Nexus products share a common backbone of advanced AI techniques. Not wrappers — real engineering depth that solves genuinely hard problems.
These aren't features on a slide deck. They're techniques we've built, tested, and deployed in production for real businesses.
LLMs exhibit a fundamental asymmetry: they are mediocre generators individually but excellent evaluators when given context. Nexus exploits this by sending questions to multiple models, then using a synthesis model in evaluation mode — where it is disproportionately strong.
When multiple models agree with high confidence, that's a fundamentally different signal than a single model guessing. Multi-pass verification produces higher accuracy than any individual model.
Every answer in the Nexus system is fully traceable. You can follow the complete reasoning chain from question to final synthesis — seeing how each model responded, where they agreed, where they diverged, and how the synthesis resolved differences.
Citations, confidence scores, and log probabilities are not afterthoughts. They are deeply baked into every tool, making Nexus output verifiable and auditable.
Open source models give us access to KV cache internals and log probabilities that proprietary APIs don't expose. KV cache optimization turns expensive inference into affordable at-scale processing — loading a document once and asking unlimited follow-up questions at near-zero marginal cost.
Log probabilities provide genuine confidence scoring, not heuristic estimates. When a model is 95% confident in a token, that means something precise and measurable.
Not every task needs a frontier model. Extraction runs on fast, cheap models that excel at structured tasks. Classification happens on fine-tuned specialists. Synthesis and reasoning run on frontier models. Each stage uses the right tool for the job.
This staged approach dramatically reduces costs while maintaining — or even improving — accuracy compared to sending everything to the most expensive model.
LoRA (Low-Rank Adaptation) adapters don't add knowledge to a model — they make the model better at extracting and using the information you already have. This is a critical distinction. You're not teaching the model new facts; you're teaching it new skills.
Combined with frontier models generating synthetic training data, LoRA fine-tuning produces specialized models that outperform general-purpose models on your specific tasks at a fraction of the cost.
Embeddings, vector databases, and knowledge graphs form the retrieval backbone. But our approach to RAG is different: we use techniques that avoid chunking wherever possible, preserving document structure and relationships that traditional chunk-and-retrieve pipelines destroy.
Knowledge graphs add a structured layer on top of vector search, capturing typed relationships between concepts that pure similarity search misses. Combined with embeddings fine-tuned for your domain, retrieval accuracy improves significantly.
The platform layer powers specialized capabilities that can be used standalone or as building blocks for custom solutions.
Ingest, understand, and query complex documents. Handles scans, handwriting, forms, stamps, and mixed media with AI-powered extraction and validation.
Learn more →A knowledge system on top of your data. Not just documents — bring in any data source. Persistent context across conversations, projects, and teams.
Learn more →Access Nexus capabilities programmatically. Consensus engine, research engine, document intelligence — all available via a clean REST API.
Learn more →Agent orchestration and multi-step pipelines. Coordinate short-term and long-term agents with dependencies, approval gates, and monitoring.
Learn more →Each product has its own UI and marketing. But they share authentication, billing, conversation history, and the full capabilities layer. Build once, deploy everywhere.
Use our products directly, integrate via API, or let us build a custom solution on this infrastructure for your business.