Create and launch work
Teachers start from a template or draft, edit the task, and launch it for a class without rebuilding the workflow around AI.
LiftEd
LiftEd helps schools turn checked work into a clear picture of knowledge: what students understand, where the gaps are, and what should happen next. It is not an LMS, not a content library, and not an AI tutor pasted onto the old workflow.
~500 paying teachers
~20k active students
~$50k revenue since launch
Problem
Marks alone do not explain what to fix.
Teachers spend hours preparing tasks, checking work, and turning marks into something useful. Open answers are the hardest part: they carry more signal than multiple choice, but they are slow to review at classroom scale.
For students, feedback often arrives too late. For school leaders, the gradebook shows results, but not the map behind them: which topic fell through, which class needs support, and where the next intervention should start.
- Manual checking slows feedback, especially for written and open-answer work.
- One mark does not show which topic or skill broke down.
- Administrators need a school-level picture without adding another reporting chore for teachers.
Solution
We built the layer between checking and decisions.
The product flow starts with the teacher: create a task, adapt it, launch it in class, and review results. AI helps with the heavy checking work, but the teacher keeps the final judgement and context.
After that, LiftEd turns the result into something the school can act on: topic progress, weak spots, adaptive practice, parent visibility, and dashboards for administrators. The core loop is checking → diagnostics → gap map → next action.
Check written answers
AI reviews written work against criteria and explanations, while the teacher keeps control of the final judgement.
Find the weak topics
Results become a topic map, so a class result can point to a specific gap rather than only a gradebook entry.
Give each role its view
Teacher, student, parent, and administrator each see the part of the signal they can actually use.
Technical work
AI is useful only when the workflow around it is strict.
We treated AI grading as one part of a stricter assessment system, not as a magic box. AI output is connected to rubrics, attempts, teacher review, and visible reasoning, so the result can be inspected and corrected.
The surrounding product logic matters just as much: retries, topic gates, adaptive practice, classroom launch flows, parent reports, and analytics that roll up from student to class to school.
Assessment engine for tests, short answers, and written responses.
AI-assisted grading connected to criteria, attempts, review, and explanations.
Adaptive practice that keeps a student on a weak topic before moving on.
Role-based surfaces for teacher work, student practice, parent visibility, and school analytics.
LLM gradingAdaptive engineGap diagnosticsRole-based UIsSchool analytics
What changed
Less checking drag
Teachers spend less time sorting raw answers and more time deciding what the class needs next.
A clearer map of gaps
Assessment becomes a map of topics to revisit, not only a mark after the lesson.
A product for the whole school
LiftEd connects teacher, student, parent, and administrator views, which makes it usable beyond a single classroom.
Tell us what you're building.
Start with a few detailsWe reply within one business day. Then Azamat joins every first call personally, so you get an honest scope, budget, and fit from the person responsible for delivery.