Enterprise AI services · Oracle Partner
AI services built for pilot-to-production delivery.
InferShift bridges the gap between experimental AI and resilient enterprise workloads. From AI readiness through secure architecture to private deployment on Oracle Cloud Infrastructure, we build governed systems that perform at scale.
Three service tracks
Strategy, delivery, and private deployment.
Three engagements that map to the five-stage Shift Framework. Each ends with explicit deliverables, not slides.
AI Readiness
A focused assessment of AI opportunity, operating constraints, data realities, security posture, and delivery path.
- Stakeholder interviews and use-case longlist
- Data, security, governance and integration audit
- Quantified success criteria and risk constraints
- Practical roadmap for pilot-to-production delivery
Pilot-to-Production AI Delivery
A delivery-led engagement that moves selected use cases from architecture through controlled pilot into operational rollout.
- Reference architecture on OCI with audit posture
- AI system build, workflow integration, and user experience
- Pre-agreed pilot exit criteria and scorecard
- Phased production roll-out and adoption support
Private AI & Voqu
Private AI implementation patterns for enterprises that need control over deployment, integration, data paths, and operating model.
- OCI tenancy deployment with BYOK and region pinning
- Voqu private voice AI inside your tenancy
- Integration with enterprise systems and identity
- Audit log streaming to your SIEM
The Shift Framework
Five disciplined stages. One path to production.
A systematic approach to engineering high-impact AI inside complex enterprise ecosystems. Every stage has explicit exit criteria.
Discover
Map goals, pain points, data realities, constraints, and candidate AI opportunities.
AI Readiness MemoDefine
Prioritise use cases, quantified success criteria, risk constraints, and engagement scope.
Use Case BriefDesign
Reference architecture on OCI, governance and audit posture, integration plan, pilot exit criteria.
Reference ArchitectureDeliver
Controlled pilot in production setting with pre-agreed exit gates, then phased roll-out.
Pilot ScorecardDrive
Production roll-out, adoption tracking, continuous evaluation, outcome measurement against KPIs.
Adoption DashboardEngagement model
Start focused, then move deliberately.
Enterprise AI work should begin with enough structure to reduce risk without creating unnecessary programme overhead.
Readiness call
30 minutes. Free. No slides. We end with a written readiness summary.
Focused assessment
A two-week deep-dive: data, governance, integration surface, pilot exit criteria.
Roadmap or sprint
Move into a delivery roadmap or a four-week implementation sprint with explicit success metrics.
Start with an AI readiness call.
Align your leadership and technical teams on a secure, scalable path to enterprise AI deployment. Thirty focused minutes, written summary, no pitch deck.