Synaptosearch Product
Discover what you're really looking for. InnerMatch learns how features interact — not just how close they are — revealing hidden compatibility across any domain.
The Problem
Most AI matching systems are built on a single, flawed assumption.
The Breakthrough
Instead of measuring vector distance, InnerMatch learns how features relate to each other during real-world use — capturing patterns that static similarity cannot.
Phase I — Current Capabilities
Learns continuously from real-time feedback. No offline training cycles. Fast adaptation to new domains with minimal data.
Meaningful match quality from session one. Starts from a principled baseline and refines through every interaction.
Approvals, rejections, rankings, engagement patterns — each signal reshapes future matches. The system evolves with its users.
Add or remove dimensions at runtime without retraining. Far more flexible than locked deep-learning embeddings.
Identifies which feature dimensions truly drive match quality. Surfaces hidden factors users didn't know to ask for.
Career goals change. Learning interests evolve. Personal tastes shift. InnerMatch tracks who the user is becoming.
Supports inhibitive and negative similarity — goes beyond Euclidean distance to model real-world compatibility logic.
Lightweight, computationally efficient model. Scales to high query volumes for production environments.
The Bigger Idea
Traditional recommendation systems attempt to guess what users want. InnerMatch helps users discover what they want — by treating search as a conversation between the user and the data.
With each interaction, the system refines its model of user intent, contextual relevance, and latent preference structures — things users care about but don't yet consciously recognize. Over time it uncovers cultural alignment, communication style compatibility, aesthetic preferences, and hidden knowledge relationships.
Applications
Match candidates on career ambition, cultural alignment, and team compatibility — not just skills. Faster hiring, improved retention, better candidate experience.
Model evolving romantic preferences and uncover compatibility beyond surface traits. Move beyond swipe mechanics toward relationship discovery.
Guide learners to courses, mentors, study groups, and learning paths based on evolving skills and goals.
Adaptive recommendations that learn deeper preference structures — generating results that feel surprisingly relevant.
Help employees discover relevant documents, subject-matter experts, and hidden connections across department silos.
Support information needs that begin without a clear question. The system lets users refine their own thinking during interaction.
Phase II — Future Capabilities
Phase II features are in active design. Contact us to discuss early access or co-development.
| Feature | Description |
|---|---|
| Disentangling Component Vector Encoding Module | Learns structured latent representations; separates independent factors of variation for better interpretability and matching precision. |
| Real-Valued Vector Computations | Extends beyond binary feature vectors to continuous-valued relationships — enabling richer semantic similarity. |
| Structured Matrix-Formatted Input | Accepts relational and graph-like data; matching beyond flat vector representations. |
| Connection Masking | Identifies the most valuable subset of feature connections; improves efficiency and explainability. |
| Customizable Optimization Criteria | Domain-specific objective functions; adjustable fairness, diversity, or accuracy constraints for HR, dating, EdTech, and more. |
| Modality-Agnostic Matching | Text, image, and video encoded into unified representations — one engine across all data types. |
| Enterprise-Ready Architecture | SaaS, API, and On-Prem options; privacy, fairness, and compliance built in from the ground up. |
Deployment
Managed matching service — rapid integration, no infrastructure overhead. The fastest path to production.
Start Free TrialIntegrate InnerMatch into any application via developer-friendly API. Pay-per-use pricing, full documentation.
Request API AccessFull local deployment for strict privacy requirements. GDPR, HIPAA, and CCPA ready from day one.
Enterprise InquiryDirect integration into custom AI pipelines. Drop InnerMatch's matching logic into your existing stack.
Developer Inquiry"InnerMatch represents a shift in how AI systems understand similarity. Instead of treating matching as a static geometric problem, the platform treats it as a dynamic learning process shaped by human interaction."
"The long-term vision is to build systems that help people not only find what they want, but also understand themselves better through discovery."