How do I request a Forethought proof of concept / proof of value, and what data do you need (past tickets, KB, integrations)?

Most teams don’t need another demo that answers a handful of FAQs—they need proof that an AI agent platform can move real metrics on their real data. That’s exactly what a Forethought proof of concept / proof of value (POC/POV) is designed to do.

Below is a clear breakdown of how to request a Forethought POV, what data we need (past tickets, knowledge base, integrations), and how to set expectations internally so the outcome is board-ready.

Quick Answer:

• To request a Proof of Value: Submit the “Get Started” form or request a demo, and ask for a POV scoped to your environment.
• Data we typically need: At least ~20,000 historical tickets, a connected knowledge base (help center + internal docs where possible), and access to your core support systems (Zendesk, Salesforce, Freshdesk, Intercom, etc.).
• Time to value: Most customers launch an initial POV in days, not months, using no-code setup and native integrations.

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How to Request a Forethought Proof of Concept / Proof of Value

1. Start with the “Get Started” or Demo Request

The fastest way to request a Forethought POV is to:

1. Go to: Get Started
2. Fill in:
   • Your name and company
   • Role (CX leader, Support Ops, IT, etc.)
   • Helpdesk / CRM you use (e.g., Zendesk, Salesforce, Freshdesk, Intercom)
   • Monthly ticket volume and channels (chat, email, voice, etc.)
3. In the comments/notes field, add a line like:
   • “We’d like to run a Proof of Value using our past tickets, knowledge base, and current integrations.”

This flags your request as more than just a generic demo—you’re signaling you want a measurable, data-driven evaluation.

2. Align on POV Goals During the First Conversation

In your first conversation with the Forethought team, we’ll help you define what success looks like. Typically, POV goals focus on one or more of:

• Deflection & resolution rate:
  How many tickets can Solve (the omnichannel AI agent) fully resolve end-to-end?
• First response time (FRT):
  How much can we cut time-to-first-touch across channels?
• Time-to-resolution (TTR):
  How quickly can AI resolve or prepare tickets for your agents?
• CSAT and experience quality:
  How does AI-driven support impact customer satisfaction versus your baseline?
• Operational efficiency & cost:
  What ROI can you expect from fewer repetitive tickets and faster workflows?

We then scope a POV around the channels, use cases, and workflows that will prove or disprove value as quickly as possible.

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What Data Forethought Needs for a Proof of Value

A Forethought POV is only as strong as the data you let it learn from. Our multi-agent system is “trained on your data,” not generic internet content, so we’ll work with you on three main inputs:

• Historical support tickets
• Knowledge base & internal documentation
• Integrations to your live systems

1. Historical Ticket Data: Foundation for Real-World Performance

Forethought’s AI performs effectively with:

• 20,000+ historical tickets (email or chat) for strong model performance
• At least 2,000 tickets per month to operate smoothly and produce meaningful metrics

Why ticket data matters

Your tickets teach the AI:

• How customers actually phrase problems
• What intents map to which resolutions
• Which workflows and policies agents follow to close cases
• How your tone, brand voice, and escalation patterns look in practice

What we typically ask for

• Export or API access to historical tickets from:
  • Zendesk Support
  • Salesforce Service Cloud
  • Freshdesk
  • Intercom
  • Or your current helpdesk/CRM via API connector
• Fields that help with triage and routing:
  • Subject, body, and channel
  • Existing tags or categories
  • Priority, status, and assigned group
  • CSAT or outcome where available

How it powers the Forethought modules

• Solve: Learns how to reason through customer intents and generate on-brand, accurate responses.
• Triage: Builds ticket classification models to auto-tag, prioritize, and route inbound volume.
• Assist: Powers AI-generated replies and summaries inside your helpdesk to speed agents up.
• Discover: Identifies patterns and knowledge gaps from real interactions to suggest new articles and workflows.

If you don’t have 20,000 tickets yet, still reach out—our team can discuss options and set realistic expectations for a smaller data footprint.

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2. Knowledge Base (KB) and Internal Documentation

Forethought learns from both customer-facing and internal-facing knowledge. For a strong POV, we’ll want access to:

Customer-facing knowledge sources

• Public help center articles (e.g., Zendesk Guide, Salesforce Knowledge, Intercom Articles, Help Scout, custom docs)
• FAQs, step-by-step guides, troubleshooting flows
• Policy pages (billing, refunds, SLAs, account changes, returns, etc.)

Internal-facing knowledge sources

• Internal runbooks and macros
• Agent playbooks / SOPs
• Policy documentation (edge cases, exceptions, regulatory rules)
• Private Confluence/Notion/SharePoint spaces used by support

This mix lets the AI:

• Answer common and complex questions with grounded content
• Respect exceptions and edge-case handling rules
• Use “hallucination mitigation” to verify facts against your knowledge before responding

Key point: You stay in control. Role-based access and permissions ensure internal-only docs are used appropriately and surfaced only where allowed.

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3. Integrations and System Connections

Agentic AI only delivers full value when it can take action, not just answer questions. To do that, we connect Forethought to your stack so Autoflows can execute real updates.

