1,284 agents in service  ·  every action attributable Runs in your cloud  ·  your keys, your models, your data

Agent operating platform

Somewhere safe
for your agents
to work.

Governed data and knowledge, one runtime across every model, isolated sandboxes for real execution, and a complete account of everything an agent did.

Book a demo → See the architecture
Deploys in your VPCSSO & RBAC from request oneRead-only until you grant more
Trace  ·  req_9f2a1c Live
One request, end to end Attributable 100%

Agents in production at

NORTHWINDHELIOSTATPARAGON CLOUDKITE & COORBITAL FREIGHT

The gap

Agent pilots are easy. Agents in production are not.

The model was never the hard part. What stops a promising pilot is everything around it — the data it is allowed to see, the systems it is allowed to touch, and the answer to "what exactly did it just do?"

Access

The data is not ready to be used

Records here, documents there, permissions modelled differently in each. Every new agent starts by solving the same retrieval and governance problem from scratch.

Blast radius

Useful means it can change things

An agent that can only talk is safe and not very valuable. The moment it can run code or write to a system, someone has to be able to bound what it reaches.

Accountability

Nobody can reconstruct what happened

Scattered logs across a model gateway, a vector store and six tools do not add up to an answer for risk, finance or a customer asking about their data.

The platform

Agents need more than a model. They need somewhere to stand.

Slamurai provides the common infrastructure underneath every agent, so each new one is a configuration rather than a rebuild.

Data & knowledge

Your information, in a form agents can use

Ingest, store, govern and analyse customer data — structured records and unstructured documents alike. Embeddings make it retrievable by meaning; knowledge graphs let an agent follow the relationships between things.

  • Structured and unstructured ingestion
  • Embeddings for semantic retrieval
  • Nodezilla graph traversal
  • Governance down to the field
AI & agent runtime

One runtime, whichever model you choose

A common runtime across models and inference providers. Agents reason, retrieve, call approved tools, run workflows, and hand work to other specialists.

Conversational interface

Chat that can act, not only answer

Chat is the front door. An agent invokes the tools and workflows that person is authorised to use, carrying their identity, permissions and context the whole way.

Secure agentic execution

Sandboxes with walls that hold

Isolated environments to write and run scripts, analyse data and work with files — and no route from the sandbox into the platform underneath it.

Extensible modules

Add capability without touching the core

A controlled module architecture keeps new capability off the platform's critical path. In time, agents can draft and revise modules inside governed development sandboxes.

Governance & observability

Nothing happens off the record

Every action is observable and attributable: who initiated it, which agent acted, which model ran, what data it reached, what tools it called, what it changed and what it cost.

  • End-to-end trace per request
  • Data access logged at field level
  • Cost attributed by team and agent
Agentic tool

Nodezilla — ask the graph, not the database.

The knowledge graph is only useful if an agent can walk it. Nodezilla is the traversal engine, exposed as a first-class tool any agent can call: start at an entity, follow the relationships that matter, and get back only the nodes the caller is cleared to see.

// tool call, emitted by the agent
nodezilla.traverse({
  from: "invoice:INV-4471",
  follow: ["billed_to", "governed_by", "approved_by"],
  depth: 3,
  as_of: "2026-08-11"
})
→ 14 nodes · 21 edges · 38 ms
6 nodes withheld — outside caller's scope
38 msMedian traversal
Depth 6Before it asks you to narrow
Per callerScoped, never post-filtered

Every hop is written to the request trace, including the ones refused.

Request lifecycle

What happens when somebody asks for something.

一

Identify

The request arrives with the person's identity and permissions attached. The agent inherits them for the duration and cannot exceed them.

二

Retrieve

The agent gathers only what that person is cleared to see — records, documents and the relationships between them in the knowledge graph.

三

Act

Approved tools and workflows run. Anything needing code runs in a throwaway sandbox with no route back to the platform.

四

Record

The whole trace is written down — models, data, tools, changes and cost — before the answer reaches the person who asked.

Governance & observability

Every action answers the same eight questions.

Not a log you have to reconstruct a story from. One record per request, complete enough to hand to an auditor, a finance team, or a customer asking what touched their data.

trace / 2026-08-11 / req_9f2a1c Retained 7 years
Initiated bye.dunn@northwind.co — SSO, group: finance-analyst
Agentreconciliation-agent v2.4 → delegated to document-reader v1.1
Modelclaude-opus · customer-managed endpoint · 12,480 in / 1,932 out
Data accessedledger.transactions (6 fields, 1,204 rows) · 3 documents · 14 graph nodes
Tools callederp.query · sandbox.python · sheets.write
Changed1 file created (variance-aug.xlsx) · 0 records updated
Policyfinance-readonly: allowed  ·  finance.write: denied, out of scope
Cost$0.41 — billed to finance-ops

The same record for a chat reply, a scheduled workflow, or an agent acting on another agent's behalf.

Book a demo →

Extensible modules

New capability ships through the same gate every time.

Modules are how the platform grows without the core changing underneath you. Longer term an agent can propose one itself — and it still passes every step below before anyone can use it.

01

Propose

A person or an agent drafts a module in a governed development sandbox.

02

Test

Automated suites run against the module's declared contract.

03

Scan

Security scanning on code, dependencies and requested permissions.

04

Approve

A named owner signs off on scope, data access and blast radius.

05

Version

The module enters the registry, pinned and immutable.

06

Deploy

Staged rollout by tenant, reversible in one step.

Models & systems

Bring what you already run.

No single model provider, no forced migration. Slamurai connects read-only first; you grant write access one tool at a time.

Anthropic
OpenAI
Google Vertex
AWS Bedrock
Azure OpenAI
Self-hosted
Snowflake
Databricks
Postgres
SharePoint
Salesforce
S3

Before you ask

The questions security asks first.

If yours is not here, we would rather answer it on a call than in marketing copy.

Where does it run, and who can see our data?

Slamurai deploys into your cloud account. Your data stays in your tenancy, encrypted with your keys, and is never used to train anyone's model. We can operate a managed instance for you, or hand you the controls entirely.

Which models can we use?

Any of them, including your own. The runtime abstracts the provider, so you can route by task, cost or data residency, and switch without rewriting an agent. Bring your own keys and endpoints.

What stops an agent doing something destructive?

Three things in sequence: it can only inherit the initiating user's permissions, it can only call tools from an approved catalogue, and any code it writes runs in an isolated sandbox with no route to the platform and no egress beyond systems you have approved.

How do we prove what happened afterwards?

Every request produces one complete record — initiator, agent, model, data touched at field level, tools called, changes made, policy decisions and cost. It is queryable, exportable and retained on your schedule.

How long does it take to get something live?

A first read-only agent against one data source usually runs inside a week. Write access follows once you have watched the traces and are satisfied with the scope.

How is it priced?

A platform fee plus metered inference and execution, attributed by team and agent so the cost of each workflow is visible rather than pooled. Model spend is passed through at cost when you bring your own keys.

Book a demo

Bring the process you would not trust an agent with yet.

We will run it on Slamurai in your environment, then walk the trace with you: what it reached, what it changed, what it was refused, and what it cost.

45 minutes, engineer to engineer.
No slides unless you ask for them.
hello@slamur.ai if you would rather write.

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We reply within one working day. Your details are used to arrange the demo and nothing else.

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We will be in touch within one working day to find a time. If it is urgent, reply to the confirmation email and it will reach the team directly.

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