Expert time is scarce and generic AI lacks instructional grounding. Arch scales expert reasoning to every learner.
Learners click through lessons, memorize concepts but can't apply them. They fail certifications not from lack of intelligence or effort, but because critical reasoning connections are missing.
Generic AI tutors give explanations that sound plausible but contradict certification standards. Inefficient scope or abstraction level. No grounding, no auditability, no control.
SMEs (Subject Matter Experts) can be the best teachers, but they can't provide feedback on thousands of practice attempts. Traditional scaling methods dilute quality.
For adults, having a mortgage, kids, full time job, education has to optimize for maximum mastery per unit learning effort.
Arch enables experts to teach AI how they solve problems once, so AI can support thousands of learners with the same exact reasoning.
Reasoning structure is extracted from lessons and training materials. Based on this index, educators define reasoning steps and conceptual connections required for a specific problem class. The system can draft problem/question pools with one click for each class.
Students receive targeted feedback showing exactly where their reasoning breaks down – not just 'incorrect answer' but which conceptual connection they're missing.
Every part of the internal index is explainable, traceable to exact source material. The system uses an advanced retrieval engine not a black-box. It provides reasoning primitives, lesson context and deterministic recommendation constraints to the generator model.
Reasoning structure and exact source references provide orthogonal dimensions in AI grounding. These represent exactly the same reasoning paths and style educators use in lessons and practice problems.
Welcome back!
Your Machine Learning Fundamentals exam is approaching. Let's do some practice. How much time do you have?
1 hour
Structured practice
Two problems that combine PCA and Linear Regression. These problems cover 3 of the 5 exam reasoning patterns you haven't practiced yet.
Welcome back! How much time do you have?
15 minutes
Focused concept reinforcement:I've selected 8 flashcards covering fundamental concepts across PCA, Linear Regression, and Gradient Descent. These concepts appear in 40% of exam problems.
View flashcardsContext: Student practicing a problem and gets stuck.
Question: "I'm struggling to understand how neural networks learn. Can you explain backpropagation?"
"Backpropagation is the fundamental algorithm used to train neural networks. It works by computing gradients of the loss function with respect to each weight by applying the chain rule, iterating backwards from the output layer to the input layer. The gradient descent optimizer then uses these gradients to update the weights..."
"You understand chain rule and gradient descent individually, but struggle with how the chain rule applies across multiple network layers.
Complete chain:
...
Key insight: Each layer's gradient uses the previous layer's gradient. This is the propagation part, gradient information flows backward."
* Privacy respected: The model does not see student answers. It is enough to know the student successfully answered step 1, step 2 and failed step 3.
Upload high-quality core materials, key lessons, worked solutions (quality over quantity). Then Arch will guide you through the model building, review and optimization process. It can run with minimal interaction or offer maximum control by annotations, checkpoints and structural edits.
Define a question or problem type as you usually do.
Edit or upload step-by-step instructions on how to solve. It is important to provide detailed steps (system might help detect step boundaries).
For each step: "What concepts and reasoning are needed to solve this step?" Choose from a filtered list showing conceptual components and reasoning connections.
* Done once per problem type, not per problem.
High leverage, low overhead.
No guesswork. No analysis paralysis. Clear next steps aligned with certification requirements.
Work through practice questions and problems at your own pace.
Receive precise feedback grounded in expert reasoning. Know exactly where your thinking breaks down.
Removes confusion and wasted effort, preserves productive struggle.
Learners build mastery faster with targeted guidance.
Student already usse AI. The question is which they use – generic AI with no alignment to certification standards, or Arch grounded in expert's reasoning.
This system guides learning. Institutions retain complete control over curriculum design, certification standards, and pass/fail decisions.
Institutions own their data. We process it. Single-tenant SaaS – dedicated instances, no shared infrastructure.
Helps with accreditation by making exams well-defined at cognitive and reasoning levels – but doesn't enforce standardization.
Requires domain expert input for initial indexing and after major curiculum changes.
Upskilling programs, role-based certifications, technical training pathways, and compliance requirements for your workforce
Product certifications, partner enablement programs, and customer training that drives adoption and reduces support costs
License preparation, industry certifications, and continuing education for professions (Bootcamps, online or hybrid Master's programs, etc.)
SMEs (part-time or consultant enough) who design curriculum, author training materials, and understand how learners develop mastery
Clear learning objectives, defined assessment standards, and measurable competency requirements
Lessons, worked solutions, practice problems, and documentation that reflect how your experts teach
Initial setup requires expert time to review and fine-tune indexing, define problem classes and reasoning structure.
* This initial investment creates long-term leverage as the model can auto-generate practice problems (educator review available) and guide 1000s of learners.
Academic institutions: Arch will be evaluated in formal educational (university) context. The architecture naturally fits to controlled applications where compliance and auditability are just as important as performance.
Adult learners worry about predictive profiling, hidden performance evaluations, and risk scores. Cognitive science shows, the lack of trust impedes cognition and fosters disengagement, avoidance and cheating.
Arch provides an E2EE safe space to practice, experiment and fail.
Stored for compliance and certification. Clearly distinguished with explicit privacy policies.
End-to-end encrypted. Opt-out available. Hard-deletable by learners.
Never processed for system adaptation. Hard-deletable by learners anytime.
Most adaptive learning systems surveil every interaction to build learner models. This enables profiling, learner analytics dashboards, that erodes trust and psychological safety.
Our architecture achieves precision diagnostics by processing only practice and exam evidence. Minimal data processing that can run in browser or TEE with full E2EE.
Result: Learners practice more honestly when they are not being watched.
We're partnering with organizations during Early Access to refine Arch based on real educator workflows and institutional needs.
If you run technical training, professional certification, or compliance programs and need to improve completion rate, time to certification and market value let's explore weather Arch can solve your problem.