Deploy enterprise AI without losing control of context, policy, or auditability.
Olympus sits between your teams and model vendors. It keeps session memory portable, enforces policy before every call, routes across providers, and gives you replayable audit trails when decisions matter.
Your team does not start over when the model, route, or workflow branch changes.
Governance is enforced upstream instead of getting bolted on after the fact.
Trace timelines, reconstruct context, and answer operational or compliance questions quickly.
Carry working state across providers and workflow branches.
Apply identity, routing, and risk controls before execution.
Escalate high-risk actions with signed, reviewable trails.
Inspect decisions later without relying on vendor black boxes.
Most AI stacks break at the point of accountability.
The prototype works. The production questions are harder.
- Context disappears between sessions, tools, and provider changes.
- Prompt-level guardrails are not the same as enforceable policy.
- Approvals happen in Slack threads and email chains, not in an auditable system.
- When something goes wrong, most teams still cannot answer what the system knew at the time.
Olympus changes the operating model.
- Olympus owns the session, not the model vendor.
- Policy is enforced before calls leave your environment.
- High-risk actions route through governed approval paths.
- Execution history stays replayable when legal, security, or operations need answers.
Olympus gives enterprise AI a control plane.
Instead of stitching together routing, prompts, approvals, and memory by hand, teams use Olympus as the layer that governs execution.
Portable Context
Session state survives provider swaps, long-running workflows, and restarts. Your team does not have to start over just because the model changed.
Policy Enforcement
Versioned policy packs are enforced before every call, so governance is not left to prompt conventions or downstream cleanup.
Governed Approvals
High-risk actions pause for human review with escalation paths, signed decision trails, and role-aware routing.
Replay + Audit
Inspect timelines, reconstruct context, and replay execution history when teams need to explain what happened.
How Olympus fits into the stack
Teams work through Olympus
Applications, agents, and internal users send work through Olympus instead of directly to a model vendor.
Olympus applies control
Policy, routing, approval thresholds, and identity-aware rules are enforced before the request moves forward.
Olympus preserves continuity
Context, session state, and memory persist across model changes, retries, and workflow branches.
Olympus keeps a replayable trail
Every important decision can be inspected later through execution history, Delta replay lineage, and audit exports.
More than routing
Gateway tools help teams connect to models. Olympus helps teams operate AI in environments where memory, policy, approvals, and replayability are not optional.
If the question is only which model should answer, a gateway may be enough. If the question is whether the system can be governed, trusted, and explained in production, that is where Olympus begins.
Where Olympus lands first
Customer Service Escalations
Carry context across agents, approvals, and provider changes without forcing teams to restate the case at every handoff.
Regulated Knowledge Workflows
Mask sensitive data, preserve operational continuity, and keep a defensible trail for healthcare, finance, and other controlled environments.
Internal Enterprise Copilots
Give teams AI assistance without letting business memory, policy, and workflow state get trapped inside one vendor session.
Built for teams that have to answer for AI decisions
Olympus is designed for security, compliance, platform, and operations leaders who cannot afford black-box workflows, brittle context windows, or undocumented approvals.
Best initial fit: regulated internal copilots, customer service escalations, and other workflows where procurement, legal, or audit teams will eventually ask how the system behaved and what it knew at the time.
Two ways to engage before a full pilot
If the workflow is real but the buying committee is not ready for a platform commitment, start with a teardown or a design sprint. Both are built to turn ambiguity into a concrete next step.
AI Control Teardown
Review one workflow, identify the governance and control gaps, and leave with a concrete recommendation path.
Starting at $5,000
Design Sprint
Map how policy, approvals, replay, and provider control should work for one governed workflow before the pilot.
Starting at $15,000
Control the session. Control the risk.
- Your company talks to Olympus, not directly to the model vendor.
- Policy enforcement happens before execution, not after cleanup.
- Replay and audit trails make legal, security, and operations reviews practical.
If AI decisions need to survive scrutiny, Olympus belongs in the stack.
See how Olympus handles provider routing, policy enforcement, approvals, continuity, and replay in one control plane.
Request a compliance walkthrough
Share the team, workflow, and risk surface you are evaluating. We will use that to tailor the first conversation around governance, provider portability, approvals, and auditability.
Lead with governance, not generic orchestration
Olympus closes faster when the conversation is about policy enforcement, replayability, and provider-independent control rather than model novelty.
- The workflow legal, security, or audit will eventually scrutinize
- The provider lock-in or context continuity problem you are dealing with
- Who owns the decision internally: security, AI governance, platform, or operations