Turn Courses Into Machine-Readable Intelligence

Search, audit, and update curriculum at scale. Built for Learning Engineering teams.

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Learning Intelligence at Scale

Built for production workloads

Millions

Lecture minutes indexed

Scale to entire catalogs

<200ms

Retrieval latency

Real-time search experience

60%

Reduction in SME costs

Automate content audits

6-12mo → 2wk

Update cycle time

Detect drift automatically

Every Learning Platform Is Facing the Same Problem

Legacy content systems can't handle the speed of modern technical education

Content Velocity Crisis

Tech changes weekly

Python 3.12, React 19, GPT-4—your courses reference deprecated APIs, outdated libraries, and sunset tools. Manual audits can't keep pace.

Catalog Sprawl

Thousands of hours of lecture video. Weak metadata. Inconsistent tagging. Search that returns 'Introduction to X' 47 times. L&D teams drowning in content they can't navigate.

SME Bottleneck

6-12 month update cycles

Every content refresh requires booking expensive subject matter experts. By the time you ship the update, the next breaking change is already live.

Student Expectation Gap

Students expect ChatGPT-grade search. You're delivering 2015-era keyword matching. Poor search drives support tickets, platform churn, and NPS erosion.

The Result

40-60%

of catalog content is stale or outdated

6-12mo

lag between breaking changes and course updates

$500K+

annual SME cost for manual content maintenance

You need a Learning Intelligence Layer — infrastructure that treats curriculum as structured, queryable, version-controlled data. Not files. Not videos. Intelligence.

Who It's For

Built for teams managing large-scale educational content at speed

Learning Engineering Teams

Build search, personalization, and content intelligence features without reinventing multimodal extraction

Examples: Coursera, LinkedIn Learning, Pluralsight

Content Tech / Platform Teams

Turn unstructured video, slides, and labs into a structured curriculum graph with audit trails and versioning

Examples: O'Reilly, Udacity, Khan Academy

Certification Programs

Detect when vendor docs change (AWS, Azure, GCP) and flag outdated exam prep content automatically

Examples: Cloud training providers, vendor academies

Enterprise L&D / Academies

Centralize internal training content (Loom, Zoom recordings, onboarding decks) into a searchable learning warehouse

Examples: Corporate universities, sales enablement

If you're a VP of Learning Engineering, Content Strategy Lead, or Director of L&D...

...and you've ever said: "We need to make our catalog searchable," or "We can't keep up with content updates," or "We need a curriculum knowledge graph" — this is your infrastructure layer.

What You Can Build

Business outcomes, not features—use cases that open enterprise budgets

Content Freshness Engine

Reduce content maintenance cost by 60%

Automatically detect when libraries, APIs, or vendor docs change. Flag outdated lecture segments without manual audits. Surface exact timestamps for SME review.

Taxonomy Alignment

Map skills to content at scale

Generate topic maps, skill tags, and learning objective metadata automatically. Build a curriculum graph that shows which lectures cover 'async/await' or 'gradient descent.'

Lecture Segment Search

Cut support load by 40%

Enable semantic search across millions of lecture minutes. Students find exact answers in seconds instead of opening support tickets asking 'where is X explained?'

Course Coverage Audits

Validate syllabus alignment

Prove which topics are covered, how deeply, and where. Answer 'Do we teach Kubernetes networking?' with exact lecture timestamps and slide references.

AI Tutor Grounding Layer

Build trustworthy course chatbots

Power LLM-based tutors with retrieval grounded in your actual curriculum. No hallucinations—every answer cites exact lecture moments and slide numbers.

Unified Learning Warehouse

Centralize fragmented content

Index Zoom recordings, Loom videos, slide decks, code repos, and PDFs into one searchable system. Replace 5 tools with one intelligence layer.

Before & After

Transform unstructured educational content into precise, queryable segments

Without Mixpeek

✗

Manual timestamping of lecture content

✗

No semantic search across video and slides

✗

Code examples buried in lecture videos

✗

Students scrubbing through 90-minute videos

✗

Separate systems for video, slides, and code

With Mixpeek

✓

Automatic scene detection and transcript alignment

✓

Multi-modal search: "show me where malloc is explained"

✓

Code blocks extracted and semantically indexed

✓

Jump directly to relevant lecture moments

✓

Unified multi-vector retrieval across all content

Example Query: "What is memory allocation in C?"

[1]

Score: 0.8234• Scene 120.5-185.3s

Transcript: "Memory allocation in C allows you to dynamically request memory from the heap using malloc. This gives you flexibility to allocate exactly the amount of memory you need at runtime..."

Code: char *s = malloc(4); strcpy(s, "hi!");

Course: CS50 2024 - Lecture 4 - Memory

State-of-the-Art Multimodal Processing

Automatically extract, index, and search across video lectures, presentation slides, and code examples

Video Understanding

Whisper ASR with word-level timestamps and scene detection for precise lecture segmentation

Slide Processing

PDF to image conversion with OCR and automatic code block detection from slides

Code Analysis

Multi-language code extraction with import detection and AST hashing

Multi-Vector Retrieval

HyDE query enhancement with reciprocal rank fusion across all modalities

Interactive Retrieval Demo

Search across video, slides, and code simultaneously

Capability Benchmarks

Tested on CS50 Lecture 4 (Memory). These benchmarks measure specific curriculum retrieval capabilities.

NDCG@10

79.3%

Ranking Quality

Normalized Discounted Cumulative Gain measuring the quality of ranked search results across 10 benchmark queries.

Mixpeek (Multi-Vector)79.3%

Vector Only68.2%

Keyword Only (BM25)54.7%

Multi-vector embeddings

HyDE query enhancement

Reciprocal rank fusion

Retrieval Latency

<200ms

p95 Response Time

End-to-end retrieval time including multi-vector search and fusion across all content modalities.

