What this solves
12
Production-grade AI in your enterprise stack — with the governance to match.
Proof-of-concept AI is easy. Making it production-ready, secure, observable, and enterprise-compliant is the real work.
Get a quote Typical timeline: 6–16 wks
Process
How it works
01
Discover & Map
We audit your current processes and identify the highest-impact automation opportunities.
02
Design & Build
Enterprise AI projects fail most often at the last 20%: moving from a working demo to a system that passes security review, handles enterprise data residency requirements, integrates with Active Directory and existing ITSM tooling, and has the monitoring and governance controls that a regulated business needs.
03
Monitor & Optimise
Your automation ships with monitoring, alerting, and a handover pack. We stay available for optimisation.
What's included
Enterprise AI projects fail most often at the last 20%: moving from a working demo to a system that passes security review, handles enterprise data residency requirements, integrates with Active Directory and existing ITSM tooling, and has the monitoring and governance controls that a regulated business needs. We build the full production architecture — deploying on Azure OpenAI Service or AWS Bedrock for data residency, integrating with enterprise identity providers, implementing prompt logging and PII redaction, and wiring in the MLOps tooling needed to manage model versions, monitor drift, and maintain compliance over time.
What you receive
- Enterprise AI architecture document — deployment topology, data flow, security controls, and compliance mapping
- Azure OpenAI or AWS Bedrock deployment with VNet integration, private endpoints, and managed identity
- PII detection and redaction layer (Microsoft Presidio or custom) applied to all inputs and outputs
- Prompt logging pipeline with structured output to your SIEM or data warehouse
- MLOps pipeline: model version management, A/B testing framework, and performance regression testing
- AI governance runbook — approved use case register, incident response procedure, and quarterly review process
- Enterprise SSO integration and usage dashboards by team and use case for chargeback or compliance reporting
Typical outcomes
- AI capability deployed into production in an architecture that passes enterprise security review
- Data residency requirements met — no customer or regulated data transiting public model endpoints
- Full prompt and response logging with PII redaction, retention policies, and access controls
- Model performance monitoring with drift detection and alerting before degradation impacts users
- AI usage governance framework — approved use cases, prohibited inputs, and escalation procedures documented and enforced technically
- Integration with enterprise identity (Azure AD / Okta) for authentication and usage attribution
Technology we use
Ready to discuss your project?
Book a free sessionIntegrations
Tools & integrations we work with
Azure OpenAI ServiceAWS BedrockMicrosoft PresidioAzure ADTerraformKubernetesLangSmithPrometheusGrafanaPython
We integrate with your existing stack — no rip-and-replace required.
Questions
Common questions about Enterprise AI Integration.
Regulated deployments are our most common enterprise engagement. We design for the specific requirements of your sector: GDPR data residency via Azure UK/EU regions, HIPAA-compliant deployments with BAA-covered services on AWS or Azure, and FCA-appropriate logging and explainability controls for financial AI. We work with your compliance team from the architecture phase, not as an afterthought.
Yes. We produce threat model documentation as part of the architecture deliverable and can support your pen test team with technical context. We also conduct our own pre-pen-test review covering OWASP LLM Top 10 vulnerabilities — prompt injection, insecure output handling, model inversion — before any third-party assessment.
Failed enterprise AI projects almost always have one of three causes: no clear business metric to optimise for, a proof-of-concept architecture that couldn't survive security review, or no ownership for ongoing maintenance after delivery. We address all three: we start by defining measurable success criteria, we build to production standards from day one, and we include governance and MLOps tooling so the system has a defined owner and operating model after we leave.
At minimum: an approved use case register (specifying what the AI is permitted to do and with what data), prompt and response logging with defined retention and access policies, a prohibited inputs list enforced technically, human review gates for high-stakes outputs, and a quarterly model performance review process. We document all of this and wire the technical controls into the deployment.
We pin model versions in production so updates never happen automatically. When a new version is available, we run your evaluation harness against it first. If it passes, we promote it through staging to production with a brief parallel-run period. If it regresses on any benchmark, we stay on the current version until the issue is understood. No surprises in production.
Yes. All three have well-documented extension points. For ServiceNow, we use the AI Search and Now Assist frameworks or custom widget integrations. For Salesforce, we integrate via Einstein extensions or custom Lightning components calling your hosted model endpoint. For SAP, we typically build a sidecar integration layer rather than modifying core configuration. The approach depends on your platform version and customisation constraints.