What can an AI agent do through Time Machine?

An agent can create and manage users, departments and roles; build courses, modules, topics, lessons and versions; author quizzes and AI role-play exercises (text, voice, video); ask AI experts grounded in company documents; upload and link documents and media; and read certificates, scorecards and per-student readiness insights. There are 18 endpoint groups covering the full learning lifecycle.

Does my company keep ownership of its training data and IP?

Yes. Time Machine is the learning loop you own. Your expertise, courses, role-plays, assessments and outcome data stay yours. You can swap the underlying model without losing the company-veteran expertise encoded in your learning system - the test of real control in the AI era.

API & MCP · Developer Platform

Connect your AI agents to the learning loop.

Time Machine now exposes a full REST API and MCP server. Your AI agents and automations can provision learners, build courses, run role-plays, certify skills, and read readiness scores - so training stops being a one-time project and becomes a continuous, autonomous loop your company owns.

Schedule a Demo Read the API docs

18Endpoint groups

100+REST operations

REST + MCPTwo ways to connect

YoursYour IP, your data

Why this matters

Human capital + token capital, compounding.

"You can offload a task, or even a job, but you can never offload your learning. The future of the firm is the ability to compound that learning across people and AI." - Satya Nadella, Chairman & CEO, Microsoft

Every company is now building two kinds of capital: human capital - the judgment, relationships, and pattern recognition of its people - and token capital - the AI capability it owns. The advantage doesn't come from picking the best model. It comes from building a learning loop on top of the models, where both kinds of capital compound and the institutional expertise stays yours.

That loop is exactly what Time Machine is. The API and MCP make it programmable: your agents handle orchestration and automation, while Time Machine provides the structure, simulations, certifications, scorecards, and reporting that turn raw knowledge into measurable, replicable skill. Swap out a generalist model whenever you like - the "company-veteran" expertise lives in the learning system you own.

The capabilities

What your agents can do through Time Machine.

One API surface covering the entire learning lifecycle - from provisioning a new hire to certifying them and reading their readiness. Every capability below is callable over REST or as an MCP tool.

Users & provisioning

Create, update, deactivate and reactivate team members the moment they join - or the moment HRIS says so.

Users

Departments & roles

Model your org structure and assign people to roles, so training paths map to real responsibilities.

Departments

Courses & versions

Create empty courses, assign roles, publish, and read published version snapshots - fully programmatically.

Courses · Course Versions

Modules, topics & lessons

Structure curriculum end to end - modules, topics, learning bytes, lessons and intros - and reorder it on the fly.

Modules · Topics · Lessons

Role-plays & exercises

Generate text, voice, and video simulations (ILE / RPE) and demo & app trainings where reps practice for real.

Exercises · Demo Trainings

Quizzes & knowledge checks

Author quiz questions and in-lesson checks (multiple-choice or open-ended) to verify understanding, not just completion.

Quiz Questions · Lesson Questions

AI experts

Ask company AI experts synchronous questions over the API - grounded in the documents you assign to them.

Experts

Documents & media

Upload, replace and link source material - docs, PDFs, decks, images and recordings - then attach it to topics.

Documents · Media · Topic Documents

Certificates & insights

Read certifications and PDFs, plus company- and student-level insights, scorecards and activity events.

Certificates · Insights

Model Context Protocol

One MCP server. Every model.

Point Claude, ChatGPT, Gemini, or your own agent framework at the Time Machine MCP server and the entire capability set above becomes a set of agent tools. Your agent coordinates training in plain language - "onboard this new AE," "check if Jordan is ready for live calls," "regenerate the demo course from the new release notes" - and Time Machine does the work.

Plain-language access to the same operations as the REST API
Drop-in for Claude Code, Claude Desktop, and MCP-aware agents
Same API-key auth and the same permissions as REST
Lets your existing AI agents own orchestration; Time Machine owns teaching, evaluating and certifying

Claude Claude Code ChatGPT Gemini Cursor Custom agents

Agent · plain language → MCP tools

# Prompt to your MCP-connected agent
"A new AE, Jordan Vega, just started on the
 Enterprise team. Onboard them and tell me
 when they're certified to take live calls."

# The agent calls Time Machine MCP tools
create_user(name="Jordan Vega",
            role="account-executive")
assign_course(course="AE Onboarding")
get_student_insights(user="jordan")
list_certificates(user="jordan")

# → "Jordan is 80% through onboarding.
#    Objection-handling role-play: needs work.
#    Not yet certified for live calls."

Quickstart

Authenticate once. Then drive the loop.

Issue a key from Company Settings → Integrations, send it as a bearer token, and you're live. Base URL https://api.timeml.ai, endpoints under /api/v1.

