Price your project in delivery units

Our base foundation is serverless-first—ideal for AI and startups. Add foundational VPC networking to run Compute Engine VMs, GKE clusters, or private-IP workloads: Shared VPC topology, subnets, Cloud Router, Cloud NAT, Private Service Connect, and firewall policies.

Dedicated or Partner Interconnect or HA VPN tunnels to on-premise or other clouds, dynamic BGP cross-environment routing, and hybrid Cloud DNS forwarding. Excludes third-party telecom carrier lead time.

Integrates Google Cloud Identity with Microsoft Entra ID, Okta, or Ping: automated SCIM user and group provisioning, SAML/OIDC single sign-on, super-admin break-glass account separation, and phishing-resistant MFA enforcement.

Granular cloud cost governance: standard and resource-level billing export to BigQuery, mandatory cost-allocation label taxonomy enforced via CI/policy, automated budget and anomaly alerts, CUD optimisation analysis, and a Looker Studio showback dashboard.

Designs and enforces VPC Service Controls perimeters around sensitive Google Cloud APIs (BigQuery, Cloud Storage, Vertex AI, KMS). Includes ingress/egress rule design, Access Context Manager policies, dry-run audit log analysis, and zero-disruption cutover to enforced mode.

Deploys Global External Application Load Balancers fronted by Cloud Armor security policies: preconfigured OWASP Top 10 WAF rules, adaptive DDoS protection, geo-fencing, bot management, and per-client rate limiting.

Configures Google Cloud Backup and DR vaults, automated backup plans, and compliance retention schedules across Compute Engine, Cloud SQL, AlloyDB, and GKE, validated with a live game-day restore execution and recovery runbook.

End-to-end multi-region architecture across two Google Cloud regions: global traffic routing, cross-region database replication, explicit RTO/RPO tiering, and implementation of one reference failover pattern.

Configures Google Cloud Assured Workloads for regulated industries and government workloads (AU Regions, IRAP Protected, FedRAMP, or EU Sovereignty): data residency locks, cryptographic key residency, support personnel access controls, and compliance gap register.

Establishes encrypted, redundant network connectivity between your source cloud (AWS VPC / Azure VNet) and Google Cloud VPC for parallel migration waves, including BGP routing and cross-cloud DNS resolution. Deduplicated if Advanced Hybrid Networking is selected in Foundations.

De-risks production migration cutover: weighted DNS traffic shifting, synthetic and real-user telemetry comparison across source and target environments, sub-minute rollback runbooks, and dedicated 24/7 engineering hypercare during the cutover window.

Provisions a dedicated Google Cloud VMware Engine private cloud: ESXi node cluster sizing, NSX-T networking and Private Services Access peering to native GCP services, VMware HCX workload mobility mesh for live vMotion migrations, and identity federation.

Agentless Wiz CNAPP deployment across your Google Cloud organisation: custom least-privilege scanning roles, Kubernetes/container registry connectors, out-of-the-box security framework baselines, toxic combination prioritisation, and an initial remediation workshop.

Extends Wiz into engineering workflows: CI/CD pipeline IaC and container image scanning gates, Software Bill of Materials (SBOM) tracking, custom Rego policy authoring, and bi-directional Jira/ServiceNow/Slack remediation workflows.

Full onboarding to Google Security Operations (Chronicle SIEM & SOAR): native GCP, Workspace, IdP, and EDR log feeds, custom parser extensions, curated threat detections mapped to MITRE ATT&CK, 2–3 automated response playbooks, and analyst handover.

Deploys an autonomous Tier-1/Tier-2 alert investigation fleet built on Google Agent Development Kit (ADK) and Vertex AI: automated evidence enrichment, false-positive suppression, incident timeline synthesis, and human-approved containment actions inside Google SecOps.

Secures generative AI pipelines from build to runtime: Wiz AI-SPM discovery of shadow AI and exposed training buckets, Artifact Registry container/model scanning, Model Armor runtime protections, and Google SecOps detections for Vertex AI audit logs.

Implements declarative Security Command Center (SCC) posture definitions across folders and projects: preventive and detective Security Health Analytics / custom modules, Terraform drift detection, and a governed risk-exception process.

Eliminates IAM sprawl and over-privileged accounts: IAM Recommender and Policy Analyzer audit, migration from individual bindings to Google Groups RBAC as best practice, custom least-privilege role definitions, and a recurring access recertification workflow.

