About Recursive Labs
Building the future of artificial intelligence through rigorous research, transparent development, and products that amplify human potential.
Our Mission
Recursive Labs is an artificial intelligence research and development organization committed to advancing the state of AI technology while maintaining unwavering focus on safety, transparency, and human benefit. We believe that the most powerful AI systems emerge not from purely automated solutions but from thoughtful human-AI collaboration that amplifies human intelligence, creativity, and decision-making capabilities.
Our research spans fundamental challenges in AI safety and alignment, advanced reasoning architectures, interpretability and explainability, fairness and ethics, and human-AI collaboration. We translate this research into practical products that deliver genuine value while adhering to rigorous safety standards and transparent operational practices.
Who We Are
Research-Driven Organization
Recursive Labs operates at the intersection of cutting-edge AI research and practical product development. Our team comprises machine learning researchers, AI safety specialists, software engineers, product designers, and domain experts from diverse fields including cognitive science, computer science, ethics, and human-computer interaction.
We maintain active research programs investigating fundamental challenges in artificial intelligence: developing robust alignment techniques ensuring AI systems pursue intended objectives, advancing interpretability methods making AI decision-making transparent and auditable, creating reasoning architectures enabling genuine problem-solving rather than mere pattern matching, and exploring collaboration paradigms that augment rather than replace human capabilities.
Commitment to Transparency
We believe transparency serves as foundation for trustworthy AI. Our commitment extends beyond technical explainability to encompass organizational transparency: we openly publish research findings, document system limitations and capabilities, engage with external auditors for independent evaluation, and maintain clear communication about both achievements and ongoing challenges.
This transparency enables stakeholders to make informed decisions about AI deployment, supports collective progress through shared knowledge, builds public trust through demonstrable accountability, and creates feedback loops improving our systems through external scrutiny and collaboration.
Safety-First Development
Every capability increase undergoes rigorous safety evaluation before deployment. We implement multiple defense layers: comprehensive input and output filtering, behavioral monitoring detecting anomalous patterns, rate limiting preventing exploitation, staged deployment enabling gradual rollout with rapid rollback capabilities, and continuous monitoring for distributional shift or emerging misalignment.
Our development philosophy prioritizes safety over speed, understanding over mere performance metrics, and long-term reliability over short-term competitive advantage. We invest substantially in fundamental safety research, recognizing that robust AI alignment requires continued innovation as systems grow in capability and deployment scope.
Our Products
Recursive Labs develops three flagship products translating our research into practical tools that enhance human capabilities across conversational AI, creative generation, and knowledge infrastructure.
Phractal (Phi)
Conversational AI Platform
Phractal represents our flagship conversational AI platform enabling natural language interaction with advanced language models for knowledge work, creative tasks, analytical reasoning, and complex problem-solving. Unlike conventional chatbot interfaces offering single-model access, Phractal implements intelligent model routing—analyzing each query to determine optimal AI variant based on task type, complexity, context requirements, and performance characteristics.
Core Capabilities
- Intelligent Model Routing: Automatic selection among GPT-5 variants (Nano for simple queries, Mini for medium complexity, Full for advanced reasoning) optimizing response quality, latency, and computational efficiency based on query characteristics.
- Extended Thinking Modes: Configurable reasoning depth enabling systems to allocate additional computation for complex problems, generating explicit reasoning traces, exploring multiple solution paths, and verifying conclusions through self-critique.
- Multi-Modal Understanding: Native vision capabilities processing images, charts, diagrams, and documents alongside text, enabling comprehensive analysis of visual information integrated with conversational context.
- Web-Enhanced Responses: Real-time web search integration providing current information, fact-checking capabilities, citation of sources, and grounding of responses in up-to-date knowledge beyond training data.
- Contextual Memory: Sophisticated conversation management maintaining coherent context across extended dialogues, intelligent summarization preventing context overflow, and selective retrieval of relevant prior interactions.
Technical Infrastructure
Phractal's architecture combines multiple technological components: FastAPI backend implementing model routing logic and conversation management, Redis caching for high-performance session state, PostgreSQL with vector extensions for persistent storage and semantic search, Server-Sent Events (SSE) enabling real-time streaming responses, and comprehensive monitoring infrastructure tracking performance, costs, and quality metrics.
The platform implements sophisticated safety measures including prompt injection detection, output filtering for harmful content, behavioral anomaly detection, rate limiting preventing abuse, and comprehensive audit logging supporting accountability and continuous improvement.
Use Cases
- Research and Analysis: Literature review, hypothesis generation, data analysis interpretation, and synthesis of complex information across domains.
- Creative Writing: Ideation, drafting, editing assistance, style refinement, and creative exploration while preserving authorial voice and intent.
- Technical Problem-Solving: Code generation and debugging, architectural design discussions, algorithm explanation, and technical documentation.