Authentication

# Every request carries your API key as a bearer token
curl https://api.timeml.ai/api/v1/users \
  -H "Authorization: Bearer tml_live_<your_token>"

Provision a new learner

POST /api/v1/users
curl -X POST https://api.timeml.ai/api/v1/users \
  -H "Authorization: Bearer tml_live_<token>" \
  -H "Content-Type: application/json" \
  -d '{
    "email": "jordan.vega@acme.com",
    "firstName": "Jordan",
    "lastName": "Vega",
    "departmentId": "sales",
    "roleId": "account-executive"
  }'

Read readiness for a student

GET /api/v1/insights/students/{userId}
curl https://api.timeml.ai/api/v1/insights/\
students/jordan-vega \
  -H "Authorization: Bearer tml_live_<token>"

# → scorecards, certifications,
#   role-play performance, skill gaps,
#   readiness scoring

Ask an AI expert

POST /api/v1/experts/{id}/ask
curl -X POST https://api.timeml.ai/api/v1/\
experts/sales-playbook/ask \
  -H "Authorization: Bearer tml_live_<token>" \
  -H "Content-Type: application/json" \
  -d '{
    "question": "How do we handle a security objection?"
  }'

Publish a regenerated course

POST /api/v1/courses/{id}/publish
curl -X POST https://api.timeml.ai/api/v1/\
courses/product-101/publish \
  -H "Authorization: Bearer tml_live_<token>"

# Pair with documents + experts endpoints
#   to rebuild a course whenever new
#   product docs are approved.

Use case · Engineering

Headless onboarding, fully automated.

A real pattern from a Time Machine customer: an automation - orchestrated by Claude Code - that runs every day and wires Time Machine into the rest of the stack. New hires get provisioned and trained with zero manual steps.

The stack

Claude Code - orchestrator that ties the systems together
Time Machine - the learning experience and certification engine
NetSuite (HRIS) - the source of truth for new employees
Microsoft Dynamics + Office 365 - CRM, email, Teams, OneDrive
Power Automate - human-in-the-loop approvals

When HR enters a new employee in the HRIS, the automation provisions accounts across every platform - including Time Machine - creates the department and role if they don't exist yet, and enrolls the hire in their welcome and tech-stack courses. Weekly, it pulls learning stats back into the HRIS and nudges anyone who's behind.

New hire entered in HRISNetSuite triggers the daily automation.

Accounts provisioned everywhereDynamics, Office 365, and Time Machine via POST /users. Department & role created if missing.

Courses auto-assigned"Welcome" + "Tech Stack: Demo & App Training" enrolled on day one.

Progress synced & nudgedGET /insights writes weekly stats to HRIS; reminders go out over Office 365.

Content stays currentNew product doc in OneDrive → courses & experts regenerate and republish automatically.

Use case · Sales

The sales manager who stopped chasing spreadsheets.

This team had already built its own AI ecosystem - agents with access to CRM, playbooks, SOPs and competitive intel. The problem wasn't information. It was execution. Time Machine became the learning and certification engine behind their AI strategy.

From "did they finish?" to "are they ready?"

Their AI agent connects to Time Machine over MCP and API. When a rep joins, the agent builds a personalized onboarding path based on role, territory and experience. As the rep progresses, the agent monitors, answers questions, assigns remediation when they struggle, and advances them on mastery.

Instead of asking "did they complete onboarding?" the manager now gets objective answers to "can they handle objections, deliver the demo, and own a customer conversation?" - pulled straight from Time Machine scorecards, assessments, certifications and role-plays.

Personalized paths from CRM + experience data
Continuous coaching and automatic remediation
SCORM content tracked alongside AI learning
RACI-based accountability per learning objective
Readiness verified before reps touch a live deal

Automated readiness report → manager

↑

Time-to-productivityTrending down quarter over quarter.

✓

Certification progress9 of 11 new reps certified on discovery.

Skill gap detectedObjection handling below bar for 3 reps → remediation auto-assigned.

◎

Coaching prioritiesRanked list of who needs the manager's time, and why.

⤓

Exported to BI / CRM / HRISReadiness data flows into the systems leadership already uses.

Endpoint reference

18 groups. The whole learning lifecycle.

A map of the public REST API. Full request/response schemas live in the interactive OpenAPI docs.

Users 6

Create, read, update, deactivate and reactivate team members.

POST /api/v1/users PATCH /api/v1/users/{id}

Departments 7

Manage departments, create roles, and assign users to roles.

POST /api/v1/departments POST /api/v1/departments/{id}/roles

Courses 9

Create, update, publish and role-assign company courses.

