An autonomous agent that resolves customer issues end-to-end — querying systems, executing actions, and escalating with full context. No human hand-holding required.
The ibl.ai Customer Support Agent is a fully autonomous AI agent that handles customer inquiries across email, chat, and phone transcripts from intake to resolution.
It doesn't wait to be told what to do next. It queries your CRM, searches knowledge bases, updates ticketing systems, and closes cases — or escalates them with complete context when human judgment is required.
This is not a chatbot. It reasons over customer history, cross-references policies, executes multi-step resolution workflows, and operates across every channel your customers use — all without a human in the loop for routine cases.
A chatbot pattern-matches inputs and returns scripted responses. This agent reasons over live data, executes actions across connected systems, and autonomously drives cases to resolution without human prompting.
The Customer Support Agent is a true AI agent that goes beyond simple Q&A. It reasons, plans, and executes multi-step workflows autonomously while you retain full code ownership and infrastructure control.
Processes customer inquiries arriving via email, live chat, and phone transcripts through a unified reasoning pipeline — no channel-specific configuration required.
Ingests incoming messages from all channels, classifies intent, retrieves relevant policy and account data, and drafts or executes a resolution — without waiting for an agent to pick up the ticket.
Connects directly to Salesforce, ServiceNow, and other CRM or ITSM platforms to pull real-time customer account data, order history, and open case records.
Queries the CRM mid-conversation to retrieve account status, flags duplicate tickets, updates case fields, and logs resolution notes — all without human input.
Searches internal knowledge bases, product documentation, and policy repositories using semantic retrieval to find the most relevant resolution path.
Identifies the correct knowledge article, extracts the applicable resolution steps, and applies them to the customer's specific case context — not just linking to a document.
Detects when a case exceeds its resolution authority — due to complexity, sentiment, compliance requirements, or policy limits — and escalates to a human agent.
Packages the full case history, attempted resolutions, customer sentiment analysis, and recommended next steps into a structured handoff before escalating — so the human agent starts informed.
Monitors open cases for SLA breaches, unresponsive customers, or stalled resolution workflows and triggers follow-up actions automatically.
Detects cases approaching SLA thresholds, sends follow-up communications, re-queues stalled tickets, and alerts supervisors — without waiting to be asked.
Applies your organization's resolution policies, refund thresholds, and regulatory compliance rules during every interaction — consistently and auditably.
Cross-references resolution actions against configured policy rules before executing — blocking out-of-policy actions and logging every decision for audit purposes.
Generates structured resolution logs, case outcome summaries, and trend reports across support volume, resolution rates, and escalation patterns.
Compiles daily and weekly support performance reports, surfaces recurring issue patterns, and pushes summaries to dashboards or Slack and Teams channels — automatically.
The agent ingests the incoming customer inquiry from any channel — email, chat, or phone transcript. It parses the message, identifies the customer, and retrieves their full account and case history from connected CRM and ticketing systems.
The agent analyzes the inquiry against customer history, open tickets, knowledge base content, and applicable policies. It determines the most likely resolution path, assesses complexity, and identifies any compliance or escalation triggers.
The agent executes the resolution — updating CRM records, applying credits or policy actions within authorized limits, sending responses, closing tickets, or triggering downstream workflows in ServiceNow, Salesforce, or connected platforms.
The agent assesses whether the resolution was successful, whether the customer responded, and whether any SLA or compliance conditions were met. If the case remains unresolved or triggers an escalation rule, it prepares a structured handoff package.
The agent logs the full resolution trail — actions taken, systems queried, decisions made, and outcomes achieved — to your audit system. It updates dashboards, pushes summaries to Teams or Slack, and flags patterns for supervisor review.
74% of tier-1 support cases resolved autonomously, reducing average handle time from 8 minutes to under 90 seconds per case.
62% reduction in billing inquiry escalations and a 3x improvement in first-contact resolution rate across 200,000 monthly patient interactions.
Citizen inquiry backlog reduced by 58% within 90 days of deployment, with full compliance with FedRAMP and agency data governance requirements.
Dealer support resolution time reduced from 2.3 days to 4 hours average, with 80% of standard order inquiries resolved without human involvement.
Claims inquiry handling cost reduced by 67%, with auditable resolution logs satisfying state insurance regulatory compliance requirements.
First-call resolution rate increased from 41% to 79%, with $4.2M in annual support cost savings across 1.1M monthly customer interactions.
Administrative support overhead reduced by 45%, with every client interaction logged to a tamper-evident audit trail for professional liability compliance.
The agent queries and writes to Salesforce Service Cloud in real time — retrieving account records, updating case fields, logging interaction notes, and triggering workflow rules without human input.
Connects to ServiceNow ITSM to create, update, route, and close tickets autonomously. The agent applies your configured SLA rules and escalation policies directly within the ServiceNow workflow engine.
Delivers escalation alerts, case summaries, and daily resolution reports directly to Teams channels. Queries SharePoint-hosted knowledge bases and policy documents as part of its resolution reasoning.
Pushes real-time case alerts, SLA breach notifications, and supervisor summaries to configured Slack channels. Supports human-in-the-loop approval workflows via Slack for out-of-policy resolution requests.
For internal employee support use cases, the agent queries HR systems to resolve benefits, payroll, and policy inquiries — with role-based data access controls enforced at the integration layer.
Authenticates users and enforces role-based access controls via Okta or Azure AD — ensuring the agent only surfaces data each user or customer is authorized to access, with every access event logged.
You receive the complete codebase. No black-box SaaS dependency — your team can audit, extend, and modify every component of the agent. Your IP, your infrastructure, your control.
Deploy entirely within your own data center or private cloud with zero external network dependencies. Purpose-built for regulated industries, government agencies, and organizations with strict data residency requirements.
Run on AWS, Azure, Google Cloud, or any combination. The agent is infrastructure-agnostic — deploy where your data already lives and where your compliance requirements are already met.
Choose the LLM that fits your requirements — GPT-4, Claude, Gemini, Llama, Mistral, or a custom fine-tuned model. Swap models without re-architecting the agent. No vendor lock-in at the model layer.
No usage data is sent to ibl.ai or any third party. Every agent action, system query, and decision is logged to your own audit infrastructure — meeting the requirements of SOC 2, HIPAA, FedRAMP, and GDPR-regulated environments.
The majority of routine customer inquiries are resolved end-to-end by the agent without human involvement, directly reducing headcount requirements for tier-1 support operations.
Agent-assisted and escalated cases are resolved faster because the agent delivers full context, attempted resolutions, and recommended next steps to the human agent at handoff — eliminating redundant investigation time.
Enterprise deployments across 500–2,000 seat support operations consistently achieve seven-figure annual savings through autonomous resolution, reduced escalation volume, and elimination of after-hours staffing costs.
By querying all relevant systems before responding, the agent resolves cases correctly on the first interaction — reducing repeat contacts, customer churn, and the cost of re-opened tickets.
ibl.ai's enterprise-wide flat-fee licensing model eliminates per-seat and per-interaction pricing. Organizations with large support teams or high interaction volumes achieve order-of-magnitude cost advantages over SaaS alternatives.
See how ibl.ai deploys autonomous AI agents you own and control — on your infrastructure, integrated with your systems.