Not another chatbot. Aster is an outcome-driven intelligence engine that reasons over text, images, structured inputs, and voice, with pluggable optimizer functions, brand-aware content design, persistent memory, and a self-improving learning loop. It powers any surface, from lead qualification to in-app product surveys, across every domain.
Users abandon because questions feel irrelevant, repetitive, or endless. CSAT surveys average just 25%.
Lack of intelligent qualification means sales teams waste time on leads that static forms couldn't properly assess.
Insights from insurance intake don't improve healthcare triage. Every team rebuilds from scratch with zero shared learning.
Explain your problem in plain English. Upload heuristics if you have them.
AI creates a seed prompt, question graph, and optimizer function.
Run evals against your criteria. Auto-optimize with GEPA techniques.
Ship to any frontend: chat, form, Slack, WhatsApp, voice, or API.
Every session makes it smarter. Federated learning across domains.
RAG chatbots retrieve answers from a knowledge base. Aster orchestrates a dynamic conversation toward a measurable outcome, deciding what to ask next based on an optimizer function.
Typeform asks the same 15 questions regardless of who you are. Aster infers signals from context, skips irrelevant questions, and adapts the flow in real-time.
Custom decision trees take months to build and rot immediately. Aster deploys in hours and gets smarter with every conversation through eval-driven refinement.
Use our pre-built scoring functions, connect your own API, or build one visually. Mix and match across projects.
Pre-built optimizers for lead scoring, triage, readiness assessment. Start in minutes.
POST to your endpoint. Aster sends collected answers, you return a score + signals.
Drag-and-drop DSL editor. Define rules, weights, thresholds visually. No code required.
Users upload photos, documents, or screenshots. Aster doesn't just store them, it reasons over visual content to extract signals, detect patterns, and inform the next question.
Aster dynamically chooses the optimal input type per question, free text when context is ambiguous, radio buttons for constrained choices, sliders for scales, chip-select for speed.
Aster fuses signals across modalities. An image, a slider value, a selection, and free text aren't separate inputs, they're combined into a unified context that drives the optimizer.
Use our built-in memory infrastructure. We store user preferences, interaction history, and learned context. Zero setup required.
Already have a user profile system, CRM, or data warehouse? Plug it in via API. Aster reads from your memory layer at session start.
Remembers patient history, medications, allergies, past visits. No repeating yourself every appointment.
Knows past tickets, resolution history, account tier, and previous frustrations. Skips "tell me your issue from scratch."
Tracks risk profile, life events, portfolio preferences over time. Advice evolves as circumstances change.
Tracks mastery levels, learning pace, preferred explanation style. Each session adapts to where the student left off.
Signed up for Growth plan. Primary use: team reporting. 12-person team. Preferred communication: concise, no fluff.
Billing issue, double charged. Resolved with refund. Frustrated tone. Flagged: sensitive to billing topics.
Asked about cohort analysis. Noted: exports to Google Sheets. Power user behavior. Likely upgrade candidate.
Aster loaded full memory before conversation started. Knew about billing sensitivity, feature gap, and communication preference. Adapted tone and approach accordingly.
Drop your brand book, style guide, tone of voice document, or content standards. Aster extracts and applies them automatically.
Formal vs. casual, warm vs. direct, playful vs. professional. Aster calibrates every generated question and response to match your voice spectrum.
Your preferred terms, banned words, industry jargon, product names. "Customers" not "users." "Team members" not "employees." Always enforced.
What your brand stands for. Topics to avoid. Sensitivity guidelines. Inclusive language requirements. All baked into the system prompt and validated in evals.
Question length limits, reading level targets, formatting rules, emoji usage, sentence structure preferences. Your content design team sets the rules, Aster follows them.
Aster parses your uploaded guidelines into a structured brand profile: tone vectors, terminology dictionary, value constraints, and content rules.
Brand profile is woven into the system prompt that drives question generation. Every LLM call carries your brand DNA as a first-class constraint.
Before any flow goes live, Aster runs a brand eval suite: synthetic conversations scored against your guidelines by an LLM-as-judge. Checks tone, vocabulary, values compliance, reading level, and sensitivity.
In production, a sample of live sessions is scored on brand adherence. Drift alerts fire if scores drop. Auto-refinement adjusts prompts to stay aligned.
Dashboard showing real-time brand compliance across all your deployed flows. Drill into individual sessions. Export for your brand team's review.
Inbound & outbound. Score, qualify, and route leads dynamically with full context.
Highest DemandIntelligent triage. Resolve or route with fewer questions, higher accuracy.
All IndustriesOnboarding, project scoping, needs assessment. Personalized to each user.
Conversion LiftSDR-quality qualification at scale. Personalized discovery via AI.
RevenueAdaptive quoting, risk assessment, KYC. Compliance-aware flows.
RegulatedSymptom assessment, urgency scoring, care routing. Context-aware.
High ImpactAdaptive testing, placement, learning path personalization.
AdaptiveCandidate screening, skills assessment, culture fit. High volume.
ScaleBehavioral-triggered NPS, CSAT, feature research. Memory-aware — never asks what it already knows.
Replaces SprigJoin the private beta. Deploy intelligent, brand-consistent, self-improving conversations in hours, not months.