When AI Must Get It Right.
The only proven, enterprise-grade, real-time platform that makes AI in production actually work. Sub-10ms latency. Full ACID consistency. Fifteen years in mission-critical production.
AI did not change what Volt is. It changed how urgently the world needs it.
The Missing Layer Between Signals and Action
Most architectures have the insight. What they lack is the layer that turns it into safe, consistent decisions.
AI reasons from what’s true right now
Volt provides real-time authoritative state via MCP tools so agents query current, ACID-consistent data during reasoning. Not stale snapshots from a warehouse sync. The data an agent reads is the data the next transaction commits against. No gap. No window for wrong answers.
Deterministic authority over probabilistic outputs
AI produces recommendations. Volt decides what actually happens. The same conditions always produce the same outcome: deterministic, consistent, and enforced at scale. That’s not a governance layer. That’s the execution authority.
A single operating mode. Under any load.
A Volt cluster under heavy load behaves the same as one under light load. No cache that needs warming. No replica that can lag. No layer that fails into a bad state. Google’s Spanner publishes 10ms reads and 50ms writes. Volt delivers sub-10ms with full ACID compliance.
Every decision. Explained. Auditable.
Volt records the complete chain atomically: what the agent queried, what data it received, what was recommended, what was decided, and why. Not reconstructed from logs. Written at the moment it happened. The explainability infrastructure regulated industries require.
Three Fears. One Answer.
Every technology leader making decisions about AI right now is navigating three fears simultaneously.
- Getting stuck in pilot mode. Major enterprise technology companies are betting billions on agent-driven infrastructure. Agentic workloads are already hitting production systems harder than anyone anticipated. The enterprises whose AI is in production had the right infrastructure ready before the agents arrived. The gap between AI in a pilot and AI in production is almost always a data infrastructure problem, not a model problem.
- Getting it catastrophically wrong. A wrong fraud decision. A billing error. A network action taken on stale data. AI that gets it wrong does not fail quietly. It fails with confidence, at scale, in production. The cost isn’t a slow page load. It’s millions of dollars, regulatory exposure, and reputational damage that takes years to repair.
- Not being able to explain what happened. Regulators are asking questions AI deployments can’t answer. Why did your system block that transaction? What data did the agent use? What was the account state at the moment of the decision? Most AI architectures record the recommendation. They don’t record the complete chain that produced it.
Volt resolves all three. Deploy AI at scale, with data you can trust, and explain every decision when asked.
<10ms
…Ingest-to-action latency with full ACID compliance and five-nines availability. Google’s Spanner publishes 10ms reads and 50ms writes.
11%
…of enterprises have agentic AI actively running in production today. The bottleneck isn’t the models. It’s the infrastructure underneath them.
20–100×
…more database operations per agent task compared to a human doing the same task. Your existing stack was sized for humans.
$132B
…projected server CPU market by 2030, driven primarily by agentic AI workloads that require more CPU processing power than earlier AI applications.
Build. Run. Operate.
Volt works with AI across three dimensions. Each matters. The one in the middle is where everything changes.
01
Build
TABLE STAKES
AI accelerates how you work with Volt. Schemas, stored procedures, pipelines, and working applications can be generated and iterated in a fraction of the time they used to take. Faster time to value. Less time writing Java. Already in use with major platform builders and system integrators.
The foundation that gets you moving. Available now, in production with major platform builders and system integrators.
02
Run
WHERE VOLT IS IRREPLACEABLE
Runtime use cases where AI and Volt together produce outcomes that neither can achieve alone. Agents need to reason from data that is accurate right now. Volt provides that, and ensures AI recommendations are acted on correctly, at speed, with a complete audit trail. The data the agent queries is the data the next transaction commits against. No gap. No stale state. No wrong answers delivered with confidence.
This is where Volt is the only platform that delivers. No cache layer. No stale data. No wrong answers.
03
Operate
TABLE STAKES
AI improves how Volt runs in production. Ingest logs and metrics, generate faster and more accurate root cause analysis, surface problems before they become critical, and recommend or automate corrective actions. Already in production use with major enterprise customers.
Already in production with major enterprise customers. Faster, more accurate, cheaper than human-led operations.
Why the Architecture Is the Differentiator
Conventional architectures layer streaming brokers, state stores, cache layers, and application logic on top of each other. Each hop adds latency. Each boundary introduces failure modes. Volt collapses that stack into a single execution environment where state, logic, and decision-making live in one place, and the whole system behaves the same under heavy load as it does under light load.
ACID consistency at scale.
