, Stripe, Square, Klaviyo, Mailchimp, ActiveCampaign, ConvertKit, Pipedrive, GoHighLevel, Calendly, Notion, Airtable, ClickUp, Webflow, Zapier. Then vertical-specific tools: ServiceTitan, Jobber, Housecall Pro for trades. Kajabi, Teachable, Circle for creators. Toast, Resy, OpenTable for restaurants. Etsy, Faire, Printify for makers. Real question worth asking: how much of a typical small business stack does the 8-tool package actually cover, and which kinds of businesses are well-served versus left out? A rough walk through some common archetypes: Office-based service business (consultants, accountants, agencies, B2B services). Coverage is decent. Most are on Google Workspace or Microsoft 365, run finance through QuickBooks, communicate via Slack, and many use HubSpot. The 8 tools probably hit most of the core stack for this group. E-commerce or DTC brand. Coverage is thin. Shopify isn't there. Stripe isn't there. Klaviyo isn't there. The actual revenue stack of an online store is mostly outside the covered set. Local trades (HVAC, plumbing, insulation, electrical, landscaping). Coverage is essentially absent. The operating systems for these businesses are ServiceTitan, Jobber, Housecall Pro, Square for payments, sometimes QuickBooks for accounting on the back end. The customer-facing and operational tools are not on the list. Creators, coaches, course sellers. Coverage is absent. Kajabi, ConvertKit, Teachable, Circle, Substack. None of it is in the package. Restaurants and hospitality. Coverage is absent. Toast, Square POS, Resy, OpenTable, Toast Payroll. The actual operating systems are not on the list. A few patterns emerge from that walk. First, the package targets a specific kind of small business. Office-based, white-collar, finance running through QuickBooks, meetings on Google or Microsoft, sales through HubSpot. That is a real segment. Anthropic chose it deliberately and the workflows make sense for that profile. Second, for everyone else, the prebuilt workflows mostly don't touch the tools they actually use day to day. The choice isn't "use Claude for Small Business or not." It's "AI in my operations, yes, but via custom work outside this package." That's not a complaint about the launch. Building 8 polished integrations is hard and Anthropic had to pick. It's more an observation that "Claude for Small Business" as a category name covers a wider universe than what the package actually addresses on day one. Curious how this lines up with what people are actually running. If you operate a small business, how many of the 8 covered tools are in your stack? And what's NOT on that list that you'd most want connected to an AI agent? submitted by /u/KolioMandrata [link] [comments]

Airtable: “Sorry, we can’t expose this API endpoint” 14 mcp tools seem to be enough... Me with my Claude MAX: cool, just reverse‑engineered their internal API, rebuilt it as MCP with GUI +60 Tools, now Claude controls everything and 5k users are happy lol... [Experiment - Educational Purposes]

With this MCP, you can let Claude Code or 15+ IDEs with AI build or edit your own database application in Airtable. Note: This is an add-on to the official MCP. It unlocks more features and does not replace the official one. If you need any missing tools, just ask for them in the Issues section. Safety: - - Everything runs inside your browser/device - Request has come out from your device same as normal airtable client if you want video showcase: Using Claude Code to manage base views, computed fields (Formulas), and extensions (free + open source, 2000+ users already using it) : r/Airtable submitted by /u/Aggravating_Bad4639 [link] [comments]

Built an AI quoting system on Claude cowork, now stuck on the boring part: how do teammates stay in sync?

