Silverlake by Redouble AI: Java-Native Agentic OS
Redouble AI went through Y Combinator in 2024 as a quality-control layer for AI in regulated industries. But the founders learned the real problem wasn't checking AI outputs. Enterprise engineers had no way to build and deploy production-grade agentic AI in the first place. The product they built to close that gap is an agentic operating system called Silverlake.
That shift reflects something the broader market is only now starting to confront: more than 90% of generative AI pilots fail to yield business returns. Off-the-shelf AI tools don't deliver the ROI they promise. And the managed agent runtimes that have launched over the last two years all share the same architecture: forcing enterprises to embrace a parallel AI stack that runs counter to all their existing security, permission, and deployment realities. . For enterprises running on Java, that's a dead end.
12 million Java engineers, zero production-grade agentic frameworks built for them
Every large enterprise's leadership wants agentic AI deployed. The engineers responsible for the entire tech stack work in Java, the language running banking cores, insurance claims systems, and biopharma operations. But every major agentic framework that has shipped over the last two years is not built for them - it’s built for the AI engineers, who are far and few in between, and who are not the ones who build, develop, maintain, and extend the actual tech core of their companies.
Most AI engineers live and work in Python - the right language for data science, rapid prototyping, and modeling. But production agentic systems have much more in common with enterprise software than with a Jupyter notebook. They need the same transaction management, permission enforcement, deployment pipelines, and monitoring that Java has been solving for 30 years. No existing framework has been built on that foundation — until now, with Silverlake.
What makes this gap especially acute: Java engineers won't ship mission-critical workflows on a stack that handles permissions through system prompts, lacks granular access control, or moves data outside their own infrastructure. The cultural and engineering mismatch is real.
What Silverlake does
Built as a Java-native agentic AI operating system, Silverlake maps every agentic concept onto an enterprise primitive Java engineers already know and trust. The founders' analogy: Hadoop was a real technological breakthrough, but hated by enterprise. Snowflake fixed that by mapping onto familiar primitives, the US Dollar. Silverlake does the same thing for agents in the enterprise.
Agent identity maps onto the existing enterprise identity layer, extended rather than replaced. Agent audit plugs into the existing audit framework, extended rather than duplicated. Orchestration, permissions, rate limiting, observability, and fault tolerance follow the same logic throughout.
In practice: an engineer writes about 30 lines of code to define an agent, specifying its objective, tools, and guardrails. From there, the platform handles everything else. A single mid-sized cloud instance runs over 1,000 agents in parallel. Every LLM decision is logged with full reasoning. Zero failed runs from rate-limit errors (HTTP 429s) have been recorded. On throughput, the team reports up to 5x better performance than the leading Python-based agentic frameworks, which they attribute to unifying runtime, agents, and tools in a single process instead of treating the agent runtime as an external managed layer.
Because deployments run in plain Java and slot into existing CI/CD pipelines, each new workflow tends to spread organically. One engineering team automates a process; the team next door sees the output and gets on board. Redouble AI describes this as one of the most consistent patterns across early enterprise customers.
Security as a foundational layer, not a configuration option
Guardrails in Silverlake are deterministic code checks running outside the model, not system prompts that a well-crafted input can bypass. Access control encompasses both role-based and attribute-based policies (RBAC + ABAC). Every tool call is scoped and typed. Domain objects passed between agents are immutable (patents pending), so a compromised step in a long agent chain can't corrupt data downstream.
For companies concerned about where their data ends up, the deployment model matters as much as the security architecture. Private cloud, managed cloud, on-premise, hybrid, and air-gapped environments are all supported. Integration with existing identity and infrastructure systems is built in, and the platform works with any frontier or open model, including AWS Bedrock and Google Vertex. Compliance coverage includes SOC2 Type 2, HIPAA, GDPR, and NIST AI RMF.
Pricing is also structured differently from managed platforms. On per-session pricing, infrastructure costs grow with every agent added — fine at small scale, but enterprise agentic workflows don't stay small. On Silverlake, runtime and agents are one system, so adding agents doesn't add line items. Beyond that, inference costs are actively reduced through automatic context compaction, field summarization, two-tier retrieval, and pass-by-reference for domain objects.
Where it's running: three industries, three different problems
Developed with design partners across biopharma, financial services, and insurance, and now deployed in production in all of these sectors, Silverlake addresses the core constraints all of these sectors share: data volume, regulatory exposure, document complexity. Each maps onto different parts of the platform.
Biopharma: from siloed data to submission-ready documents
Drug development organizations typically run on data across disconnected preclinical, clinical, commercial, and regulatory teams. Getting leadership a consolidated update can take weeks. Getting a submission-ready Clinical Study Report out the door is slower still: these documents run hundreds of pages and have to meet strict regulatory standards, which means months of work for entire teams of people.
With Silverlake, agents ingest and consolidate data across teams through a single interface that fully respects existing authorization and permission structures. Document drafts for internal and regulatory use are generated from a company's own templates or from expert-curated ones, with all edits auto-saved and fed back into the system for next time. Repetitive data pipelines (ingesting and cleaning CRO data, flagging potential outliers in medical monitoring) run automatically. Preclinical report generation and discovery data analysis are also covered, addressing a significant portion of the bottlenecks between data collection and filing.
Insurance: underwriting, claims, and compliance in one system
Insurance workflows involve high document volume, strict compliance requirements, and real financial exposure when something gets missed. Underwriters spend time manually ingesting applicant data to calculate risk. Claims teams spend hours comparing submitted claims against policies to determine coverage and spot discrepancies.
