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Uvi is building an enterprise reasoning layer designed to stop AI agents from reconstructing the same company context on every task. Its central product, the Decision Graph, links tickets, pull requests, code, feature flags, Slack threads and internal services so an agent can recover not only what happened, but why a decision was made, who owns it and what is blocking it.
Uvi’s pitch is that agent “amnesia” is an infrastructure problem, not simply a prompt-quality problem. The company says enterprises can spend 12 million tokens each week resolving context and six hours of engineering time maintaining the wiring that connects an agent to scattered systems. Those figures are Uvi’s 2026 claims, not independently audited measurements.
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What Uvi says it is building
Uvi describes its software as infrastructure beneath agents such as Claude Code, Cursor and Cowork. It resolves internal vocabulary, entity relationships and user intent under existing permissions, then presents the resulting context to the agent. The company’s product positioning is: “More context won’t fix your agents. The ‘why’ behind it will.”
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThe practical goal is to connect fragments that normally live in separate tools. A ticket may describe a symptom, a pull request may contain the implementation, a Slack thread may explain the trade-off, and a feature flag may show the rollout status. Uvi says its graph joins those records and reconstructs the reasoning that was never explicitly written in one place.
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What a Decision Graph adds
Uvi’s July 7, 2026 explanation draws a line between ordinary agent memory and its proposed reasoning layer: “Memory recalls what the agent has seen. A Reasoning Layer returns what your company has decided.”
Memory and retrieval
Conversational memory usually stores prior exchanges or facts an agent has ingested. Retrieval-augmented generation (RAG) searches documents or indexed records and supplies relevant passages. Both approaches can answer questions about information that was captured, but they may leave the agent to infer relationships among separate records.
Decision Graph context
Uvi says its graph treats decisions, owners, triggers, blockers and supporting evidence as first-class context. That is intended to answer operational questions such as:
- What changed in this project, and why?
- Who owns the dependency that is blocking the release?
- Which pull request implemented the decision described in a Slack discussion?
- Which feature flag or service is connected to the affected ticket?
The distinction is important for engineering organizations where the answer is distributed across systems rather than contained in a single document.
How the system is meant to work
- Connect sources: Uvi says it maps data from code, tickets, pull requests, feature flags, Slack and internal services.
- Resolve entities and vocabulary: The service links project names, services, people and internal terms that may be written differently across systems.
- Build relationships: Decisions, dependencies, ownership, triggers and blockers are represented in a graph rather than treated as isolated passages.
- Enforce access: Uvi says permissions are enforced at graph level and that source access controls can be mirrored, so an agent should not receive records its user cannot access.
- Serve agent queries: Coding and enterprise agents can use the connected context to reason about a task without repeatedly rebuilding the company map from raw sources.
Who Uvi is targeting
Uvi identifies R&D, platform and head-of-AI teams as its primary enterprise buyers. That differs from many agent-memory libraries and hosted APIs aimed at developers building agent products. The buyer question is therefore less “How do I save this chatbot’s conversation?” and more “How do we give every approved agent a governed map of how our engineering organization works?”
Deployment and governance
Uvi says it supports on-premises and VPC deployment, a requirement for organizations that cannot send internal engineering records to a general public service. It also claims graph-level permission enforcement. Public technical documentation detailing supported cloud environments, isolation architecture, retention, connector coverage and audit controls was not established in the available material, so teams should request those specifics during evaluation.
Performance and cost claims
Uvi reports the following results on its official site:
| Claim | What Uvi reports | How to interpret it |
|---|---|---|
| Head-to-head benchmark | 97 wins out of 100 against MCP-enabled GPT | Vendor-reported; methodology and independent replication were not provided. |
| Latency | 2× lower than RAG baselines | Vendor-reported comparison; baseline configuration is not publicly established here. |
| Query cost | 8× lower than a DIY agent using the same model | Vendor-reported and likely dependent on workload, model and implementation. |
| Context reconstruction | 12 million tokens per week | Uvi’s 2026 estimate of enterprise usage, not an audited industry figure. |
| Maintenance effort | 6 engineer-hours per week | Uvi’s 2026 estimate for maintaining integrations and wiring. |
These numbers indicate the economic problem Uvi is pursuing—repeated token usage and integration maintenance—but they should not be treated as a neutral benchmark. No public pricing, per-query rate, contract minimum or referral terms were identified. A buyer would need a workload-specific estimate covering ingestion, graph storage, model calls, connectors and deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Funding and company timeline
Independent funding coverage reports a $7.5 million seed round from CT Innovations, Cardumen, Mana VC and 91 Ventures in 2026. Startuply reports that Gilad Salinger and Dror Wolmer founded Uvi in 2023 and that the company emerged from stealth in October 2026. Startuply also identifies third-party validation of the performance claims as an outstanding milestone.
What an evaluation should verify
- Whether connectors capture the systems your teams actually use and update changes quickly enough for operational work.
- How identity, group membership and source ACLs propagate into graph queries.
- Whether the graph can show evidence for an inferred relationship, not just return an unexplained answer.
- Latency and token consumption on your own tickets, repositories and incident workflows.
- On-premises or VPC architecture, data retention, encryption, logging and deletion procedures.
- Total cost compared with maintaining your current integrations and paying for repeated context windows.
Bottom line for developers
Uvi is not presenting itself as another chat-history store. It is proposing a governed Decision Graph that joins enterprise records and returns the rationale behind decisions. That could help coding agents answer “why” questions that document search alone cannot reliably resolve. The concept and deployment claims are clear; the 97/100 benchmark, 2× latency improvement and 8× cost reduction remain company-reported until Uvi publishes methodology and independent results.
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