AI solutions designed around your operational requirements.
From enterprise strategy to production systems, Rennix AI delivers seven core solution areas engineered for security, integration, and measurable business value.
Enterprise AI Strategy
We help leadership move from ambition to a credible, funded AI program grounded in your data, systems, and regulatory context.
- AI readiness assessments and capability mapping
- Use-case identification and prioritization
- Implementation roadmaps and delivery planning
- Governance, risk, and responsible-AI frameworks
- Enterprise adoption and change management
A practical path from opportunity mapping to an owned production program.
Agentic AI & AI Agents
Custom AI agents and copilots that reason over enterprise data, use business tools, and complete multi-step workflows with the right guardrails.
- Workflow automation across existing systems
- Agents that query data and call enterprise tools
- Employee copilots and customer-facing assistants
- Human-in-the-loop controls and audit trails
- Task orchestration and monitoring
Reason · plan · act
Every tool call is permissioned, logged, and recoverable.
Generative AI & RAG
Retrieval-augmented systems that answer from your knowledge accurately, with citations, and without exposing sensitive data to third parties.
- Secure knowledge assistants and enterprise search
- Document intelligence and extraction
- Multilingual (Arabic & English) chatbots
- Retrieval-augmented generation grounded in your corpus
- Guardrails, source attribution, and evaluation
Computer Vision & Video Intelligence
Real-time analytics on your existing camera infrastructure for safety, security, and operations, processed locally at the edge.
- Incident detection and real-time alerting
- Intrusion, safety, and access-violation monitoring
- People and vehicle analytics
- Operational monitoring and evidence capture
- Works with existing CCTV, NVR, and VMS
Illustrative detection view. Video can remain on-site while alerts and evidence are produced locally.
AI Platform Engineering
The infrastructure layer for private AI: model deployment, orchestration, and monitoring engineered for reliability, scale, and control.
- Private AI platforms and model deployment
- LLM integration and API orchestration
- Retrieval, evaluation, and observability pipelines
- Scalable, secure AI infrastructure
- Monitoring, logging, and cost control
One governed layer for models, retrieval, monitoring, and enterprise integration.
Business & Decision Intelligence
Turn organizational data into decisions with natural-language analytics and executive dashboards that anyone can use in Arabic and English.
- Natural-language analytics and Text-to-SQL
- KPI cards, interactive charts, and dashboards
- Automated insights and forecasting
- Executive decision-support systems
- Governed, auditable access to your databases
Custom AI Development
When off-the-shelf won't do, we design and ship custom AI applications integrated with your systems and supported for the long term.
- End-to-end design and development
- Integration with existing enterprise systems
- Testing, deployment, and MLOps
- Ongoing support and iteration
- Built to your security and compliance requirements
A delivery path for requirements that do not fit an off-the-shelf product.
Questions about planning an enterprise AI solution.
The architecture begins with your operational requirements—not a predetermined model or deployment pattern.
Which AI solution should an organization start with?
Start with a high-value problem that has a clear owner, usable data, and a measurable outcome. Discovery compares value, feasibility, integration effort, data sensitivity, and governance before selecting a pilot.
Can Rennix AI integrate with existing enterprise systems?
Yes. Solutions can be designed around existing databases, documents, applications, cameras, identity controls, and operational workflows. The exact connection method and access level are agreed during technical discovery.
Can the solution use different AI models?
Yes. Open and commercial models can be evaluated per workload, language, privacy requirement, latency, and cost. The selected model is one component of the architecture rather than the architecture itself.
What happens before a pilot reaches production?
The team validates data access, integration, quality, security controls, user workflow, and success measures. Production planning then covers monitoring, ownership, support, change management, and ongoing evaluation.
Not sure where to start?
Book a consultation and we'll help you identify the highest-value AI use cases for your organization.
Book a Consultation