In today’s fast-paced digital environment, both end-users and support teams deal with scattered knowledge sources, rising ticket volumes and slow resolution times. Traditional IT Service Management (ITSM) tools offer limited flexibility, complex setups and vendor lock-ins.

InstaResolve-AI is an innovative solution — a complete, intelligent ecosystem designed for efficient and human-centered issue resolution.

Challenges in issue resolution

Despite significant investments in ITSM platforms like ServiceNow, Zendesk and Salesforce, organizations face:

• Fragmented knowledge: There is no unified view across available solution sources such as KBs, past incidents and expert insights.
• High ticket volumes: Burden SMEs and inflate operational costs.
• Zero learning from historical tickets: Repeat issues are manually handled without past context.
• Manual and inefficient handling: Slow down resolution and increases frustration.
• Lack of centralized knowledge access: Causes inconsistent support quality across teams.

Key capabilities:

• Multi-source integration: Connects with ITSMs like ServiceNow, Salesforce, Jira and external knowledge bases.
• Insta AI-search: Quickly retrieves the most relevant solutions from an expansive pool of knowledge.
• 1-Click incident creation: If no suitable solution is retrieved from knowledge sources, users can create detailed, AI-summarized tickets instantly with a single click.
• Peer/SME connect: Enables intelligent identification and active engagement with subject matter experts through multi-channel notifications, enabling direct collaboration for accelerated resolution.
• Automated KB generation: Converts case histories into templated KB articles for future use.
• User feedback insights: Continuously learns and improves based on real-time feedback.

The core personas of InstaResolve-AI

The diagram below illustrates the primary personas - end user and support specialist (SME), along with their core responsibilities and platform interactions:

Figure1: Key personas- InstaResolve-AI

InstaResolve-AI process flow

InstaResolve-AI delivers intelligent, streamlined resolution by automating query handling, enabling seamless collaboration and continuously improving knowledge reuse.

The diagram below illustrates the end-to-end resolution lifecycle, from knowledge search and ticket creation to SME engagement and automated KB generation.

Figure2: Process flow- InstaResolve-AI

1. AI-powered query handling:
   1. When a user raises a query, the system intelligently searches through published KBs, resolved incidents and historical tickets using a specialized KnowAssist AI agent.
2. Automated resolution or ticketing:
   1. If a solution exists, it is instantly shared with the user.
   2. If no solution is found, the user is prompted to create a support ticket.
   3. The system auto-generates a context-rich, AI-summarized ticket and routes it to the appropriate ITSM platform (e.g., ServiceNow, Jira, Salesforce)
3. SME routing and smart assistance:
   1. SMEs receive enriched tickets containing prior incidents, conversation summaries and relevant knowledge context.
   2. The system uses intelligent agents (SME Finder, Expert Assist) to route incidents to the most relevant experts based on skill and availability.
4. Real-time notifications and collaboration:
   1. SMEs are notified via integrated channels (Email, Slack, MS Teams).
   2. Agents generate suggestions and peer collaboration options to accelerate resolution.
5. Continuous knowledge enhancement:
   1. Once the incident is resolved, KB builder and Slack reporter automatically generate and submit reusable knowledge base articles.
   2. Guarded content and SME-authored KBs expand the knowledge pool for future queries.
6. Feedback loop for improvement:
   1. End-user and SME feedback is captured in real-time to fine-tune AI responses and optimize the knowledge lifecycle.

Technical architecture -InstaResolve-AI

InstaResolve-AI is built with enterprise-grade AWS services:

Figure3: Technical architecture- InstaResolve-AI

• Amazon ECS: Hosts the AI agents and admin UI.
• Amazon Bedrock: Powers advanced LLM querying (Anthropic Haiku/ Amazon Nova).
• Amazon OpenSearch serverless: Manages embeddings and vector-based semantic search.
• Amazon DynamoDB: Stores knowledge and user feedback data.
• Amazon Lambda: Handles integration, data fetching, and processing.
• Amazon Titan Embedding Models: Generate accurate embeddings for chunked KBs and incidents.

Use Cases:

• Internal IT support portals: Speed up ticket resolutions.
• Customer support centers: Reduce agent load and improve CSAT scores.
• Knowledge management teams: Auto-create and update KBs for ongoing learning.
• B2B SaaS platforms: Offer faster onboarding support to clients.
• Healthcare, BFSI and retail sectors: Solve domain-specific issues rapidly through enriched knowledge bases.

Conclusion

InstaResolve-AI delivers a future-ready solution that goes beyond just ticketing. It’s about creating an intelligent, self-evolving ecosystem where end-users are empowered, SMEs are enabled and businesses experience true operational efficiency.

Say goodbye to endless tickets, fragmented knowledge and delayed support.

Say hello to smarter resolutions, better learning and faster service — with InstaResolve-AI.