Built for
Contact centers and support teams
Chat, email, social messaging, and ticketing workflows
Customer service
Assist agents with consistent, compliant responses
Provide agents with editable reply suggestions that preserve human review. Use ticket context, tone controls, and team guardrails to speed replies, maintain brand voice, and reduce compliance risk across chat, email, and social messaging.
Core capabilities
Context injection, policy guardrails, multilingual rewriting
Templates and prompts tuned for customer service use cases
Visibility
Exportable audit logs
Edit history and explainability notes for QA and compliance
Business outcomes
Why customer service teams use this assistant
Designed for agents and supervisors, the assistant focuses on consistency, speed, and compliance while keeping agents in control. It supplies concise suggestions (chat, email, social), centralizes templates, and logs edits so teams can train, audit, and govern replies without slowing agents down.
- Consistent tone and brand voice through centrally managed templates and tone controls
- Compliant responses using configurable policy rules and automatic redlines
- Localized replies with regional adaptation and translation hints
- Context-aware suggestions using ticket history, product data, and SLA state
Agent-first design
How it works in your workflow
Suggestions appear inside the agent interface as editable drafts. Agents keep full control—edit, send, or escalate—and each change is logged. Policies run in the background to flag disallowed content or offer compliant rewrites. Ticket fields and recent thread history are injected to make replies accurate and actionable.
- Agent-side suggestions: edit-first workflow preserves human review and reduces accidental sends
- Ticket-context injection: include ticket summary, order info, and SLA state in prompts
- Policy guardrails: configurable per team to block or rewrite disallowed phrases
- Audit and explainability: exportable logs and notes on why a suggestion was generated
Support-focused prompts
Prompt library — ready-to-use examples
A customer-service prompt library tuned for chat, email, escalation, knowledge creation, and localization. Use these as templates or customize per team.
Short chat reply
Prompt: "Using the ticket summary: {ticket_summary}, write a concise chat reply (20–40 words) that is empathetic, offers the next step, and asks one clarifying question if needed."
- Ideal for live chat and messaging
- Limits length to keep conversations quick and focused
Email reply with subject
Prompt: "Draft a customer email subject and body from ticket notes: {ticket_notes}. Keep subject actionable and body under 5 short paragraphs; include steps to reproduce and expected ETA."
- Structured for asynchronous support and troubleshooting
- Produces actionable subject lines for inbox routing
Escalation summary
Prompt: "Summarize this ticket for Tier 2 escalation: {ticket_history}. Include root-cause hypothesis, logs attached, and recommended next actions."
- Creates concise briefs for handoff to specialists
- Includes attachments and suggested next steps
Policy-compliant rewrite
Prompt: "Rewrite this reply to remove any content that violates policy {policy_guidelines} and preserve the helpful steps."
- Runs in the background to reduce compliance risk
- Produces safe alternatives without losing substance
Multilingual support
Prompt: "Translate and adapt this response into {language}; preserve brand voice and local idioms appropriate for {region}."
- Adapts tone and idioms by region
- Useful for translation workflows and TMS handoffs
Connect where your work happens
Integrations and knowledge sources
The assistant is designed to work alongside ticketing and collaboration tools so suggestions reflect live context. Connectors surface ticket fields, recent messages, and knowledge-base content into prompts.
- Zendesk, Salesforce Service Cloud, Intercom, Freshdesk
- Gmail and hosted email; Slack and Microsoft Teams
- WhatsApp and SMS gateways for messaging channels
- Confluence and internal knowledge bases for trusted content
- CMS, product docs, and Translation Management Systems (TMS)
What data powers replies
Security, data use, and privacy
Replies are generated using ticket context and the knowledge sources you connect. Access and retention of customer data follow your configuration: you control which fields are used in prompts and can disable retention or exports as needed. Audit logs capture suggestion metadata and agent edits to support QA and compliance reviews.
- You choose which ticket fields and docs are injected into prompts
- Edit trails and explainability notes exported for audits and training
- Policies can remove or redact sensitive content before suggestions reach agents
Adopt with confidence
Rollout best practices
Start small, measure, iterate. Pilot with a single team, collect edit logs and supervisor feedback, then scale templates and policies across teams. Provide quick coaching hints and use exportable logs to update knowledge articles.
- Begin with high-volume, low-risk ticket types (billing queries, order status)
- Enable editable suggestions and audit logs from day one
- Use coaching hints to train agents and refine templates
- Gradually add stricter guardrails for regulated communications
FAQ
How does the assistant integrate with ticketing systems like Zendesk or Salesforce?
Integrations surface ticket fields, recent message history, and attachments into the prompt so replies reflect live context. Connectors typically map ticket metadata (customer info, order IDs, SLA state) and allow admins to choose which fields are injected. Integration options include embedding suggestions inside the agent UI or surfacing them via a sidebar.
What controls exist to ensure customer replies comply with company policy?
Policies and guardrails run against draft replies to flag or automatically rewrite disallowed content. Admins configure team-level policy rules and allowed/blocked phrase lists. When a policy triggers, the assistant offers compliant alternatives while preserving helpful steps; supervisors can review logs to refine rules.
Can agents edit AI suggestions before sending and how are edits audited?
Yes—suggestions are presented as editable drafts. Every agent edit, send, or discard action is recorded in an exportable audit log that includes the original suggestion, final message, and explainability notes about why the suggestion was generated.
How do we localize AI-generated replies for different languages and regions?
Use the multilingual prompt cluster to translate and adapt replies with region-appropriate idioms and tone. You can connect a Translation Management System (TMS) or restrict translations to verified templates. Admins can also create region-specific templates to ensure compliance with local phrasing.
What data is used to generate replies and how is customer data protected?
Replies are generated from the ticket context and any knowledge sources you connect. Admins control which fields are injected into prompts and can enable redaction or disable retention. Audit logs capture metadata rather than raw PII unless explicitly configured to include it.
How do we measure whether AI suggestions improve response time and customer satisfaction?
Measure adoption and impact using operational metrics already tracked by your contact center—look at suggestion acceptance rate, edit distance (how much agents change suggestions), handle time for specific ticket types, and CSAT trends for tickets where suggestions were used. Use exportable logs to correlate suggestion usage with outcomes.
What is the recommended rollout plan for agents and supervisors?
Start with a pilot on simple, high-volume ticket types. Keep suggestions editable, collect supervisor feedback, and refine templates and policies weekly. Expand to more teams once templates and guardrails prove reliable; use coaching hints and audit exports to train new hires.
How are knowledge-base articles created or updated from resolved tickets?
Use the 'Knowledge-article draft' prompt to convert resolution steps into short articles. The assistant formats step-by-step instructions, expected outcomes, and suggested tags. Supervisors review drafts before publishing to your knowledge base or CMS.
What happens when the AI cannot confidently suggest a reply (escalation workflow)?
When confidence is low or policy blocks a suggestion, the assistant offers an escalation summary prompt that prepares a concise handoff for Tier 2, including root-cause hypotheses, logs to attach, and recommended next steps. Teams can configure automatic routing for these escalations.
Can we version and approve canned responses centrally for compliance?
Yes—canned responses and templates can be versioned and published to specific teams. Admins can require supervisor approval for new templates, and audit trails record which version was used in a given reply.
Related pages
- PricingCompare plans and get started with a pilot.
- Platform comparisonSee how an agent-first assistant differs from other approaches.
- IndustriesCustomer service use cases and vertical-specific guidance.
- ResourcesBest practices for AI adoption in support teams.
- About TextaCompany background and product philosophy.