When engineers use public AI tools, code goes somewhere — stored in chat logs, potentially used for model training, passing through servers your security team never approved. Most teams either accept that risk or block AI tools entirely. TypingMind is the third option: a workspace where you own the API keys, choose the deployment, and define what the team can access — running Claude, GPT, Gemini, or open-source models through a single interface.
❝TypingMind has impressed us with its intuitive use and easy configuration; it quickly connects to various LLMs and enables rapid creation and deployment of custom AI characters throughout the company. The continuous updates and new features keep the tool evolving, benefiting our team without the need for in-house developments.❞
On this page:
- How BYOK and self-hosting keep your code out of AI training pipelines
- Switching between Claude, GPT, Gemini, and open-source models per task
- Building agents loaded with your own stack, conventions, and internal docs
- Token limits, team groups, and audit logs for engineering leads
Your code never trains anyone else's AI model
API access — as opposed to consumer products like ChatGPT or Claude.ai — means your conversations are not used for model training by default. TypingMind routes requests through your own API keys directly to the provider. For teams that need stronger guarantees, you can self-host the entire workspace inside your own VPC or run it fully air-gapped. No data leaves your network boundary.
No training on your data
Direct API access means your code, architecture docs, and incident details are never used to improve public AI models.
Managed cloud or self-hosted
Use managed cloud for a zero-ops setup, or self-host inside your own VPC — AWS, GCP, Azure, or on-premises.
Bring your own API keys
API requests go directly from your workspace to the provider using your keys. Costs are billed directly to your accounts.
Any model, no lock-in, no separate subscriptions
Different tasks call for different models. Claude Sonnet for reasoning through a complex bug or reviewing a hairy PR. GPT-5.4 for generating boilerplate and refactoring. Gemini Pro when you need to reason across an entire codebase or a 50,000-line log file. DeepSeek or a self-hosted open-source model for high-volume, cost-sensitive tasks.
One workspace, one login — no separate team subscriptions per provider. Each AI agent can be pinned to a specific model. Engineers switch models mid-task. When a new model ships, you add it in minutes and it's available to the whole team immediately.
❝TypingMind has given us a great, consistent UI for LLM use across the various models. It's a substantial improvement over some of the early native AI apps many on our team had been using.❞
Agents built for your exact stack and standards
Each agent you build gets a system prompt loaded with your coding standards, preferred libraries, architectural constraints, and security policies. Engineers stop re-explaining context on every prompt — the agent already knows your stack. New hires use the same agents as veterans and get answers grounded in how your team actually builds, not generic best practices.
Reviews pull requests for logic errors, security vulnerabilities, and test gaps — aligned to your team's specific conventions, not generic style guides.
Diagnoses bugs, race conditions, and performance regressions from stack traces or code snippets — explains root causes with actionable fixes grounded in your codebase.
Generates API docs, README files, and ADRs from code — pre-loaded with your doc standards and format templates so output is consistent and publish-ready.
Evaluates design trade-offs and drafts ADRs using your existing architecture patterns as context — not generic system design advice.
Answers questions about your specific codebase, explains how your services fit together, and walks new hires through internal tooling — reducing ramp time from weeks to days.
Generates unit and integration tests from existing code — with test style and coverage strategy matching your team's actual testing patterns.
All your internal knowledge in one queryable place
Upload your Confluence pages, Notion docs, API specs, runbooks, and ADRs. Engineers query them in plain English and get source-referenced answers — not web search results or guesses. A junior engineer setting up their local environment gets a direct answer without pinging a senior. A dev troubleshooting an unfamiliar service at 2am gets a response from your actual runbooks. Senior engineers stop fielding the same questions on repeat.
| Name | Status | |
|---|---|---|
| Backend Architecture Overview 2026 | Ready | |
| API Design Standards & Conventions | Ready | |
| Production Deployment Runbook | Ready | |
| Incident Postmortem: Payment Outage Q4 | Ready | |
| Engineer Onboarding Checklist | Ready | |
| Internal SDK Reference Guide | Ready |
Connects to every tool your team already uses
Connect agents to GitHub, Jira, Confluence, and internal systems via plugins or MCP servers. An engineer asks about a ticket and gets back the actual Jira description, linked PRs, and deployment status — not a prompt asking them to paste it in. Build custom MCP servers against any internal API: your metrics platform, CI pipeline, or deployment tooling. All connections run through your own credentials and stay within your deployment boundary.
❝We use TypingMind across various tasks to streamline our workflows and enhance productivity. We fell in love with the product and believed it to be the only one of its kind.❞
Set up your first integration
Plugins and MCP servers take minutes to configure. Start with GitHub or Jira, then add internal APIs as needed. Nothing passes through TypingMind infrastructure unless you choose managed cloud deployment.
Full control over who uses what, and how much
Organize engineers into groups by role or squad. Each group gets its own set of agents, knowledge bases, and model access — a DevOps engineer sees infra runbooks and deployment agents, a frontend dev sees component libraries and design tokens. Set token and message caps per user per day to keep AI costs predictable. Pre-load shared prompts so code review templates and architecture checklists are used by default.
Groups let you organize your engineering team and control which agents, knowledge bases, and models each role can access — with audit logs for every session.
Code Reviewer
In-useUsage
User Groups
Max tokens / user / day
200,000
Max messages / user / day
150
Success stories from engineering teams
Real teams using TypingMind — what they built, why they chose it, and what changed.
Case Study: InnoGames integrates AI to enhance efficiency across departments
InnoGames is a leading game development and publishing company behind Forge of Empires, Tribal Wars, and Rise of Cultures. Their 157 engineers use TypingMind as a central hub — with a "Pro Coder" agent for coding suggestions and refactoring — and consume 10M+ GPT-4 tokens monthly through SSO-authenticated access.
InnoGames Success Story →
Case Study: Atomic Object leverages AI through TypingMind
Atomic Object is a software development consultancy that builds custom software for clients. 80% of their team adopted TypingMind across 7+ AI models — using it to accelerate code quality and consulting work without sending client code through consumer AI products.
Atomic Object Success Story →
Case Study: PixelMechanics builds AI collaborative workspace via TypingMind
PixelMechanics chose TypingMind because API-based access meant their data was not used for model training — and the flexible cost model (pay only for actual usage) beat per-seat subscriptions. Their team deployed 13+ agents including a Coding Assistant, Jira Ticket Supporter, and Project Draft Innovator.
PixelMechanics Success Story →
Case Study: i22 accelerates development with AI-powered workflows
i22 is a digital agency specializing in custom software development and digital transformation. Their engineering and project teams use TypingMind to streamline development workflows and produce technical documentation — while keeping client code out of public AI systems.
i22 Success Story →
Ann is a member of the Customer Success team at TypingMind. She helps customers get the most out of their AI workspaces and is passionate about delivering great experiences.