AI agents in customer service that don't bluff.
SearchUnify's AI agents for customer service answer customers, draft for reps, route tickets, flag escalations, and audit cases.
Each one retrieves from your systems before acting, shows its sources, and hands off to a person or the next agent when it can't.
No source, no action.
Salesforce · ServiceNow · Zendesk · Slack · Khoros + 40 more connectors and MCPNo knowledge migration · SOC 2 Type II · ISO 27001 · HIPAA
SSO login started failing after we rotated our IdP certificate. Getting "SAML assertion invalid".
- Salesforce KB· KB-1182 “Rotating IdP certificates”entitled ✓
- Docs· auth-guide#saml-troubleshootingpublic
- Jira· ENG-4471internal — withheld
After a rotation, the SP metadata still points at the old cert. Re-upload the new IdP certificate under Setup → SSO → Identity Provider and re-save — the assertion validates on next login.
Enterprise, P2. 27-message thread. Data export job stalls at 80% for accounts >2M rows.
- Salesforce· case #41077 “export stalls at 80%”precedent ✓
- ELK MCP· pulled customer logs · batch 41 → OOMcollected ✓
- Jira MCP ENG-4471 created · linked · logs attached
- Slack MCP #eng-data swarm · timeline posted · @on-call
- Salesforce MCP status → Engineering engaged
Timeline: reported 12 Aug → repro 13 → workaround failed 15 → logs today.
Root cause (ELK): 2M-row batch ceiling — #41077 fix: chunked export.
Draft reply: “Enable Chunked Export under Admin → Data…”
P3 · SSO/SAML · German · 3 reassignments · customer replied 4× in 24h · $1.2M ARR, renewal in 41 days.
Assigned to M. Chen · Tier-2 · SAML 92% · DE native · load 4/8 · CSM notified. Why: 9 of 14 cases with this pattern escalated; Chen is the only available rep with the skill and the language.
Scored 22/25 · process gap: no KB linked before close — same pattern in 6 of Chen's last 20 cases → coaching plan updated
Drafted KB-1182-DE from the resolution · reviewer: Chen · published to help center + agents' retrieval layer. Next German SSO question: the L1 agent has a source.
Real conversations. Real outcomes.
Support leaders on what actually changed after they switched the harness on, in their own words.
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Customer Pulse · August 2026GlobalFoundries“Self-service went up, case conversion went down — and our engineers finally had one place to find the answer.”
Ilavarasu RadjuNilamegameLead Business System Analyst, GlobalFoundries -
Customer Pulse · June 2026Saviynt“We maximised deflection and closed the knowledge gaps — customers and engineers get accurate answers instantly.”
Sahana ParameswarappaLead Support Site Management, Saviynt -
Customer Pulse · June 2026TechnologyOne“Deeper community engagement, more self-service, and members connected to the right knowledge without friction.”
Roxanne HoareDirector of Customer Community, TechnologyOne -
Customer Pulse · May 2026Revenera“Faster resolution, empowered engineers, smoother outcomes across our entire technical support operation.”
Sudheendra KumarDirector, Technical Support, Revenera -
Customer Pulse · April 2026Celonis“Knowledge finally has visibility. Self-service adoption is up and our support outcomes are more consistent.”
Michelle StumpfHead of Knowledge Management · KCS Certified Trainer, Celonis
- AI 100 · 2026Empowering intelligent knowledge management
- PEAK Matrix® 2026Major Contender — Enterprise Search
- Leader · 26 consecutive quartersEnterprise Search Grid® since 2020
- Champion · 5th straight yearEmotional Footprint 2026
- Gold · 2026AI-Powered Knowledge Management
- Major Player · 2025Knowledge Discovery Software
Most AI support pilots stall for four reasons.
Not because the models aren't good enough. Because of what got sold: a builder instead of an agent, a bot instead of a crew, an answer instead of a resolution, and a guess instead of a source.
Builders, not agents.
- The market
Agentforce, ServiceNow, Microsoft sell you a builder. Months later you have a project, not an agent that does the job.
