enSmaller
enSmaller provides an AI reliability platform that enforces evidence-based answer construction for enterprise LLM workflows. Unlike conventional retrieval-augmented generation systems that retrieve first and verify later, enSmaller defines required answer components before generation, sends only necessary context to the model, and independently verifies each part of the output. This approach reduces token costs, eliminates hallucinated guesses, and surfaces missing information with specific explanations.
- Artificial Intelligence
- Developer Tools
- Enterprise Software
- Software Only
Funding
Founders
Product
Problem
Large language models generate confident but ungrounded responses because they optimize for likelihood and coherence rather than factual accuracy. Standard retrieval-augmented generation approaches retrieve similar content, send everything to the model, and check answers only after generation—leading to high costs, hallucinated guesses when information is missing, and outputs that are difficult to trust in production environments.
Solution
enSmaller changes how AI answers are constructed by defining what the answer must include before the model is called, then sending only the minimal evidence needed to satisfy those requirements. The platform verifies that sufficient evidence exists before generation begins and independently checks each part of the output against the source material. If information is missing, enSmaller explicitly identifies what is absent and why, rather than allowing the model to guess. This governance-first approach reduces token consumption, improves answer reliability, and makes AI workflows deployable, scalable, and debuggable in production settings.
Target Audience
Primary customers are enterprise engineering and AI teams deploying LLM-based workflows in production who need reliable, cost-efficient answers and clear audit trails for compliance and debugging.
Features
- Pre-generation answer specification that defines required content components before the model is invoked
- Minimal-context retrieval that sends only necessary evidence to the model, reducing token costs
- Pre-generation evidence sufficiency checks that prevent generation when required information is absent
- Post-generation independent verification of each answer component against source material
- Explicit missing-information reporting that identifies gaps and explains why they occurred
- Production-oriented architecture designed for workflow governance, scalability, and debugging