Start with the part of the estate that hurts most. Every module shares the same discovery data, the same policy engine and the same audit record, so expanding scope never means re-platforming.
Establish what you actually run, how it connects, and what changing it would affect.
Agentless inventory of every host, instance and service you own.
Scans networks, hypervisors, cloud accounts and Kubernetes clusters on a schedule, resolving duplicates and reconciling ownership. Finds the estate nobody documented — typically 8–14% more than the CMDB knew about.
Observed traffic and configuration resolved into a live service map.
Combines connection telemetry, configuration parsing and query analysis to build a directed graph of what talks to what. Every edge carries evidence, so engineers can see why a dependency was inferred.
Desired state defined once and enforced continuously.
Captures configuration across databases, operating systems and middleware, compares it to your standard, and either reports or remediates drift according to policy.
One queryable record of every asset, version and licence.
A normalised inventory across seventeen database engines, nine operating systems and the middleware layer, with end-of-support dates and licence positions attached.
Your service management record kept accurate without manual upkeep.
Bi-directional reconciliation with ServiceNow, Jira Service Management and other CMDBs. Discovered truth flows in; ownership and business context flow back out.
Provision, patch, upgrade and migrate at estate scale with the same guardrails every time.
Standard builds produced identically, every time.
Golden-image and configuration pipelines for every supported platform, with hardening baselines applied at build time rather than retrofitted.
Self-service infrastructure inside your guardrails.
Catalogue-driven provisioning across cloud and on-premises, generating the Terraform, Bicep or Ansible your platform team already maintains rather than replacing it.
Estate-wide patching sequenced around real dependencies.
Plans waves from the dependency graph so quorum, replication and availability constraints are respected. Validates health between waves and stops on the first signal that matters.
Major version upgrades run as a repeatable pipeline.
Pre-flight compatibility analysis, staged execution, in-flight validation and a tested rollback path for database, middleware and OS major versions.
Seventeen engines, one operating model.
Provisioning, patching, backup verification, HA configuration, failover testing and performance baselining across SQL Server, Oracle, PostgreSQL, MySQL, MongoDB, Cassandra and more.
Application servers, web tiers and messaging handled as first-class estate.
Lifecycle automation for IIS, Apache, NGINX, Tomcat, JBoss, WebLogic, WebSphere, Kafka, RabbitMQ and MQ Series — the layer most automation programmes leave manual.
One control plane across Azure, AWS, Google Cloud and Oracle Cloud.
Account and subscription governance, resource lifecycle, tagging enforcement and cost guardrails applied consistently regardless of provider.
Windows, Linux and Unix under a single lifecycle.
Kernel and package management, hardening, service configuration and reboot coordination for Windows Server, RHEL, Ubuntu, SUSE, AIX and Solaris.
Cluster lifecycle without a bespoke platform team per cluster.
Version upgrades, node pool rotation, add-on management and policy enforcement across EKS, AKS, GKE and OpenShift, respecting pod disruption budgets throughout.
Datacentre exits and cloud migrations run as a production line.
Wave planning from the dependency graph, automated target build, data movement, cutover rehearsal and validated rollback — repeated at volume rather than run as a one-off project.
Detect, diagnose and resolve — with the platform doing the routine work unattended.
Diagnostics gathered and first response executed before an engineer joins.
On alert, the platform collects evidence across the dependency path, correlates it with recent changes, and runs the approved first-response actions.
Known failure modes corrected without a human in the loop.
Policy defines which conditions may be remediated unattended, in which environments, within which windows. Everything else escalates with a recommended action.
Correlated evidence instead of a wall of alerts.
Ranks candidate causes by combining topology, change history, telemetry and log signals, and shows the reasoning behind each candidate so engineers can confirm or discard it quickly.
Capacity and failure risk flagged before it becomes an incident.
Models growth in storage, connections, memory and transaction volume against historical patterns and raises a change request with a proposed remediation.
Ask the estate a question. Get an answer with the evidence attached.
Natural-language access to inventory, topology, change history and telemetry, plus runbook generation reviewed by a human before it can execute anywhere.
Prove control to auditors and executives from the same record the engineers work in.
Controls tested continuously, evidence produced automatically.
Maps technical checks to CIS, PCI DSS, HIPAA, SOX, DORA, NIST 800-53 and internal standards, running them on a schedule and packaging the results as auditor-ready evidence.
Guardrails evaluated before anything executes.
Policies written as code decide what may run, where, by whom and in which window. Denials explain themselves, so engineers can fix the request rather than file a ticket.
Visual authoring with production-grade execution semantics.
Build workflows on a canvas or in YAML — they are the same object. Retries, compensation, parallelism, human tasks and rollback are first-class rather than bolted on.
Change advisory that keeps up with the change volume.
Risk-scored changes route to the right approvers with the evidence already attached. Low-risk, well-precedented change can be pre-authorised by policy.
The operational picture leadership actually asks for.
Automation coverage, change success rate, patch currency, compliance posture and avoided cost — drawn from execution data rather than assembled by hand each month.
Scheduled, parameterised reporting for every audience.
Build a report once and deliver it as PDF, spreadsheet or API to the teams and regulators that need it, on the cadence they need it.
A tamper-evident record of everything the platform did.
Every action, approval, parameter and output written to an append-only log with cryptographic chaining, retained to your policy and queryable over API.
Everything the console does, available programmatically.
A documented REST API, webhooks, event streaming and SDKs — the console is built on the same surface, so nothing is reserved for the UI.
Most organisations begin with discovery and a single automation domain, prove the control model, then widen scope one estate at a time.
Deploy the control plane and a runner. Complete inventory and dependency map for the agreed scope, reconciled against your CMDB.
Pick the highest-friction workflow — usually patching or provisioning — and take it from manual runbook to governed automation with your approvals in place.
Extend coverage across engines and regions. Policy, approvals and audit are already defined, so each new domain inherits the control model.
Promote well-precedented, low-risk change to policy-bounded self-healing. Human attention moves to the changes that actually warrant it.
Tell us the workflow that costs your team the most hours this quarter. We will show you exactly how it looks once it is automated, using your own estate as the example.