- Learning and Education: Concept explanation, interactive tutoring, practice problem generation, and personalized learning path recommendations.
- Decision Support: Option analysis, risk assessment, scenario modeling, and structured reasoning about complex decisions.
Mozaic
Infinite Canvas Generation Platform
Mozaic transforms creative ideation and visual communication through AI-powered infinite canvas generation. Moving beyond simple image creation, Mozaic enables users to build comprehensive visual narratives, interactive diagrams, conceptual frameworks, and multimedia presentations through natural language description combined with intelligent layout, composition, and content generation.
Core Capabilities
- Infinite Canvas Architecture: Unbounded workspace supporting unlimited content expansion, dynamic zooming and panning, spatial organization of related concepts, and hierarchical information structures enabling both overview and detailed exploration.
- Multi-Format Generation: Integrated creation of images, flowcharts, mindmaps, diagrams, charts, videos, and text within unified canvas, maintaining visual coherence and conceptual relationships across diverse content types.
- Intelligent Layout: Automatic spatial arrangement optimizing readability, visual flow, relationship clarity, and aesthetic composition while respecting user preferences and design principles.
- Collaborative Editing: Real-time multi-user collaboration, version history tracking changes, commenting and annotation capabilities, and conflict resolution maintaining canvas coherence.
- Export and Integration: High-resolution image export, interactive web embedding, presentation mode with guided navigation, and API access enabling programmatic canvas generation and manipulation.
Technical Architecture
Mozaic implements sophisticated rendering engine utilizing React Flow for interactive graph visualization, Canvas API for high-performance graphics rendering, WebGL acceleration for smooth interaction at scale, and optimized data structures supporting millions of canvas elements. Content generation leverages multiple AI models: GPT-5 for conceptual understanding and content planning, Gemini Flash for image generation, specialized models for diagram and chart creation, and video synthesis capabilities for dynamic content.
The platform employs intelligent caching strategies minimizing regeneration, differential updates reducing bandwidth requirements, progressive loading enabling responsive interaction with large canvases, and cloud storage integration via Supabase for reliable persistence and sharing.
Use Cases
- Strategic Planning: Business model canvases, strategy maps, organizational diagrams, and roadmap visualization.
- Educational Content: Concept maps, lesson plan visualization, interactive learning materials, and educational video generation.
- Research Communication: Literature synthesis visualization, methodology flowcharts, results presentation, and research poster creation.
- Creative Ideation: Story boarding, character development, world-building visualization, and creative project planning.
- Technical Documentation: System architecture diagrams, workflow visualization, API documentation, and troubleshooting guides.
Snowflake
Pre-Built RAG Infrastructure
Snowflake provides enterprise-grade Retrieval-Augmented Generation (RAG) infrastructure enabling organizations to deploy AI systems grounded in their proprietary knowledge bases, documents, and data sources. Unlike generic language models operating solely on pre-training knowledge, Snowflake-powered applications combine neural language understanding with precise retrieval from organizational knowledge, ensuring responses remain accurate, current, and verifiable.
Core Capabilities
- Knowledge Ingestion Pipeline: Automated processing of diverse document formats (PDF, Word, Excel, PowerPoint, HTML, Markdown), intelligent chunking preserving semantic coherence, metadata extraction and indexing, and incremental updates maintaining knowledge base currency.
- Semantic Search: Vector embeddings enabling meaning-based retrieval beyond keyword matching, hybrid search combining semantic and lexical approaches, metadata filtering for precise scoping, and relevance ranking optimizing retrieved context quality.
- Context Management: Intelligent selection of retrieved passages balancing comprehensiveness and token efficiency, reranking retrieved candidates by relevance to specific queries, deduplication removing redundant information, and citation tracking linking responses to source documents.
- Quality Assurance: Response grounding verification ensuring answers derive from retrieved context, hallucination detection flagging unsupported claims, confidence scoring indicating answer reliability, and fallback mechanisms for queries exceeding knowledge base coverage.
- Access Control: Document-level permissions ensuring users only access authorized information, role-based access control (RBAC) managing organizational hierarchies, audit logging tracking information access, and compliance features supporting regulatory requirements.
Technical Infrastructure
Snowflake's architecture combines PostgreSQL with pgvector extension for scalable vector storage and similarity search, advanced embedding models generating high-quality semantic representations, intelligent chunking algorithms optimizing retrieval granularity, and caching infrastructure minimizing embedding computation costs. The retrieval pipeline implements sophisticated reranking using cross-encoder models, query expansion improving recall, and contextual compression maximizing relevant information density within token budgets.
Security infrastructure includes encryption at rest and in transit (AES-256), secure multi-tenancy isolating organizational data, differential privacy techniques protecting sensitive information, and comprehensive compliance tooling supporting GDPR, CCPA, SOC 2, and industry-specific regulations.