- SearchUnify
Purpose-built agents for well-defined support jobs — L1 answers, rep assist, routing, QA, knowledge. Live on day one.
Bots, not crew.
- The market
A standalone chatbot at one step of the journey. No shared context, no hand-off, no idea what happened before or after.
- SearchUnify
A support harness: agents that share context and hand work to each other from the front door, beside the rep, and behind the queue, end to end.
Answers, not resolution.
- The market
Retrieval and Q&A stop at the answer. Someone still has to open the Jira, route the case, update the status, write the article.
- SearchUnify
Agents that act: they resolve the case, create the Jira, swarm in Slack, assign the right rep, and publish the KB through MCP, with approval where you want it.
Bluffs, not sources.
- The market
When the model doesn't know, it invents — confidently. Nobody can trace where the answer came from when it's wrong.
- SearchUnify
No source, no action. Every answer, draft, route and score is retrieved first, cited, and logged, or the agent hands off.
Four gaps, one harness. No source, no action, running across the whole support journey, live in a week.
Connect. Deploy. Resolve.
No model training. No agent building. No knowledge migration. Your systems stay where they are; the harness runs on top.
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1
Connect
Point it at what you already run — CRM, ticketing, KB, docs, community, telemetry, LMS.
40+ connectorsMCPpermission-aware -
2
Deploy the agents
Purpose-built for defined support jobs. Switch on the ones you need, scoped to the topics you choose. Live on day one.
L1 SupportAgent PartnerRouting · QA · Knowledge -
3
They resolve, with a source
Retrieve first, then act — answer, draft, route, create the Jira, publish the KB. Every action cited, logged, and hand-off ready.
FRAG groundingacts via MCPno source, no action -
4
The crew gets smarter
QA scores every case, Knowledge writes the article, Routing learns the pattern — so the front door has a source next time.
100% QAKCS articlesloop closed
AI Agents harnessed for resolution.
Every vendor runs the same models. SearchUnify is the harness around them: the knowledge, retrieval, orchestration and governance that make agents resolve cases instead of deflecting them.
Every channelOne brain, consistent answers
- Portal
- Chat
- Voice
- In-product
- Agent desk
Purpose-built AI agentsNot one mega-bot
- L1 Support
- L2 Troubleshooting
- Agent Partner
- Escalation
- Knowledge
- Case Quality
Orchestration and governancePlan, retrieve, act, verify
- Workflow builder
- Guardrails
- Human in the loop
- Evals
- Audit trail
RetrievalAgentic RAG, the right index for each question
- Lexical
- Dense
- Graph
- SQL
- Community
- Sufficiency check
Actions and toolsAnswers become outcomes
- MCP tools
- Skills
- Agent-to-agent
- Ticketing
- Entitlements
- Billing
Agent-ready knowledgeWritten for machines, not just people
- Cleanup and dedupe
- Knowledge from cases
- Freshness
- Versions and entitlements
- Gap detection
Connected dataPermission-aware, no copy-and-hope
- Knowledge base
- Cases
- Community
- Product docs
- CRM
- Telemetry
Any modelSwappable, and not where the results come from
- GPT
- Claude
- Gemini
- Open-weight
What changes when the harness is on.
Aggregated from published customer case studies. These are not projections, but what support teams measured after switching the agents on.
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60%
increase in case deflection
More customers self-served at the front door with a cited answer, not a bluff.
-
45%
reduction in escalations
Routing spots the at-risk case and lands it with the right rep before the customer asks for a manager.
-
35%
faster resolution
Reps start with the timeline, the logs and the precedent already in front of them.
-
40%
higher CSAT
Consistent, sourced answers and coaching built from 100% of closed cases, not a 2% sample.
-
20%
higher renewals
Fewer bad support moments in the renewal window. Support becomes a reason to stay.
Figures aggregated across published SearchUnify case studies · read the studies
Autonomy you can audit.
Every agent action is scoped, logged, and reversible. You decide what it may answer, whom it may answer for, and when it must hand off to a person, and the trace shows you exactly what happened, every time.
