Built on the language, infrastructure, and security model your engineers already trust.
Empower your Java engineers to build the long-running agents of tomorrow.
Agents need data. It lives in modern endpoints, legacy databases, scanned archives, paywalled APIs, S3 buckets, and press releases.
Your agents reach all of it from Java – with the same libraries, credentials, and access controls your apps already use.
Every agent output is untrusted input. Every tool call is scoped and guarded. Every guardrail is deterministic, written in code, and sits outside the model itself.
Agents seamlessly deploy alongside your apps, your databases, your SSO. Same infrastructure, same security, same ops team.
Your infrastructure bill shouldn't scale with your ambition. Instead, our platform enables you to grow exponentially while dramatically reducing cost compared to all other agentic frameworks. Adding new agents is free, and by unifying runtime, agents, and tools in one process, we can drastically reduce your bill.
Your AI compounds: every new data source expands it and every edit your team makes teaches it. The moat deepens with every workflow – on its own, while in production.
Plug in internal systems, external feeds, new data sources as your operations grow. Same governance, same access controls as day one.
Every edit, every override, every tone adjustment your team makes feeds back into the system and leads to a better result next time.
An analyst rewrites a section. A regulatory writer shifts the tone. A claims reviewer overrides a calculation. The platform learns while in production.
Tool calls, permission checks, agent reasoning – all recorded in excruciating detail.
An enterprise AI platform for building and running agents in production. Agents deploy with the same security, compliance, and engineering standards as the rest of your enterprise software.
Most platforms ship managed agent runtimes: Python-first, per-session billing. Silverlake is the only Java platform combining an agentic runtime with an authoring framework. Your engineers build in Java, deploy on your infrastructure, and run thousands of agents without watching the meter.
Python is the language of data science. Production agentic systems have more in common with enterprise software than with a Jupyter notebook – they need the transaction management, permission enforcement, deployment pipelines, and monitoring your ops team already runs. Java has solved those problems for 30 years, and 12 million engineers are fluent in it. Every major managed agent runtime that launched over the last 24 months is Python-only. Silverlake is the platform of choice for enterprise engineers building agentic systems.
If your engineers build on the platform directly, yes. Silverlake is Java-native and integrates with enterprise Java infrastructure. Alternatively, our forward-deployed engineering team builds and deploys turnkey solutions for you end-to-end.
Permission structures, data access policies, and output validation run as deterministic code checks outside the model. Guardrails are not just system prompts, but code that sits at a layer the agent cannot reach.
Security is built into the platform: SOC2 Type 2, HIPAA, GDPR, and NIST AI RMF compliance; role-based and attribute-based access controls; enterprise-grade encryption; full audit trails of every AI decision with structured reasoning. Your data and all AI-generated outputs remain yours.
Private cloud, managed cloud, on premise, hybrid or even air-gapped environments. The Silverlake platform integrates with your existing infrastructure and access management systems.
The platform is sector-agnostic and built for workflows where security, compliance, and accuracy are critical. Already deployed across biopharma, financial services, and insurance.
Book a demo. We start by understanding your workflows and identifying where AI automation delivers measurable ROI for your organization and team. Then we run a Proof of Concept together, giving you a clear view of the numbers before full deployment.
On managed platforms, infrastructure cost grows with every agent you run. That's fine at small scale, but enterprise agentic workflows don't stay small – they add specialized agents, validation steps, orchestration. On a per-session model, each addition grows the bill. On Silverlake, runtime and agents are one system, so adding agents doesn't add line items.
Agreed, and that's exactly the point. Managed platforms operate outside the agent, so they can't touch your token bill. Silverlake unifies runtime, agents, and tools in one process, which lets the platform actively reduce what your agents consume: automatic context compaction, field summarization, two-tier retrieval, pass-by-reference for domain objects. The Platform page has the full list, showing you exactly how we enable you to save millions compared to the current state-of-the-art.