The reasoning layer
AI agents are missing
Pre-inference context reasoning. The overlooked layer between your data and your AI.
Pre-inference context reasoning is overlooked
Everyone is building better models. Better agents. Better memory. Nobody is building the layer that reasons about what context the agent actually needs before inference begins.
Today's AI agents are brilliant reasoners. Give them the right context and they'll synthesize, analyze, and conclude with remarkable accuracy. But gathering that context? That's where everything breaks down.
Enterprise data lives in terabytes, sometimes petabytes, scattered across departments, tools, and formats. You can't feed it all into a context window. So the industry invented RAG: store documents as vector embeddings, retrieve by semantic similarity. Embed, retrieve, generate. Three steps. No reasoning anywhere in between.
This works for simple lookups. It fails the moment a question requires connecting insights from across different domains, teams, or timelines. Most enterprise decisions require exactly that.
Vector similarity is not cognition
Decades of cognitive science research on how humans retrieve and connect knowledge was completely ignored when building retrieval systems. The brain doesn't do cosine similarity. It structures, connects, inhibits irrelevant paths, consolidates over time, and reasons iteratively before arriving at a conclusion.
RAG standardized on embed-retrieve-generate in 2023 and the industry moved on. The assumption was that better embeddings or bigger context windows would eventually solve multi-hop reasoning. They won't. Transformer architecture itself degrades beyond 2-3 reasoning hops. This isn't a training data problem. It's a fundamental architectural limitation that requires an external reasoning layer.
The market is solving memory. The problem is reasoning.
Every startup building context infrastructure for AI agents is focused on the same thing: long-term memory storage. Store what the agent saw, recall it later. But storage is a solved problem. A vector store already does this.
The hard part was never remembering. It's connecting what you remember into insights that actually help the agent make a decision. None of these players attempt multi-hop reasoning, query decomposition, or cross-document synthesis. They're building better filing cabinets. The industry needs the analyst who reads the files.
Pre-inference context reasoning
Vrin adds the missing layer between your data and your AI agents. Before the model sees a single token, Vrin has already understood the crux of the query, traversed a structured knowledge graph, connected insights across domains the user never mentioned, and constructed a reasoning chain that gives the AI agent exactly what it needs.
Not retrieval. Not recall. Reasoning about what context is needed, why it's connected, and how to present it so the AI agent arrives at the right conclusion.
The result: AI responses that are accurate, cited, traceable to specific facts from specific documents, with confidence scores on every claim. Already outperforming the best published academic systems on multi-hop reasoning benchmarks.
Industries where connecting the dots is the job
Vrin serves teams that face complex, multi-step, cross-domain questions every single day to make strategic decisions that move the business.
Core Industries
Strategy Consulting & Corporate Strategy
Every engagement requires synthesizing market trends, competitor behavior, cost structures, and risk factors across dozens of sources into a single recommendation.
Investment Management & Venture Capital
Investment decisions depend on connecting incomplete signals: founder quality, market timing, tech feasibility, macro trends, comparable valuations. No single document holds the answer.
Clinical Medicine & Diagnostics
Diagnosis is chaining symptoms, test results, medical history, drug interactions, and clinical guidelines. Life-or-death multi-hop reasoning, every day.
Complex Litigation & Corporate Law
Legal arguments require linking statutes, precedents, case facts, and regulatory filings across jurisdictions. One missed connection changes the outcome.
Product Management at Tech Companies
Balancing user behavior data, funnel metrics, feature gaps, engineering constraints, and business goals. Every prioritization decision is a 4+ hop problem.
Strong Fit
Scientific Research & R&D
Hypothesis building across experiments, anomalies, model revisions, and prior literature. Discovery lives in the connections.
Complex Engineering
Aerospace, energy, infrastructure. Interdependent systems where one design choice cascades through materials, thermal effects, safety, and cost.
Supply Chain & Operations Strategy
Supplier delays, inventory levels, distribution networks, demand signals, revenue impact. Multi-hop reasoning under time pressure.
Embeddable by design
SDK, MCP server, REST API. Vrin drops into any AI agent stack. Three deployment models to match your security requirements.
Cloud
Fully managed on Vrin infrastructure. Start in minutes.
Hybrid
Your data stays in your cloud. Vrin compute on ours.
Private VPC
Everything in your account. Nothing leaves your perimeter.
Give your AI agents the ability to reason
Start free. See the difference pre-inference reasoning makes.