Just shipped a quoting system at work and I'm pretty hyped, what used to take me an hour (digging through price lists, formatting, double-checking) now takes about 5 minutes with AI doing the heavy lifting. But I've hit a new problem and I'm not sure what the "right" pattern is. The whole thing runs off a folder. I open it in Claude cowork and it just works. Inside that folder lives the price list, which gets updated regularly. The ideal flow is: when I update prices, my teammates' copies also reflect the latest version — otherwise someone's going to send a quote based on stale numbers. So my question: how are you handling this kind of sync today? Just dump it in Google Drive / Dropbox and hope for the best? Git? (Feels overkill for non-devs, but maybe?) Some shared DB / Notion / Airtable as the source of truth, and the folder pulls from it? Something else I'm not thinking of? Curious what's actually working for small teams in production, not just what sounds clean on paper. submitted by /u/ActualBrilliant7494 [link] [comments]

Using AI to untangle 10,000 property titles in Latam, sharing our approach and wanting feedback

Hey. Long post, sorry in advance (Yes, I used an AI tool to help me craft this post in order to have it laid in a better way). So, I've been working on a real estate company that has just inherited a huge mess from another real state company that went bankrupt. So I've been helping them for the past few months to figure out a plan and finally have something that kind of feels solid. Sharing here because I'd genuinely like feedback before we go deep into the build. Context A Brazilian real estate company accumulated ~10,000 property titles across 10+ municipalities over decades, they developed a bunch of subdivisions over the years and kept absorbing other real estate companies along the way, each bringing their own land portfolios with them. Half under one legal entity, half under a related one. Nobody really knows what they have, the company was founded in the 60s. Decades of poor management left behind: Hundreds of unregistered "drawer contracts" (informal sales never filed with the registry) Duplicate sales of the same properties Buyers claiming they paid off their lots through third parties, with no receipts from the company itself Fraudulent contracts and forged powers of attorney Irregular occupations and invasions ~500 active lawsuits (adverse possession claims, compulsory adjudication, evictions, duplicate sale disputes, 2 class action suits) Fragmented tax debt across multiple municipalities A large chunk of the physical document archive is currently held by police as part of an old investigation due to old owners practices The company has tried to organize this before. It hasn't worked. The goal now is to get a real consolidated picture in 30-60 days. Team is 6 lawyers + 3 operators. What we decided to do (and why) First instinct was to build the whole infrastructure upfront, database, automation, the works. We pushed back on that because we don't actually know the shape of the problem yet. Building a pipeline before you understand your data is how you end up rebuilding it three times, right? So with the help of Claude we build a plan that is the following, split it in some steps: Build robust information aggregator (does it make sense or are we overcomplicating it?) Step 1 - Physical scanning (should already be done on the insights phase) Documents will be partially organized by municipality already. We have a document scanner with ADF (automatic document feeder). Plan is to scan in batches by municipality, naming files with a simple convention: [municipality]_[document-type]_[sequence] Step 2 - OCR Run OCR through Google Document AI, Mistral OCR 3, AWS Textract or some other tool that makes more sense. Question: Has anyone run any tool specifically on degraded Latin American registry documents? Step 3 - Discovery (before building infrastructure) This is the decision we're most uncertain about. Instead of jumping straight to database setup, we're planning to feed the OCR output directly into AI tools with large context windows and ask open-ended questions first: Gemini 3.1 Pro (in NotebookLM or other interface) for broad batch analysis: "which lots appear linked to more than one buyer?", "flag contracts with incoherent dates", "identify clusters of suspicious names or activity", "help us see problems and solutions for what we arent seeing" Claude Projects in parallel for same as above Anything else? Step 4 - Data cleaning and standardization Before anything goes into a database, the raw extracted data needs normalization: Municipality names written 10 different ways ("B. Vista", "Bela Vista de GO", "Bela V. Goiás") -> canonical form CPFs (Brazilian personal ID number) with and without punctuation -> standardized format Lot status described inconsistently -> fixed enum categories Buyer names with spelling variations -> fuzzy matched to single entity Tools: Python + rapidfuzz for fuzzy matching, Claude API for normalizing free-text fields into categories. Question: At 10,000 records with decades of inconsistency, is fuzzy matching + LLM normalization sufficient or do we need a more rigorous entity resolution approach (e.g. Dedupe.io)? Step 5 - Database Stack chosen: Supabase (PostgreSQL + pgvector) with NocoDB on top Three options were evaluated: Airtable - easiest to start, but data stored on US servers (LGPD concern for CPFs and legal documents), limited API flexibility, per-seat pricing NocoDB alone - open source, self-hostable, free, but needs server maintenance overhead Supabase - full PostgreSQL + authentication + API + pgvector in one place, $25/month flat, developer-first We chose Supabase as the backend because pgvector is essential for the RAG layer (Step 7) and we didn't want to manage two separate databases. NocoDB sits on top as the visual interface for lawyers and data entry operators who need spreadsheet-like interaction without writing SQL. Each lot becomes a single entity (primary key) with relational links to: contracts, bu

How to extract structured laptop specs from messy descriptions?