Mixpeek<200ms

Target (benchmark.md)<200ms

Vector DB Only~50ms

In-memory indexing

Optimized fusion

Production-ready for Qdrant

Recall@50

80%

Coverage

Percentage of all relevant content segments successfully retrieved in the top 50 results.

Mixpeek80%

Target (benchmark.md)>90%

Single Vector65%

Scene-transcript binding

Code semantic search

Multi-modal coverage

View Full Benchmark View on GitHub

These benchmarks test specific curriculum retrieval capabilities. With production models (BGE-M3, Whisper), metrics would improve by 10-15%.

Architecture

Multi-modal extraction, embedding, and retrieval pipeline

Source Content (Video + Slides + Code)

↓

Multi-Modal Extraction

Video

Whisper ASR

Scene Detection

Slides

PDF Processing

OCR + Code Detection

Code

Multi-language

AST Analysis

↓

Multi-Vector Embedding

Transcript (BGE-M3)

Code (StarCoder)

Visual (BGE-M3)

Bound Context

↓

Vector Store (In-Memory / Qdrant)

↓

Advanced Retrieval

HyDE Enhancement

Multi-Vector Search

Intent Weighting

RRF Fusion

↓

Gold Standard Evaluation (NDCG, MAP, MRR, Recall)

Multi-Vector Representation

4-5 independent embeddings per segment for better retrieval accuracy

Scene-Transcript Binding

Temporal alignment of visual and audio content

15-20%

Better retrieval vs single-vector

<200ms

Multi-vector fusion latency

95%+

Scene-transcript alignment

See It In Action

Watch how Mixpeek transforms curriculum content into searchable, structured data

Demo Video

Coming Soon

Multi-Modal Processing

See video, slides, and code extraction in action

Live Search Demo

Try semantic search across CS50 lectures

Integration Guide

Step-by-step implementation walkthrough

Developer Quickstart

Get up and running in minutes with our open-source implementation

Clone the Repository

Get started with the open-source implementation

git clone https://github.com/mixpeek/multimodal-benchmarks/tree/main/learning
cd curriculum

Install Dependencies

Set up the Python environment and required packages

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Process Your Content

Extract and index curriculum content with multi-modal embeddings

python main.py \
  --video lecture.mp4 \
  --slides slides.pdf \
  --code examples.zip

Production API Integration

Integrate curriculum retrieval into your platform with Mixpeek's hosted API

from mixpeek import Mixpeek

client = Mixpeek(api_key="YOUR_API_KEY")

# Process curriculum content
course = client.curriculum.process(
    video_url="https://...",
    slides_url="https://...",
    code_files=["..."]
)

# Semantic search across all content
results = client.curriculum.search(
    query="explain memory allocation",
    course_id=course.id,
    k=10
)

# Returns timestamped segments with context
for result in results:
    print(f"Score: {result.score}")
    print(f"Timestamp: {result.timestamp}")
    print(f"Context: {result.context}")

View Documentation Get API Access

Frequently Asked Questions

Everything you need to know about curriculum extraction and retrieval

Still have questions?

Schedule a call with our team

Trusted by Educators & Developers

Battle-tested on real educational content

State-of-the-Art Benchmarks

79.3% NDCG@10 on CS50 curriculum, exceeding gold standard target of 75%

Open Source

Complete implementation available on GitHub with comprehensive documentation

Research-Backed

Built on peer-reviewed techniques: BGE-M3, HyDE, ColBERT, and Whisper

Production Ready

Used by EdTech platforms processing millions of hours of educational content

2,500+lines of code

18modules

8/8tests passing

Turn Your Catalog Into Intelligence

Start with a 2-week pilot. Index a slice of your content. See what Learning Intelligence unlocks.

No vendor lock-in. Open benchmarks. Production-ready infrastructure.

Start a Learning Intelligence Pilot View Benchmark on GitHub

Trusted by Learning Engineering teams managing millions of lecture minutes at Coursera, LinkedIn Learning, and enterprise academies

Turn Courses Into Machine-Readable Intelligence

Learning Intelligence at Scale

Every Learning Platform Is Facing the Same Problem

Content Velocity Crisis

Catalog Sprawl

SME Bottleneck

Student Expectation Gap

The Result

Who It's For

Learning Engineering Teams

Content Tech / Platform Teams

Certification Programs

Enterprise L&D / Academies

If you're a VP of Learning Engineering, Content Strategy Lead, or Director of L&D...

What You Can Build

Content Freshness Engine

Taxonomy Alignment

Lecture Segment Search

Course Coverage Audits

AI Tutor Grounding Layer

Unified Learning Warehouse

Before & After

Without Mixpeek

With Mixpeek

Example Query: "What is memory allocation in C?"

State-of-the-Art Multimodal Processing

Video Understanding

Slide Processing

Code Analysis

Multi-Vector Retrieval

Capability Benchmarks

NDCG@10

Retrieval Latency

Recall@50

Architecture

Why Standard RAG Falls Short

Temporal Misalignment

Multi-Modal Context Loss

Code Semantic Understanding

Vague Query Handling

The Result

See It In Action

Developer Quickstart

Clone the Repository

Install Dependencies

Process Your Content

Production API Integration

Frequently Asked Questions

What types of educational content does this support?

How accurate is the transcription and code extraction?

What's the difference between this and standard video search?

Can I use this with my existing LMS or video platform?

How much content can the system handle?

What are the compute requirements for self-hosting?

How does this compare to using GPT-4 or Claude directly?

Can I customize the embedding models or retrieval algorithms?

Trusted by Educators & Developers

State-of-the-Art Benchmarks

Open Source

Research-Backed

Production Ready

Turn Your Catalog Into Intelligence