Designs and enforces Customer-Managed Encryption Keys (CMEK) across BigQuery, Cloud Storage, Persistent Disks, and Secret Manager: regional/multi-regional key rings, KMS admin separation of duties, rotation schedules, and Org Policy CMEK constraints.

Configures Google Cloud Sensitive Data Protection (formerly DLP): organisation-wide discovery profiles for BigQuery and Cloud Storage, custom infoType detectors, cryptographic tokenisation/masking templates, and findings export to Security Command Center.

Maps your Google Cloud controls directly to regulatory frameworks including ISO 27001, SOC 2 Type II, ASD Essential Eight, APRA CPS 234, or internal frameworks. Delivers a complete traceability matrix, automated audit evidence sources, and a prioritised gap register.

Comprehensive audit of Google Workspace Admin console settings: Organisational Unit (OU) structure, external Drive sharing and DLP rules, OAuth third-party app allowlisting, SPF/DKIM/DMARC email authentication, and a prioritised remediation backlog.

Workload-centric observability (building on platform log routing): OpenTelemetry metrics and traces, custom Cloud Monitoring dashboards tracking the Four Golden Signals per service, and low-noise symptom-based log sink alerts.

Eliminates manual scaling and recurring availability incidents: GKE Horizontal/Vertical Pod Autoscaler and custom metric scaling, Cloud Run concurrency and min-instance tuning, health probe hardening, and circuit breaking.

Validates resilience before an outage occurs: structured failure injection experiments (pod termination, zone blackhole, database failover), live game-day facilitation, verification of actual RTO/RPO against targets, and blast-radius documentation.

De-risks your legacy warehouse exit before writing migration code: automated extraction of warehouse metadata and query logs, complexity scoring of tables/views/procedures, lineage-based wave sequencing, and a fixed-price DU migration roadmap.

Analyses Snowflake ACCOUNT_USAGE query telemetry to quantify warehouse idle spend, clustering overhead, and query patterns, producing a side-by-side BigQuery sizing benchmark backed by our contractual 20% cost-reduction guarantee.

Serverless, low-maintenance orchestration using Google Cloud Workflows, Cloud Scheduler, and Dataform native schedules — ideal where full Managed Airflow (Cloud Composer) is not warranted.

Production Cloud Composer (Managed Apache Airflow) setup: private IP environment sizing, reusable DAG framework and conventions, secret backend integration, automated Git CI/CD for pipelines, and SLA failure alerting.

Establishes software engineering rigour for SQL transformations in dbt or Google Cloud Dataform: staging/intermediate/mart layer conventions, incremental materialisation patterns, automated PR testing, and generated documentation.

Sets up Google Cloud Datastream for low-latency Change Data Capture (CDC) from operational databases (PostgreSQL, MySQL, Oracle, SQL Server, AlloyDB): private connectivity, backfill + streaming replication patterns, and BigQuery merge configuration.

Implements enterprise data discovery and governance in Dataplex: custom aspect types and metadata tags, business glossary onboarding, data domain and data product taxonomy, and automated column/table lineage.

Deploys automated Dataplex Data Quality scans across critical BigQuery tables: profiling, null/uniqueness/referential rule sets, quarantine routing for failed rows, and pipeline quality alerting.

Integrates Sensitive Data Protection with BigQuery: continuous discovery scans for PII/sensitive columns, automatic policy tag attachment for column-level masking, and cryptographic de-identification and tokenisation templates.

Configures zero-copy governed data sharing via BigQuery Analytics Hub: private or commercial data exchanges, data clean rooms, subscriber entitlement workflows, and egress audit logging.

Unifies Cloud Storage and BigQuery into an open lakehouse: BigLake managed Apache Iceberg tables, runtime catalog federation for Spark and BigQuery engines, fine-grained security on open storage, and automated table maintenance.

Architects a decentralised Data Mesh on Google Cloud: multi-project domain ownership topology, self-serve Terraform blueprints for publishing domain data products, and central Dataplex federated governance standards.

Builds a governed semantic layer in Looker: modular LookML project with Git version control, explores, user-attribute row and column access controls, caching policies, and 3–5 production executive dashboards.

Activates curated BigQuery customer and analytics models back into one operational SaaS system (e.g., Salesforce, HubSpot, Braze): entity sync modeling, incremental diff syncing, rate-limit handling, and error alerting.