Hey everyone, The following post is generated by AI so it's well structured to avoid any misunderstanding since English is not my native language nor I have any coding experience. I simply want to know if there is a way to extract some tech specifications for about 800 laptops which I have their SKUs on a certain website, I need a tool that can search for the SKU and can read and understand the context of the descriptions attached to extract that specific data. I have tried a Python code generated by Chat GPT, but only provided me about 60-70% accurate results only across small size batch which contain of 10 laptop, fail miserably when I asked for 50 or 100. Here is the AI summary of what I have tried: I’m working on a real-world data problem and could really use some guidance on the right approach. I have ~800 laptop SKUs in Airtable. For each SKU, I can access a product page (same SKU), but the descriptions are very inconsistent: - Different formats for the same specs (e.g. “2x USB-C”, “USB Type-C (x1)”, etc.) - Duplicated info in multiple sections - Some specs missing entirely - Mixed sources (manual entry, copied from retailers, AI-generated) My goal is to extract structured fields like: - Number of USB-A / USB-C ports - HDMI (yes/no) - Screen size - Key features (summarized) --- What I’ve tried so far: - Python (regex/parsing) → breaks بسبب inconsistency - LLMs (ChatGPT / Claude) → ~60–70% accuracy - Common issue: double counting (e.g. ports mentioned twice → wrong totals) - Batch processing PDFs (worked well only when data was structured, like Lenovo datasheets) --- What I’m trying now: I’m experimenting with a more structured LLM pipeline: Clean/normalize description Extract mentions (not final values) Resolve conflicts / deduplicate Validate (e.g. reject unrealistic values) Also considering using Claude Skills to standardize this workflow. --- My constraints: - Very limited coding experience (relying mostly on AI-assisted workflows) - Need something practical within a few days (not a long-term ML project) --- My questions: Is this the right approach (multi-step LLM pipeline), or am I overcomplicating it? Is there a better way to handle inconsistent product data like this? Would you recommend: - sticking with LLMs + validation? - using scraping + structured sources (if available)? - or something else entirely? Any direction, tools, or patterns would be really appreciated. Thanks a lot 🙏 submitted by /u/doteonchelsea [link] [comments]

Practicality Question

I'm a direct-to-seller RE investor and I designed an AI system to manage my CRM automatically. Looking for a sanity check. It's called "the Watcher." It monitors all inbound lead replies (SMS, email, call transcripts) and handles CRM updates without any human input. Lead texts back, the system classifies the response using Claude API (18 categories), then updates status, tags, drips, and notes in GoHighLevel automatically. Hot leads get pinged to my closer via Slack. Closer replies YES/BUSY right there. Stack: Airtable (automation + database + dashboard) + Claude API + GHL + Slack. Volume: 3,000 leads, 100-300 responses/day. Not all trigger changes. About 60-70% result in actual CRM updates, the rest just get logged. Three questions: Does this architecture make sense at this volume or does something off the shelf already do this? How often does this kind of webhook + API chain actually break in production? I used Claude (the chat product) to write the entire technical spec. Module by module, classification matrix, JSON schemas, API structures, 22 drip campaigns, everything. Handed it to a dev on Upwork and he said it was the most detailed spec he'd ever gotten from a client. Anyone else used Claude to produce real dev-ready docs? Did it hold up when someone actually built from it? submitted by /u/Gold_Golf_6037 [link] [comments]