Use Cases
- Enterprise Knowledge Management: Internal documentation search, institutional knowledge preservation, onboarding assistance, and expertise discovery.
- Customer Support: Automated support ticket resolution, product documentation query, troubleshooting guidance, and FAQ automation.
- Research and Development: Literature review automation, prior art search, technical specification retrieval, and cross-project knowledge sharing.
- Compliance and Legal: Regulatory documentation search, contract analysis, policy question answering, and compliance verification.
- Healthcare and Medical: Clinical guideline retrieval, medical literature search, patient education, and treatment protocol assistance.
Deployment Options
Snowflake offers flexible deployment models: fully managed cloud service for rapid deployment and automatic scaling, hybrid deployment balancing cloud convenience with on-premise data residency requirements, and fully on-premise installation for organizations with strict data sovereignty constraints. All deployment options maintain consistent API interfaces, enabling seamless migration across deployment models as organizational needs evolve.
Research Excellence
Active Research Programs
Our research organization maintains focused programs investigating fundamental AI challenges with direct application to product development:
- AI Safety and Alignment: Developing robust techniques ensuring AI systems pursue intended objectives, remain corrigible and amenable to correction, and maintain alignment as capabilities scale.
- Advanced Reasoning: Creating architectures enabling genuine problem-solving through extended thinking, systematic exploration of solution spaces, and transparent reasoning processes.
- Transparency and Explainability: Advancing interpretability methods making AI decision-making understandable, developing faithful explanation techniques, and building audit infrastructure.
- Fairness and Ethics: Addressing algorithmic bias, ensuring equitable performance across populations, and developing frameworks for ethical AI deployment.
- Human-AI Collaboration: Designing interaction paradigms that augment human capabilities, preserve agency and expertise, and enable productive partnerships.
- Open Science: Contributing to collective knowledge through publication, benchmark development, and collaborative research initiatives.
Publications and Contributions
We actively publish research findings in peer-reviewed conferences and journals, contribute to open-source AI safety and alignment tools, develop and release evaluation benchmarks for community use, and engage with academic institutions through research collaborations and joint projects. This commitment to open research accelerates collective progress while maintaining competitive product development through superior execution and integration.
External Validation
We engage independent third-party auditors to evaluate our systems, providing access to model internals, training data (where privacy-preserving), and comprehensive operational logs. This external validation offers accountability beyond self-assessment, helps identify blind spots in internal evaluation, and builds public trust through demonstrable transparency. Audit findings inform continuous improvement cycles, ensuring systems meet stated safety and performance criteria.
Values and Principles
Safety Over Speed
We prioritize thorough safety evaluation over rapid capability deployment. Every significant capability increase undergoes rigorous testing, adversarial evaluation, and safety verification before release. This approach may slow competitive feature releases but ensures deployed systems meet high safety and reliability standards.
Transparency and Accountability
We maintain transparency about system capabilities and limitations, publish documentation of known failure modes, openly share research methodologies and findings, engage with external auditors for independent verification, and acknowledge uncertainties rather than presenting false confidence.
Augmentation Over Automation
Our products emphasize augmenting human capabilities rather than replacing human judgment. We design for collaboration that preserves human agency, maintains and develops expertise rather than creating dependence, enables informed oversight through transparency, and respects human creative ownership and decision authority.
Long-Term Thinking
We consider long-term implications of AI development beyond immediate product utility. This includes investing in fundamental safety research without immediate commercial application, maintaining standards even when competitive pressure might suggest compromise, addressing potential future risks from increasingly capable systems, and contributing to collective safety progress rather than hoarding insights for competitive advantage.
Inclusive Benefit
We strive to ensure AI benefits extend broadly rather than concentrating narrowly. This means developing products accessible across economic strata, evaluating fairness across demographic groups, considering global rather than merely local impacts, and engaging with diverse stakeholders in development processes.
Building the Future of AI
Recursive Labs represents a distinctive approach to AI development—combining rigorous research with practical product creation, maintaining unwavering commitment to safety and transparency, and focusing on augmentation that amplifies human potential rather than automation that diminishes human roles.
Through Phractal's intelligent conversational AI, Mozaic's creative canvas generation, and Snowflake's knowledge-grounded reasoning infrastructure, we deliver practical value while advancing the technical and ethical standards for trustworthy AI. Our research programs ensure products benefit from cutting-edge capabilities while maintaining robust safety guarantees and transparent operation.
We invite collaboration with researchers advancing AI safety and capability frontiers, organizations seeking to deploy trustworthy AI systems, and individuals interested in AI that genuinely enhances human flourishing. Together, we can build an AI-enabled future that amplifies the best of human intelligence, creativity, and values.
Join Us
Experience the future of AI through our products and research.
