ChatGPT
OpenAI
The general-purpose assistant most teams start with. Strong all-rounder for drafting, analysis, and everyday questions.
Assistants
A working map of the tools we actually reach for, sorted by what they're for. We're not affiliated with any of them, and the right one always depends on your situation, but this is a solid place to get your bearings.
The general-purpose assistant most teams start with. Strong all-rounder for drafting, analysis, and everyday questions.
A capable assistant that holds up well on long documents and careful reasoning. Our default for writing-heavy work.
Google’s assistant, tightly tied into Workspace. Handy if your company already lives in Gmail and Docs.
AI woven into Office. Useful inside Word, Excel, and Teams if you’re a Microsoft shop.
A coding agent that works across a whole codebase from the terminal. What we use to build a lot of client software.
An AI-first code editor. Good for developers who want completions and edits without leaving their editor.
Autocomplete-style help inside your existing editor. A low-friction first step for engineering teams.
Turns prompts into working front-end UI. Fast way to get a real first draft of an interface.
Open-source workflow automation you can self-host. Our pick when you want to own the wiring and keep data in-house.
Connects the apps you already use with no code. Great for simple, reliable automations across SaaS tools.
Workflow automation built into the Microsoft stack. Sensible if your processes already run through 365.
AI inside the docs and wiki your team already keeps. Useful for summarizing and drafting in context.
Editing and tone help everywhere you type. Low-risk way to raise the floor on written communication.
Generates presentations and docs from a prompt. Saves real time on first-draft decks.
Answers questions with live sources and citations. Our go-to for quick, checkable research.
Grounds answers in documents you upload, with citations back to the source. Good for working over your own material.
High-quality image generation with a distinct look. Strong for concept art and marketing visuals.
Natural text-to-speech and voice cloning. The standard for AI narration and voice features.
Open speech-to-text that runs locally or in your stack. Solid, private transcription you can build on.
Microsoft’s cloud for running and governing AI, including Azure OpenAI. A common landing spot for enterprises that need compliance and control.
The workhorse relational database. With the pgvector extension it doubles as a vector store, so AI retrieval lives in the database you already run.
A graph database for modeling how things connect. Increasingly the backbone for knowledge-graph and GraphRAG approaches to retrieval.
Managed vector database for search and retrieval. Common backbone for AI that answers from your own data.
High-throughput serving engine for open models. What you reach for when you’re self-hosting LLMs and need them fast.
One consistent API in front of every model provider. Makes it easy to swap models and avoid lock-in.
The hub for open models and datasets. Where a lot of custom AI work starts.
Run open models locally on your own hardware. Our pick when data can’t leave the building.
A framework for stitching models, data, and tools into applications. Useful scaffolding for custom builds.
Picking tools is the easy part. Knowing which ones earn their place in your workflow, and which to skip, is where we come in.