Reduces BigQuery spend while improving query performance: INFORMATION_SCHEMA workload analysis, slot reservation and autoscaling sizing, partitioning and clustering remediation, and refactoring top cost-driving queries.

Hardens an Agent MVP for enterprise production: live connections to production data sources, end-user OAuth/IAM auth, retry and rate-limit handling, automated deployment pipeline, operational runbook, and support model.

Customer-facing conversational agent built on Vertex AI: multi-turn dialogue flows for core user journeys, grounded responses from approved knowledge bases, escalation triggers, and live human-agent hand-off.

Production Retrieval-Augmented Generation (RAG) pipeline: document data stores and connectors, semantic chunking and embedding strategy, hybrid search and reranking, citation-grounded generation, and retrieval quality tuning.

Exposes 2–4 internal APIs or databases over standardised Model Context Protocol (MCP) servers: schema validation, scoped secret and auth handling, idempotency controls, and automated tool contract tests.

Equips agents with persistent state across turns and sessions: session state storage, structured user memory profile extraction, PII scrubbing, and configurable TTL retention and privacy rules.

Architects collaborative multi-agent systems using Google ADK: specialist sub-agent design, deterministic and LLM-driven delegation and hand-off contracts, shared session state, and error/loop circuit breakers.

Builds your pre-production evaluation harness: curated golden dataset covering core and edge cases, automated trajectory and response scoring rubrics, pull-request regression gate in CI, and quality scorecards.

Extends your Agent evaluation tooling with persona-driven synthetic user simulators: stress-tests multi-turn conversations across cooperative, confused, and adversarial user personas before production release.

End-to-end agent telemetry: OpenTelemetry span instrumentation across prompts, model calls, and tool executions, Cloud Monitoring dashboards for latency and token usage, and automated failure alerting.

Applies SRE discipline to production AI agents: continuous automated evaluation of live telemetry, semantic drift and hallucination detection, production-to-eval dataset feedback loops, and CI regression gates.

Enforces runtime input and output safety guardrails using Model Armor and policy filters: blocks prompt injection and jailbreak attempts, redacts sensitive PII, screens outbound URLs, and enforces topical boundaries.

Dedicated security review of your agent architecture: least-privilege agent identity and OAuth scope review, confused-deputy and tool-poisoning threat model, adversarial red-team testing, and remediation backlog.

Adds human oversight for sensitive agent actions: asynchronous review queue, human approval/edit/override path, immutable audit trail, and reviewer operations runbook.

Decouples prompts from application code: versioned prompt registry and templates, structured variable validation, controlled promotion across dev/staging/prod, and peer change review.

Optimises inference latency and unit economics: complexity-based routing between Gemini Flash-Lite, Flash, and Pro, context caching setup, per-tenant token budget instrumentation, and unit cost dashboards.

Turnkey Gemini Enterprise deployment: edition selection, permissions-aware enterprise connector configuration, 4-week onboarding programme across 4 cohorts of up to 100 users, and 1 custom agent.

Scaled multi-department rollout building on your initial Gemini Enterprise deployment: expanded directory sync, additional cohort training workshops, AI champion enablement, department-specific indexing, and 2 additional custom agents.

Comprehensive mainframe exit assessment: automated COBOL/CICS/JCL/DB2 codebase inventory, transaction and data dependency graph, target cloud architecture mapping (GKE, Cloud Run, AlloyDB/Spanner), phased wave plan, and TCO business case.

De-risks breaking up a tightly coupled monolith: domain-driven static and runtime codebase analysis, bounded context definitions, database decoupling and anti-corruption layer strategy, API contracts, and a phased strangler-fig roadmap.

Evaluates legacy VM/on-premise applications for containerisation: runtime readiness audit, 12-factor app compliance gap analysis, session/file state extraction strategy, and target Cloud Run or GKE deployment blueprint.

Maps the upgrade path from end-of-life application runtimes (.NET Framework to modern .NET 8+, Java 8/11 to Java 21, legacy Spring): third-party dependency audit, deprecated API register, automated refactoring feasibility, and upgrade sequence.

Blueprints the migration off proprietary message brokers (IBM MQ, TIBCO, RabbitMQ, JMS): topic/queue inventory, target Cloud Pub/Sub and Eventarc topology, schema registry and ordering design, and dead-letter replay patterns.

Designs an enterprise API façade for modernising backend services: Apigee or Cloud Endpoints topology, OpenAPI specification governance, consumer-driven contract testing suites (Pact), auth mediation, and rate-limiting policies.