Practical AI solutions for real business workflows
We connect modern AI models with robust software architecture, data flows and governance so pilots can become measurable, maintainable and safe production systems.
For teams that want to automate documents, knowledge, sales, support or reporting — without inflated promises or black-box implementation.
Use-case scoping before tool selection
Privacy and access controls from the start
Human-in-the-loop for critical decisions
Integration with existing CRM, ERP and support systems
Concrete AI capabilities
Every solution is planned as a scoped workflow: data sources, user roles, quality controls, cost guardrails and operating model are defined before implementation.
Document AI & OCR
Extract, classify and validate information from PDFs, scans, forms, invoices or contracts.
- Structured data from unstructured documents
- Review queues for uncertain outputs
- Exports into CRM, ERP or internal databases
RAG Knowledge Assistant
Internal knowledge assistants with retrieval-augmented generation for policies, product knowledge, technical documentation and project knowledge.
- Answers with source citations
- Role-based access to knowledge areas
- Updatable indexing instead of static chatbots
Workflow Agents
Agentic workflows that prepare tasks, orchestrate tools and involve humans for approval when decisions are critical.
- Multi-step tasks with clear boundaries
- Tool calls with logging and fallbacks
- Approvals for risk or cost checkpoints
CRM & Sales Ops Automation
Lead enrichment, email drafts, proposal preparation, meeting summaries and follow-up flows for sales teams.
- Less manual CRM maintenance
- Consistent next steps in the sales process
- Better handovers between marketing, sales and delivery
Support Automation
Ticket triage, response suggestions, knowledge search and escalation logic for support teams with verifiable sources.
- Faster pre-sorting of requests
- Reply drafts in your brand voice
- Escalations by priority, SLA or topic
AI Dashboards & Decision Support
Dashboards that combine operational data, forecasts and AI-generated explanations without automating decision responsibility.
- Management views on process and quality data
- Anomaly and trend signals
- Explainable recommendations instead of black-box actions
Secure, verifiable and maintainable delivery
AI projects rarely fail because of the model alone. Data quality, permissions, testing, monitoring and a rollout that matches use-case risk matter just as much.
Data and access model
We define sources, retention, permissions and sensitive fields before the first prototype.
Quality assurance
Test sets, prompt versioning, evaluation criteria and manual review points reduce failure modes in day-to-day use.
Operations beyond the demo
Logging, cost controls, fallbacks and support processes are planned so the solution remains operable over time.
From use case to production-ready AI pilot
A compact path for fast validation without skipping architecture and security questions.
Discover
Use case, user roles, data sources, risks and success criteria are narrowed down together.
Design
We design the data flow, integrations, control points and the right model or tooling approach.
Pilot
A limited MVP is tested with realistic sample data and evaluated against defined criteria.
Scale
After validation, monitoring, permissions, documentation and controlled rollout follow.
AI Solutions FAQ
Does OzyCore start with an existing tool or a custom solution?
We start with the process and the data. If an existing tool is enough, we integrate it. If requirements, privacy or workflows are more specific, we build the right solution.
Can sensitive company data be included?
Yes, but only with a clear data and access model. Depending on the use case, we plan role permissions, redaction, hosting options, logging and review steps.
What is a realistic first step?
An AI readiness check or a small pilot with a clear data basis, measurable acceptance criteria and limited scope is usually the best starting point.
Ready for a realistic AI pilot?
We identify which use case is viable, what data is needed and what a safe first release can look like.