Core support system integrations

We typically integrate with:

• Helpdesks / CRMs:
  • Zendesk
  • Salesforce Service Cloud
  • Freshdesk
  • Intercom
  • (Plus other major CRM/support platforms via 70+ native integrations and API connectors)
• Channels:
  • Web chat and in-app messaging
  • Email
  • Voice and IVR
  • Mobile and SMS
  • Slack or internal channels as needed

Operational systems for actions

Depending on your use cases, we may connect to systems like:

• Ecommerce/order management (for order tracking, returns, exchanges)
• Subscription/billing platforms (for payment issues, upgrades, renewals)
• Identity/auth systems (for password resets, account verification)
• Scheduling systems (for appointment booking or changes)

These integrations allow Forethought’s agents to:

• Look up order or account details
• Execute refunds, changes, or re-shipments in line with your policies
• Schedule or cancel appointments
• Update records and status across systems
• Create, update, and close tickets automatically

All of this activity is governed by your business policies and controlled through permissions, so the AI operates within guardrails.

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How the Forethought POV Typically Runs

Step 1: POV Design & Scoping

Together we define:

• Primary metrics: e.g., deflection, resolution rate, FRT, CSAT, ROI
• Channels included: chat, email, voice, mobile, etc.
• Use case focus: e.g., order tracking, account access, billing, subscription changes
• Success criteria: clear thresholds for “pass/fail” or “go/no-go” decision

This design phase ensures the POV looks like a mini-version of production, not a lab-only experiment.

Step 2: Connect Your Data & Integrations

Once we have access approvals:

• We connect to your helpdesk/CRM
• We ingest and process historical ticket data
• We sync your knowledge base and internal docs
• We configure integrations needed for key Autoflows

Because Forethought uses native integrations and a no-code setup, customers typically launch within days, not months.

Step 3: Configure Policies, Guardrails, and Governance

Before anything goes in front of customers, we:

• Align on business policies (what the AI can and cannot do)
• Set up role-based access for your team
• Enable hallucination mitigation, so the AI verifies facts against your data before responding
• Configure handoff rules to human agents inside your helpdesk
• Ensure audit-ready logs are enabled so you can review behavior and responses

This is where we prove that agentic AI can take action while you stay firmly in control.

Step 4: Launch the POV in a Controlled Environment

We then launch in one of three ways:

• Limited audience live pilot (e.g., a subset of your website chat or a specific segment)
• Channel-specific rollout (e.g., chat only, or email-only response suggestions)
• Shadow mode (AI answers in the background while agents see and approve suggestions)

During the POV window, you’ll see the multi-agent system in action:

• Solve: Resolves frontline tickets end-to-end where policies allow.
• Triage: Classifies and routes inbound tickets with richer context.
• Assist: Surfaces summaries, reply suggestions, and next best actions to agents.
• Discover: Surfaces insights and knowledge gaps from real conversations to generate articles and suggest new workflows.

Step 5: Measure, Optimize, and Decide

Throughout the POV, we track:

• Deflection and resolution rate (including AI-only resolution)
• First response time improvements
• Time-to-resolution reductions for both AI and human-assisted tickets
• CSAT trends and qualitative feedback
• Agent productivity (e.g., time saved per ticket, macro usage vs AI suggestions)

At the end, you’ll have a clear picture of:

• Where AI can resolve more, escalate less, and move faster
• Which workflows should be automated next
• What ROI you can expect from rolling out Forethought more broadly

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Security, Compliance, and Data Handling in a POV

For most enterprise support leaders and IT teams, a POV is only viable if it meets strict security and compliance expectations.

Forethought is built for trust, backed by standards such as:

• SOC 2 Type II
• HIPAA
• GDPR
• CCPA
• NIST Cybersecurity Framework

We apply:

• Encryption in transit and at rest
• Role-based access controls and permissions
• Audit trails and logs for monitoring
• Policy-bound access to internal vs external knowledge

During the initial POV scoping, we’ll align with your security team on data flows, retention expectations, and any additional controls you require.

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What You Should Prepare Internally Before Requesting a POV

To make your Forethought proof of concept / proof of value smooth and fast:

1. Inventory your systems and channels

   • Which helpdesk/CRM do you use?
   • Which channels do you want in-scope (chat, email, voice, mobile, etc.)?

2. Estimate ticket volume

   • Total historical tickets available
   • Average monthly ticket volume (email + chat)

3. Map your knowledge sources

   • Public help center location
   • Internal docs or runbooks that support uses today

4. Clarify decision criteria

   • What metrics will define “success” for your team or your CFO?
   • What timeline do you need to hit to impact current planning cycles?

5. Loop in IT/Security early

   • Share that Forethought is SOC 2 Type II, HIPAA, GDPR, CCPA, and NIST aligned
   • Schedule time to review integrations and data handling

Doing this groundwork means your POV can move from “interesting demo” to “board-ready business case” quickly.

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Final Verdict

Requesting a Forethought proof of concept / proof of value is straightforward: use the Get Started flow, ask explicitly for a POV, and be ready to connect your real ticket data, knowledge base, and core integrations. With at least 20,000 historical tickets, a connected KB, and access to your helpdesk and key systems, Forethought’s multi-agent platform can show—within days—how agentic AI can cut first response time, improve resolution rates, and reduce operational drag across your support organization.

Next Step

Get Started