POST /api/v1/courses POST /api/v1/courses/{id}/publish

Course Versions 2

Read published version snapshots and their content.

GET /api/v1/courses/{id}/versions

Course Modules 7

Create, edit, reorder and delete modules inside a course.

POST /api/v1/courses/{id}/modules

Course Topics 8

Create, edit, reorder, move and delete topics inside modules.

POST /api/v1/modules/{id}/topics

Learning Bytes 5

Group lessons into learning bytes inside a topic.

POST /api/v1/topics/{id}/learning-bytes

Lessons 5

Create, edit and delete lessons inside a learning byte.

POST /api/v1/learning-bytes/{id}/lessons

Intros 6

Short narrative shown before a module or topic.

PUT /api/v1/topics/{id}/intro

Exercises 7

Role-play exercises: ILE_TEXT, RPE_VOICE and RPE_VIDEO.

POST /api/v1/topics/{id}/exercises

Quiz Questions 12

Quiz questions attached to a module or topic.

POST /api/v1/topics/{id}/quiz-questions

Lesson Questions 6

In-lesson checks: MULTIPLE_CHOICE or OPEN_ENDED.

POST /api/v1/lessons/{id}/questions

Demo Trainings 7

Demo & application training videos, availability and reprocessing.

POST /api/v1/demo-trainings

Experts 8

List experts, assign documents and roles, and ask questions.

POST /api/v1/experts/{id}/ask

Documents 6

Upload, replace, update and delete company documents.

POST /api/v1/documents PUT /api/v1/documents/{id}

Media 6

Manage company images and videos.

POST /api/v1/media

Topic Documents 3

Link and unlink documents to and from a topic.

POST /api/v1/topics/{id}/documents

Certificates 3

Read earned certificates and download their PDFs.

GET /api/v1/students/{userId}/certificates

Insights 3

Company- and student-level learning insights and events.

GET /api/v1/insights/company GET /api/v1/insights/students/{userId}

Built for agents and humans

Designed to be read by machines, trusted by people.

This platform is built for a world where AI agents are first-class users - without compromising the control and ownership that make the learning loop your IP.

Two ways in: REST + MCP

A clean OpenAPI 3.1 REST API for code, and an MCP server for agents that prefer plain language. Same operations, same auth.

Structured data & llms.txt

Schema.org markup and a maintained /llms.txt so answer engines and crawlers can describe this API accurately.

Plain-language descriptions

Every endpoint group is named and described for humans first - so an agent reading the docs knows exactly what each tool does.

Your IP stays yours

Swap the underlying model anytime without losing the company-veteran expertise encoded in your learning system. Sovereignty by design.

Common questions

API & MCP, answered.

What is the Time Machine API and MCP server?

The Time Machine API is a public REST API (OpenAPI 3.1) that lets your systems and AI agents drive the learning loop: provision learners, build and publish courses, generate role-play simulations, query AI experts, certify skills, and read readiness and insight data. The MCP server exposes those same capabilities to AI assistants like Claude, ChatGPT and Gemini as agent tools, so an agent can coordinate training in plain language.

How do I authenticate?

Issue an API key from Company Settings → Integrations. Keys are prefixed tml_live_ and sent as an HTTP bearer token: Authorization: Bearer tml_live_<token>. The base URL is https://api.timeml.ai and endpoints live under /api/v1.

What can an AI agent actually do?

Create and manage users, departments and roles; build courses, modules, topics, lessons and versions; author quizzes and AI role-play exercises (text, voice, video); ask AI experts grounded in your documents; upload and link documents and media; and read certificates, scorecards and per-student readiness insights. There are 18 endpoint groups covering the full learning lifecycle.

Do we keep ownership of our training data and IP?

Yes. Time Machine is the learning loop you own. Your expertise, courses, role-plays, assessments and outcome data stay yours. You can swap the underlying generalist model without losing the company-veteran expertise built into your learning system - which is the real test of control and sovereignty in the AI era.

Can agents drive both onboarding and ongoing coaching?

Yes. Agents create personalized onboarding paths when a person joins, then continuously monitor progress, assign remediation when a learner struggles, advance them on mastery, and surface readiness and coaching priorities to managers - turning a one-time setup into a continuous, autonomous training loop.

Is the API ready for production use today?

Yes - the public REST API and MCP server are live. The fastest way to scope your integration is to talk to us: we'll map your agents and systems to the right endpoints and help you stand up the first workflow. Browse the interactive docs anytime.

Get API & MCP access

Put your agents to work on real training.

Tell us about your stack and what you want to automate. We'll map it to the API and MCP and come back within one business day.

REST + MCP · SOC 2 Type II · Your IP stays yours · Or call 949-416-5055