Custom GPTs & Claude Skills.
Branded AI assistants, deployed where your team works. Trained on your content, your voice, your playbooks. And safe enough to ship.
Generic ChatGPT dies in week two.
Teams adopt generic ChatGPT or Claude and hit the same walls: hallucinated answers about their own products, wrong brand voice, no access to internal docs, no audit trail, no version control.
"Use ChatGPT" is not a strategy. It's a suggestion that dies in week two. The tool exits the workflow because nobody trusts what it says about the company.
A branded AI assistant. A Custom GPT or a Claude Skill. Closes that gap. It knows your product, your docs, your voice, and your refusals. It ships with an owner, a repo, and a feedback loop.
We build both ecosystems. And help you pick.
We build both Custom GPTs (OpenAI ecosystem) and Claude Skills (Anthropic ecosystem). Which one we recommend depends on where your team already works, what tools need integration, and what the assistant actually has to do. Here's the decision framework:
| Custom GPT | Claude Skill | |
|---|---|---|
| Best for | Public-facing assistants · GPT Store distribution · ChatGPT-native teams | Internal ops · Claude Code integration · Code-heavy workflows |
| Deployment | ChatGPT, OpenAI API, Apps SDK | Claude desktop / web, Claude Code, API |
| Customization | Instructions + knowledge files + Actions | Full Python/Node code, filesystem access, MCP servers |
| Best memory/context | Knowledge files, tools via Actions | RAG, custom code, long-horizon workflows |
| Price sensitivity | Lower per-query cost for simple Q&A | Higher capability ceiling, Claude pricing |
How we build them:
- Identify high-impact use cases across support, onboarding, content, and sales
- Ingest your internal documentation, product data, and brand voice examples
- Write the system prompts, tone controls, refusals, and safety fallbacks
- Deploy across the surfaces where your team already works (Slack, Notion, Claude Code, web widget, help desk)
- Monitor and iterate: edge cases, feedback loops, version control on prompts and Skills
What you get.
- Trained Custom GPT or Claude Skill (or both, for different use cases)
- Deployment across chosen platforms. Slack, Notion, Claude Code, web widget, help desk
- Admin controls and QA checklists for ongoing quality
- Versioned prompt and Skill repository you own
- Optional usage dashboard with per-query analytics
- 2-hour team training on operation, iteration, and refusal tuning
The stack we deploy.
Named tools, not vague capabilities.
Who this is for.
Teams with a clear internal use case. Sales enablement, customer support, content drafting, onboarding, or product search. You have at least some documentation worth training on, or you're willing to build it. If you do not have an owner on your side who can review outputs and flag failures, hold off until you do.
Frequently asked questions.
What's the difference between a Custom GPT and a Claude Skill?
A Custom GPT is a configurable instance of ChatGPT with system instructions, knowledge files, and optional Actions. Best for public-facing assistants and ChatGPT-native teams.
A Claude Skill is a code-backed plugin that runs Python or Node, reads the filesystem, and talks to MCP servers. Best for internal ops and code-heavy workflows. See the comparison table above for the full decision framework.
Which should I choose?
It depends on where the assistant has to live and what it has to do. If your team is in ChatGPT and the use case is Q&A over knowledge files, a Custom GPT ships fastest.
If the assistant needs filesystem access, code execution, or lives inside Claude Code, a Claude Skill is the right call. We often recommend one of each for different use cases.
Can you deploy it in Slack, Notion, or our website?
Yes. Slack: we deploy via the Slack API or an embed bot. Notion: via the Notion API plus a command surface. Website: via a web widget, a Claude-powered chat endpoint, or an OpenAI Assistants API integration.
Help desks. Zendesk, Intercom, Front. All have established integration patterns we've shipped before.
How is data privacy handled?
We use paid API tiers on both OpenAI and Anthropic. Data is not used for model training. For sensitive data, we run on-premise RAG with pgvector or Pinecone and route through MCP servers you host.
We support Single Sign-On (SSO) where available and never upload confidential material to free ChatGPT.
Do we own the IP?
Yes. Prompts, Skills, knowledge files, and any supporting code ship in a repo you own. You can fork it, extend it, or walk away with it. No vendor lock-in.
Read the methodology.
The full decision framework, with real examples of each ecosystem shipped in the wild.
// playbook / ai-assistantsCustom GPTs vs Claude Skills: when to ship which
The decision framework, deployment patterns, and the cost model. Steal the workflow.
Read the complete methodology →Ship an AI assistant in 30 days.
Tell us the use case. We audit your documentation, recommend Custom GPT or Claude Skill, and ship the first version inside a month. No six-figure retainer required.