With Silverlake, agents ingest all relevant applicant data, assess risk levels, and propose premiums based on the company's own benchmarks, ready for the underwriter's review. For claims, the system extracts data from submitted forms (including faxed documents), validates against policy terms and coverage rules, cross-references benefit schedules, generates a coverage determination with a full audit trail, and produces an itemized explanation of benefits. Red flags and discrepancies surface automatically. All of this runs inside the company's own infrastructure; policyholder data doesn't move to an external managed service.
VC & PE: due diligence at dataroom scale
Investment firms face a specific version of the document overload problem. Due diligence on a single deal means reading hundreds of files, any one of which might contain a material issue hidden inside dozens to hundreds of pages each. Investment memos take days to prepare even after a team has done the underlying analysis. LP obligations across LPAs and side letters require ongoing monitoring that typically runs on memory and manual review.
With Silverlake, agents ingest entire data rooms or individual files one at a time, prepare verifiable answers to specific questions, and flag anomalies, inconsistencies, and red flags automatically, with full document references for every insight generated. Investment memos and portfolio updates are drafted from structured and unstructured data, adapting to edits in real time and remembering preferences for next time. LP obligations across agreements are monitored continuously, with automatic alerts for key dates and clauses. Minutes for initial analysis, not days.
Connecting to the full enterprise stack
Agents need access to data that sits across modern APIs, legacy databases, scanned archives, paywalled sources, and object storage. Using the same libraries, credentials, and access controls the Java application already uses, Silverlake connects to all of it. Protocol support covers MCP (STDIO and HTTP), A2A, REST, and OpenAPI on the modern side, plus legacy databases, filesystems, and proprietary protocols on the older end.
One of the consistent failure modes for enterprise AI deployments: the AI can only reach a subset of the data it needs — the modern endpoints, but not the legacy databases holding 15 years of policy history. An insurance agent that can read the submitted claim form but can't query the legacy policy system can't do claims processing. A biopharma agent that can access a data summary but not the underlying database can't produce a complete study report. Silverlake's connectivity layer is designed to close that gap without requiring data migration or duplicate infrastructure.
A system that learns while it runs
Every edit, override, and correction a human expert makes feeds back into Silverlake while it's in production, with no separate fine-tuning pipeline and no manual retraining cycle. An analyst rewrites a section of an investment memo; a regulatory writer shifts the tone of a clinical report; a claims reviewer overrides a coverage determination. Each signal improves the next output automatically.
Over time, the system gets more accurate to each organization's specific standards. A biopharma company's regulatory writing conventions, an insurer's claims language, a PE firm's memo format: all of these compound into a model that reflects the organization's own expertise, not a generic baseline. This is the moat the founders describe — not just an AI that runs workflows, but one that gets better at running your specific workflows, because every expert interaction teaches it something.
The team
Martin Bittner (CEO) is a Rhodes Scholar with an MD from the University of Freiburg, a DPhil in Oncology from Oxford, and an MBA from Wharton. Before Redouble AI, he co-founded Arctoris, a pharma workflow automation company headquartered in Oxford, growing it from inception to closing millions in enterprise deals. He is a member of the Forbes Technology Council and an elected member of the Young Academy of the German National Academy of Sciences.
Andrey Santrosyan (CTO) spent 25 years building enterprise software and AI applications, more than 120 in total. Before Redouble AI, he was Founding VP of Data Operations at Redesign Science, an early generative AI startup in New York, and before that Associate Director of Data Science and Engineering at Novartis, where he led complex software and AI development projects with teams of dozens of engineers. .
Redouble AI is backed by Y Combinator, Exponential, Transpose Platform, Tiny VC, and the Oxford Seed Fund.
Two advisors are worth naming specifically. James Gosling, the lead inventor of Java, a member of the National Academy of Engineering, and a recipient of the IEEE John von Neumann Medal, serves as a senior advisor. So does Professor Christian Terwiesch, former chair of Wharton's Operations, Information, and Decisions department and co-director of UPenn's Mack Institute for Innovation Management, whose work on operations and innovation has been cited more than 17,000 times. For a company making a technical bet on Java and a market bet on enterprise workflow automation, those are precise choices.
Getting started with Silverlake
Two paths to deployment exist. Companies with Java engineering teams can build directly on the platform: agents are defined in code, and Silverlake handles the production infrastructure, resource management, orchestration, permissions, etc. from there. For organizations that want to get help or don't have the internal bandwidth, Redouble AI's forward-deployed engineering team builds and deploys turnkey solutions end-to-end.
A typical engagement starts with a demo focused on a specific workflow: identifying where AI automation delivers measurable ROI, then running a proof of concept with clear numbers before full deployment. Redouble AI is explicit about this sequencing — the goal is to give organizations a concrete view of the returns before committing to a broader rollout.
At its core, Redouble AI is making a specific market bet: the enterprise agentic space has been underserved by Python-first infrastructure, and 12 million Java engineers represent a deployment path that the current generation of frameworks has ignored. Whether that bet plays out depends on how many enterprise organizations move beyond pilots. Given that 90% of generative AI pilots currently fail to reach production returns, the team that can actually deliver in a Java shop has a clear advantage. And given that Java has powered enterprise production systems for 30 years, the engineers who need to sign off on those deployments already know exactly what they're looking at.