Full ACID compliance at millions of transactions per second, across a distributed in-memory cluster, at sub-10ms latency. The data an agent reads during reasoning is the data the next transaction commits against. Not a cached approximation. Not a replica that might be 200ms behind. An architectural guarantee, not an operational promise.
Sub-10ms latency. Under load. Every time.
Not average latency. Consistent performance under sustained high volume, without the tail latency spikes that emerge when cache layers warm and cool. Agent reasoning loops are sensitive to latency in a way human workflows are not. Volt’s in-memory architecture means the data tier is never the bottleneck.
No cache layer. No operational debt.
No cache to manage, no replica lag to monitor, no split-brain scenario to design around. No cache that needs warming, no replica that can lag, no layer that can fail into a bad state. Every layer removed from the architecture is a failure mode that does not happen at 2am.
Fifteen years in production.
The failure modes of a real-time stateful system at enterprise scale are not documented anywhere. They are discovered in production, one at a time, over years. Volt has already found most of them and fixed them. AI can accelerate code. It cannot replace that.
Workflow-level ACID guarantees.
As agents move from read-only to read-write, multi-step agent workflows become distributed transaction problems. VoltDB and VoltSP together provide workflow-level atomicity: either all steps complete, or all compensate back to the start state. Temporal, DBOS, and equivalents do not provide true serializable isolation at every state transition. Volt does.
Complete explainability. Built in.
Every agent interaction captured with the same rigour as a human transaction. What was queried, what data Volt provided, what was recommended, what was decided, and why. The complete decision chain, recorded atomically, available for compliance review, regulatory challenge, or model retraining.
For context: Google’s Spanner, built by the world’s most resourced distributed database team, publishes 10ms reads and 50ms writes. Volt delivers sub-10ms with full ACID compliance. The gap is architectural, not incremental. Building a platform that combines streaming ingestion, in-memory transactional state, ACID-compliant processing, and five-nines availability in a single unified system is exceptionally difficult. Volt has been doing it for fifteen years.
TM FORUM CATALYST · URNC26.0.971
Validated in Production.
Not a Proof of Concept.
LIA FieldOps, Live Tier-1 CSP Environment
Volt is a technology partner in LIA FieldOps, validated in a live Tier-1 CSP environment under the TM Forum Catalyst programme. An AI agent supports field technicians in real time, querying Volt via MCP tools for live authoritative network state: current signal measurements, active incidents, and resource availability, alongside historical context from a separate analytics layer.
Volt enforces decisions sub-millisecond, captures the complete chain for human handover when confidence is low, and records every decision as authoritative truth. TM Forum Catalyst projects are by invitation. Architects reviewed what was available and selected Volt.
“AI determines what might be important. Volt determines what actually happens.”
Network event arrives. Signal measurement, active incident, or resource alert triggers the workflow.
Volt evaluates in <10ms. 99%+ of routine events handled deterministically against current authoritative state. No AI needed, no latency, no cost.
Edge case escalates to agent. Agent queries Volt via MCP tools for live network state: current signal data, active incidents, and resource availability.
Agent reasons from real data. Not a warehouse sync. Not a cached approximation. Authoritative current state, ACID-consistent.
Technician receives recommendation with full context. Approves or overrides. Volt records the complete chain.
Decision recorded as authoritative truth. Complete audit trail. Every query, every recommendation, every outcome. Available for compliance, retraining, and explainability.
Not a Claim. A Track Record.
Third parties who have arrived at the same conclusions independently. That matters, because it means the case is not just Volt talking about Volt.
Production deployments
Global leaders in financial services, telecommunications, and enterprise technology are running Volt in mission-critical environments today: real-time charging, fraud prevention, network control. Not pilots. Production systems where being wrong at 2am has real financial consequences.
TM Forum Catalyst: LIA FieldOps (URNC26.0.971)
Volt is a technology partner in LIA FieldOps, validated in a live Tier-1 CSP environment. The scenario requires sub-10ms ACID-consistent state accessible to AI agents during reasoning. Catalyst projects are by invitation. The architects reviewed what was available and selected Volt.
Independent analyst recognition
Stratola (March 2026) names Volt as the defining platform for the Decision Product category: sub-10ms ACID-compliant actions for mission-critical use cases, described as almost entirely unaddressed by current platform vendors. Decision Products are the real-time tier of the data product maturity model.
Market evidence for the agentic load argument
Major enterprise technology companies are repositioning their data infrastructure around agent-driven workloads. IDC projects 10x agent usage by 2027. Gartner projects 70% of enterprises deploying agentic AI in IT operations by 2029, up from less than 5% in 2025. Citi projects the server CPU market will reach $132 billion by 2030, driven primarily by agentic AI workloads.
The only platform that gives AI real-time truth to act on.
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