Agentic AI
AI capabilities are powerful, but often need to be trained for a specific task for better accuracy, speed or specificity. Leveraging Agentic AI is the solution, and Zero Point AI can help develop these for usage in your teams.
Our AI Agent development can integrate neatly into existing systems, wihtout adding significant overhead to your teams' workloads. We are familiar with developing agents that can run locally for optimum security and control or in the cloud for a good balance of performance and cost. Our team are experienced in design too - ensuring that your requirements for a agent are met without bloating to enhance productivity without the downside of increased admin burden. We ensure AI enhances your technology ecosystem rather than complicating it.
Why Agents Matter
LLMs are great, but there are some pitfalls...
- Overly Generic: LLMs are trained for general usage, so can be less useful in specific scenarios
- Data Silos: AIs can't always see all the information they need, hindering their intelligence
- Inefficiency: Manual copying between systems wastes time and introduces errors
- Security Gaps: Poorly designed agents (particularly unseen LLM usage) create vulnerabilities and compliance risks
Proper AI Agent development transforms AI from an interesting experiment into a reliable business capability.
Agent Ideas for You
AI API Integration
Connecting your applications to AI services:
- OpenAI, Anthropic, and other language model APIs
- Specialist AI services (document processing, image analysis, speech recognition)
- Custom AI endpoints from your or our bespoke agents
- Multiple AI providers for resilience and optimal model selection
Data Source Integration
Enabling AI to access the information it needs:
- Database connections (SQL, NoSQL, data warehouses)
- Document repositories and knowledge bases
- CRM and ERP systems
- Cloud storage (SharePoint, Google Drive, Dropbox)
- Real-time data feeds and APIs
- Legacy systems and proprietary formats
Application Integration
Embedding AI into existing software:
- Microsoft 365 and Google Workspace integration
- CRM platforms (Salesforce, HubSpot, Dynamics)
- Collaboration tools (Teams, Slack, Zoom)
- Customer service platforms (Zendesk, Intercom)
- Business intelligence and analytics tools
- Custom internal applications
Workflow Automation
Building AI-enhanced processes:
- Trigger-based AI processing (new document → analysis)
- Multi-step workflows with human approval points
- Scheduled AI tasks (daily reports, weekly summaries)
- Event-driven processing (form submission → AI routing)
- Integration with workflow platforms (Zapier, Power Automate)
Security and Compliance
Integration creates new pathways for data - security must be paramount:
- Authentication: OAuth 2.0, API keys, JWT tokens with appropriate scoping
- Encryption: TLS for data in transit, encryption at rest where required
- Access Control: Role-based permissions and principle of least privilege
- Audit Logging: Comprehensive logging of AI interactions and data access
- Data Governance: Clear policies on what data AI can access and process
- Compliance: GDPR, HIPAA, and industry-specific requirements
Security-First Integration
We implement defence-in-depth: multiple layers of security controls ensure that even if one fails, others remain. This includes input validation to prevent injection attacks, rate limiting to prevent abuse, data sanitisation before sending to AI, and output validation before writing back to systems. For sensitive data, we can implement tokenisation, masking, or anonymisation so the AI never sees actual personal information.
Performance and Reliability
Integrations must be fast and dependable:
- Response Time Optimisation: Caching, async processing, and efficient queries
- Failover and Retry Logic: Handling transient failures gracefully
- Load Balancing: Distributing requests across multiple instances
- Circuit Breakers: Preventing cascade failures in distributed systems
- Monitoring and Alerting: Proactive detection of issues
- Service Level Agreements: Clear expectations and metrics
Data Pipeline Development
Many AI applications require sophisticated data pipelines:
- ETL Processes: Extracting, transforming, and loading data for AI consumption
- Real-Time Streaming: Processing data as it arrives
- Data Quality: Validation, cleansing, and enrichment
- Vector Databases: Storing embeddings for semantic search and RAG
- Incremental Updates: Keeping AI systems current without full reprocessing
User Experience Integration
AI should feel like a natural part of existing interfaces:
- In-Context AI: AI features appear where users already work
- Progressive Enhancement: AI augments rather than replaces existing functionality
- Clear Indicators: Users know when AI is being used
- Consistent Patterns: AI interactions follow familiar UI paradigms
- Feedback Mechanisms: Users can improve AI through corrections
Legacy System Integration
Modern AI can work with older systems:
- API wrappers around legacy interfaces
- Database integration for systems without APIs
- Screen scraping as a last resort (with appropriate safeguards)
- File-based exchange for batch processing
- Middleware for protocol translation
We've successfully integrated AI with systems decades old, bringing modern capabilities to established infrastructure.
Common Integration Scenarios
Document Processing Integration
AI analysis of documents within existing systems:
- Email attachments automatically processed on arrival
- SharePoint documents indexed with AI-generated metadata
- Contract management systems enhanced with clause extraction
- Expense systems with automatic receipt data extraction
Customer Service Integration
AI support within helpdesk platforms:
- Ticket categorisation and routing
- Suggested responses based on knowledge base
- Sentiment analysis and escalation triggers
- Chatbots integrated with ticketing systems
Business Intelligence Integration
AI insights within analytics platforms:
- Natural language queries of business data
- Automated insight generation from dashboards
- Anomaly detection and alerting
- Natural language report generation
Communication Platform Integration
AI assistants within collaboration tools:
- Teams/Slack bots for common queries
- Meeting transcription and summarisation
- Email drafting assistance in Outlook/Gmail
- Document summarisation in shared drives
Monitoring and Maintenance
Integration is an ongoing responsibility:
- Health Monitoring: Tracking availability, performance, and errors
- Usage Analytics: Understanding how AI is being used
- Cost Tracking: Monitoring API usage and associated costs
- Quality Metrics: Measuring accuracy and user satisfaction
- Version Management: Handling API changes and deprecations
- Incident Response: Quick resolution when issues arise
Documentation and Handover
Comprehensive documentation ensures long-term success:
- Architecture diagrams and data flow documentation
- API specifications and integration guides
- Configuration management documentation
- Troubleshooting guides and runbooks
- User guides for AI-enhanced features
- Administrator guides for management and monitoring
Cost Considerations
Integration costs depend on several factors:
- Complexity: Number of systems, data volumes, transformation needs
- Custom Development: Extent of bespoke coding versus configuration
- Testing Requirements: Rigor needed based on criticality
- Legacy Challenges: Difficulty integrating with older systems
- Ongoing Costs: Hosting, API fees, maintenance requirements
We provide transparent estimates and help you understand total cost of ownership.
When to Integrate
Integration makes sense when:
- AI capabilities need to be accessible in existing workflows
- Multiple systems must share AI-processed data
- Manual data transfer creates bottlenecks or errors
- User adoption depends on seamless experience
- Scale requires automation rather than manual processes
- Compliance requires audit trails and access controls