- Permission-aware retrieval: agents inherit your existing entitlements, not a copy of them
- Source citation on every generated answer, traceable to a document you own
- Topic-level guardrails and confidence thresholds for handoff
- Full audit trail: what was retrieved, what was said, and why
- SOC 2 Type II · ISO 27001:2013 · HIPAA · GDPR
retrieve "saml assertion invalid"
↳ salesforce/kb 3 docs · entitled ✓
↳ docs/auth-guide 1 doc · public
↳ jira/ENG-4471 blocked · internal only
confidence 0.91 > threshold 0.80
action answer_customer
↳ cited KB-1182, auth-guide#saml
handoff not required
✓ resolved in 11s · logged
What do SearchUnify AI Agents do?
SearchUnify AI Agents help enterprises automate the customer-support lifecycle, from answering customer questions and resolving cases to classifying tickets, managing escalations, auditing case quality, and assisting support agents. Each agent is purpose-built for one support job, such as L1 support, L2 troubleshooting, routing, escalation, knowledge, or case quality. The agents work together on the same case, so context carries from the front door to the resolution.
What are AI agents for customer service?
AI agents for customer service are AI systems that take action on support work instead of only answering questions. They can resolve a case, update a ticket, route it to the right team, draft a reply for a rep, or write a knowledge article. SearchUnify's agents retrieve from your enterprise systems before every action and hand off to a person when they can't find a reliable source.
What is a multi-agent customer service platform?
A multi-agent customer service platform runs several specialized AI agents that share context and pass work to each other, instead of relying on one standalone chatbot. SearchUnify is the harness around those agents. It supplies the knowledge, retrieval, orchestration, and governance that make agents resolve cases instead of deflecting them.
How do SearchUnify AI Agents avoid hallucinations?
SearchUnify AI Agents follow one rule: no source, no action. Every answer, draft, route, and score is retrieved first, cited, and logged. When an agent can't find a reliable source, it hands the case to a person or to the next agent rather than guessing. Confidence thresholds and topic-level guardrails control when an agent acts on its own.
Which systems do SearchUnify AI Agents connect to?
SearchUnify AI Agents connect to Salesforce, ServiceNow, Zendesk, Jira, Slack, Confluence, ELK, Zoomin, Khoros, and more than 40 other systems through prebuilt connectors and MCP. They retrieve from your CRM, ticketing, knowledge base, product docs, community, and telemetry where that data already lives, with no knowledge migration required.
Do I need to build or train my own AI agents?
No. SearchUnify AI Agents are purpose-built for well-defined support jobs and are live on day one. There's no model training, no agent building, and no knowledge migration. You connect your systems, deploy the agents your team needs, and scope each one to the topics you choose. See it live with a demo.
Which AI models does SearchUnify support?
SearchUnify is model-agnostic. It works with GPT, Claude, Gemini, and open-weight models, and you choose where the models run and where the results come from. Because SearchUnify handles the retrieval and governance, you can swap models without rebuilding your agents.
How does SearchUnify keep AI agents secure and auditable?
Every agent action is scoped, logged, and reversible. Retrieval is permission-governed and inherits your existing entitlements, so agents see only what the user is allowed to see. Each generated answer is traceable to a source document, and a full audit trail records what was retrieved, what was said, and why. SearchUnify is SOC 2 Type II and ISO 27001 certified and supports HIPAA and GDPR compliance.
What results do support teams see with SearchUnify AI Agents?
In SearchUnify's published customer case studies, support teams reported a 60% increase in case deflection, 45% fewer escalations, 35% faster resolution, 40% higher CSAT, and 20% higher renewals. These figures are aggregated results measured after the switch, not projections.
How is SearchUnify different from AI agent builders?
Agent builders give you a toolkit, and your team spends months creating agents before the first case is resolved. SearchUnify ships purpose-built AI agents for customer service that are live on day one and grounded in your own enterprise knowledge. Every action is retrieved, cited, and governed.
Bring your hardest ticket.
Not a canned demo. Bring a real case your current tooling can't resolve, and we'll connect a sandbox to your knowledge and watch the agent retrieve, cite, and answer it live.
If it can't find a source, it won't bluff. It will tell you and hand off.