Your team is building context management, multi-agent orchestration, and persistent student state on top of raw LLM calls. We already solved that. Plug in our agentic layer and ship autonomous learning features this quarter.
Your "adaptive learning" is a decision tree from 2018. Your board wants agents. Your CTO scoped it at six months and two ML hires you can't find.
You've tried wrapping LangChain around your question bank. Context windows blow up. State doesn't persist between sessions. The "AI tutor" hallucinates wrong answers. Your engineers are debugging prompt chains instead of building product.
Lexicor is the layer you're trying to build. Persistent cross-session student memory. Autonomous session planning. Validated content generation. Socratic tutoring with scaffolding control. Misconception detection that actually works. Battle-tested in production — not a demo.
Persistent, vectorized student model. Misconceptions with evidence chains. Procedure tracking. Learning profile. Cross-session state that survives weeks — not a context window hack.
Generates structured session plans from the student model. Selects questions by skill gap, sub-skill coverage, difficulty ramp. Planner-executor pattern — not a recommendation engine.
Socratic step-by-step guidance with scaffolding levels. Detects rushing, frustration, concept gaps vs procedural errors. Generates reusable procedure checklists. Controlled — not a chatbot.
Multi-model question generation with dual-solver verification and distractor auditing. Deduplication against existing bank. Your content pipeline ships validated material — not hallucinated questions.
Signal extraction from every response: misconception classification, time-pressure detection, confidence estimation, concept-gap vs procedure-gap differentiation. Feeds the Memory and Planning agents.
Automated tutor briefings, parent reporting, intervention scheduling, session prep. The administrative layer that scales your operations without scaling your team.
Context windows reset. Your student model needs to survive across sessions, weeks, months. We maintain a Bayesian mastery model with misconception evidence chains that persist indefinitely.
Chaining LLM calls breaks in production. We run a planner-executor architecture with structured handoffs between six specialized agents — not a prompt chain.
LLMs generate wrong answers. We validate every generated question through a dual-model solver, distractor auditor, and deduplication pipeline. Three models, five checks, zero hallucinated content.
You have the product and the users. You have an engineering team that ships fast. What you don't have is six months to build multi-agent infrastructure from scratch — or the ML hires to do it right.
You've scoped it internally. The estimate came back at two quarters and two specialized hires you'll spend months recruiting. That's the gap we fill.
Not sales. Engineers. Tell us what you're building, what's breaking, and where the LLM calls fall apart. We'll tell you if we can help.
hello@lexicor.ai