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Uvi is building an enterprise context and reasoning layer for AI agents. Its central idea is that coding and operations agents waste expensive model calls reconstructing decisions scattered across tickets, pull requests, Slack, code, feature flags and documents. Uvi’s proposed remedy is a Decision Graph that connects those fragments and returns the company’s recorded reasoning—what changed, why it changed, who owns it, what is blocked and which evidence supports the answer.
What Uvi is trying to fix
Most agents can retrieve text, but retrieval does not automatically explain how an organization reached a decision. A developer asking why a feature flag was disabled may need a ticket, the implementing pull request, a Slack discussion, an incident note and a later rollback. Those records often use different names for the same service or project and sit behind separate permissions.
Uvi says agents repeatedly pay to rebuild that context at the start of a task or session. Its product positioning describes this as an “agent amnesia” cost: context is reconstructed instead of reused. The company’s website claims that teams spend 12 million tokens a week resolving context and six hours a week maintaining the wiring between systems. Those are Uvi’s 2026 estimates, not independently audited measurements.
The company’s product slogan captures its distinction from larger prompts or more documents: “More context won’t fix your agents.The ‘why’ behind it will.”
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What a Decision Graph contains
Uvi describes its graph as a shared layer underneath agents such as Claude Code, Cursor and Cowork. It resolves internal vocabulary, entity relationships and user intent while respecting existing access rights. Rather than treating a document as the unit of knowledge, the graph links entities and events across systems.
Decisions and their rationale
The graph is intended to represent a decision, the evidence behind it, the trigger that caused it, the people or teams involved, resulting blockers and dependencies. For example, an agent could connect a ticket changing an API, the pull request that implemented it, a feature flag that controlled rollout, and a Slack thread explaining why the rollout was paused.
Cross-system relationships
Uvi says it maps tickets, pull requests, feature flags and discussion threads into a graph that agents can follow. Its July 7, 2026 explanation by CEO and co-founder Gilad Salinger draws the boundary this way: “Memory recalls what the agent has seen. A Reasoning Layer returns what your company has decided.”
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteDecision Graph versus memory and RAG
Agent-memory libraries commonly preserve conversation history, summaries or facts that an application explicitly stores. Retrieval-augmented generation (RAG) usually selects passages from indexed documents for a particular query. Uvi is positioning its product as enterprise infrastructure that joins live relationships across many systems and reconstructs decisions, rather than as a memory store or a single-document search layer.
| Question | Typical memory or RAG approach | Uvi’s stated approach |
|---|---|---|
| Primitive | Conversation history, summaries or retrieved passages | Enterprise Decision Graph of entities, decisions and dependencies |
| Data scope | Agent-run history or selected ingested sources | Joins code, tickets, pull requests, Slack and internal services |
| Question type | “What did the user tell me?” | “What is blocking this initiative, who owns it and why?” |
| Governance | Controls implemented by the application using the memory or index | Uvi says it mirrors source access controls and enforces permissions at graph level |
| Deployment | Often a library or hosted API | Uvi says it supports enterprise VPC and on-premises deployment |
This is a product-positioning comparison, not proof that every memory or RAG implementation has the limitations shown. The practical test is whether a system can preserve relationships and authorization when the answer spans several sources.
How an agent would use Uvi
- Connect approved systems. The organization supplies the code, ticketing, pull-request, chat, feature-flag and internal-service sources that an agent is allowed to use.
- Resolve company vocabulary. Uvi says it maps different names and identifiers to the same project, service, team or person.
- Build decision relationships. Events and records are linked so that a change, its rationale, owners, dependencies and blockers can be traversed together.
- Ask the agent a cross-system question. A coding or operations agent can query the resulting context through the Uvi layer instead of separately searching each system.
- Apply source permissions. Uvi says graph-level enforcement prevents an agent from using relationships or evidence that the requesting user could not access in the original system.
Exact connector coverage, refresh intervals, retention controls and configuration steps are not stated in the public material cited here, so teams would need those details during an enterprise evaluation.
Performance and cost claims
Uvi reports the following results on its official product page:
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| Claim | How to interpret it |
|---|---|
| 97 wins out of 100 head-to-head comparisons versus an MCP-enabled GPT | Vendor-reported benchmark result; methodology and test set are not publicly established in the available coverage |
| 2× lower latency than RAG baselines | Vendor-reported comparison; baseline configuration and workload are not specified here |
| 8× lower cost per query than a DIY agent on the same model | Vendor-reported economics claim, not an independently replicated total-cost study |
| 12 million tokens per week spent resolving context | Uvi’s 2026 estimate of the problem it targets |
| Six hours per week maintaining wiring | Uvi’s 2026 estimate of engineering effort |
No independent benchmark methodology or third-party replication for the 97/100, 2× or 8× figures was identified in the available reporting. Buyers should request the dataset, prompt suite, latency percentiles, token accounting and reproducible baseline before using the numbers in a business case.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who Uvi is selling to
Uvi identifies R&D, platform and head-of-AI teams as its primary enterprise buyers. That differs from many agent-memory tools aimed at developers who are adding memory to an individual agent product. The proposed buyer is the organization that must make shared context available across many agents while preserving permissions and reducing duplicate integration work.
Customer testimonials on Uvi’s site describe this use case. Tal Mor, VP of R&D at Melio, says the graph helps teams catch up on projects and gives Claude a way to reason over scattered decisions. Greg Sirota, Knowledge Manager at Global-e, says the company adopted Uvi to unify fragmented knowledge and reduce reliance on individual experts. These are customer statements published by Uvi, not independent evaluations.
Funding, availability and unanswered questions
Coverage published in October 2026 reports that Uvi raised a $7.5 million seed round from CT Innovations, Cardumen, Mana VC and 91 Ventures. Startuply reports that Gilad Salinger and Dror Wolmer founded the company in 2023 and that it emerged from stealth in October 2026. Startuply also notes that independent validation of the performance claims remains a milestone to watch.
Public pricing, contract terms and per-query rates are not disclosed in the cited sources. There is therefore no supported answer to “How much does Uvi cost per query?” beyond Uvi’s own relative claim about DIY-agent cost. Likewise, Uvi says it offers VPC and on-premises deployment, but public material does not specify supported cloud providers, hardware requirements, air-gapped operation or a complete deployment checklist.
What to verify before adopting it
- Which systems and versions have production connectors, and how quickly changes propagate into the graph?
- Can administrators inspect the evidence and decision links used to produce an answer?
- How are deleted records, revoked permissions and conflicting decisions handled?
- What are the measured latency and token costs for your own repositories and ticket volume?
- What deployment, data-residency, encryption and support terms apply to VPC or on-premises installations?
- Can the system export or delete graph data if the organization changes platforms?
Uvi’s thesis is straightforward: agents need an organizational record of decisions, not merely a longer transcript or a larger document index. Its Decision Graph, permission model and cross-system scope are designed around that thesis. The concept addresses a real integration and token-cost problem, but the strongest speed and savings figures remain company claims until the underlying tests and customer results are independently reproducible.
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