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One experience layer for Jeppesen ForeFlight support.
Aviation leaves no room for error. Maven AGI brings enterprise-grade AI to the Jeppesen ForeFlight support team — embedded directly in Zendesk, grounded in your knowledge, and built to make every interaction faster and more accurate.
Executive Summary
Jeppesen ForeFlight's Tier 1 Customer Support team supports a global pilot community with high-stakes, technical, accuracy-mandatory questions. The pages that follow lay out what Maven AGI is, how it would work inside the team's existing Zendesk workflow, and what the rollout could look like — starting small, expanding only at the pace the org chooses, and built so the same approach can extend across the broader organization later if and when that makes sense.
Discovery
What we heard from Jeppesen ForeFlight. Learnings from meetings with the support team — this is the state of play today and where the leverage is.
Every inbound email — a 30-second password reset or a 2-hour weight-and-balance calc for a Gulfstream — lands in the same queue and gets the same agent attention until a human sorts it out. No automated routing today by complexity or customer value.
No systematic way to flag high-ARR or VIP accounts (e.g., a $2.7M ARR customer) for priority handling in Zendesk. A NetJets chief pilot and a GA hobbyist asking the same question get differentiated treatment only when an individual agent recognizes the name — judgment, not system logic.
Agents tab-switch out of Zendesk into Center to pull account details — copying an email, searching, clicking through tabs to see plan status, purchase history, and usage. A prior sync attempt pulled day-old data, which was useless for subscription questions where customers write in immediately after a change.
~80–85% of email volume is unauthenticated — customers have been trained for years to email team@foreflight.com. That limits auto-personalization and blocks pulling account context automatically at the point of contact.
The pilot's guide is a 1,200-page PDF. Keyword search doesn't reliably surface answers, so agents fall back to ad hoc tools — Grok, ChatGPT, Google — adding time and inconsistency to every non-trivial ticket.
Recurring examples with no real decision-making: blank "sent from my iPhone" emails, data-sync toggle checks (including a military-account edge case), and "thanks, that solved it" replies that still require a human to close. Steven estimated these categories alone represent meaningful daily agent time across ~14,200 monthly emails.
Amplitude isn't connected to the support workflow, so there's no way to identify low-engagement customers (e.g., someone who's never used daily weather or logbook) and proactively nudge them into stickier usage — directly tied to renewal risk (flight-plan filers renew at ~98% vs. ~50% for non-filers).
When a customer emails team@foreflight.com or writes into Jeppesen ForeFlight, there's no reliable way to know which of the ~80 Jeppesen ForeFlight products they're asking about without an agent reading the message and pattern-matching. That guess drives routing, knowledge lookup, and SLA — and it happens by hand on every ticket.
Every inbound email — a 30-second password reset or a 2-hour weight-and-balance calc for a Gulfstream — lands in the same queue and gets the same agent attention until a human sorts it out.
Maven AGI Overview
Maven AGI is an enterprise AI platform built specifically for customer support. It's made up of two products that can be deployed independently or together: Maven Copilot, which assists human agents inside the ticketing systems they already use, and Agent Maven, which resolves customer issues autonomously across chat, email, voice, web, SMS, Slack, and in-app channels. Both are powered by the same underlying reasoning engine, and both connect to the systems and knowledge sources the team already has in place.
Maven Copilot: helps agents work faster
Maven Copilot lives natively inside Zendesk, Salesforce, HubSpot, and other ticketing platforms. It drafts suggested replies, summarizes long tickets, surfaces the right knowledge article at the right moment, and pulls in context from connected systems — all without taking the agent out of their existing workflow. The agent stays fully in control of every customer interaction.
Agent Maven: resolves customer issues autonomously
Agent Maven is a customer-facing AI agent that resolves issues end-to-end on the categories of questions the team has approved. It operates across chat, email, voice, web, SMS, Slack, and in-app — wherever the team chooses to deploy it. For everything outside its approved scope, it escalates cleanly to a human, with full conversation context attached so the agent never starts from zero.
Actions: not just answers
Maven doesn't just answer questions — it can take real actions in the team's existing systems. Through native integrations and the Maven App Marketplace, Maven can do things like look up account status, reset a password, check an order, update a record, or trigger a workflow in any connected backend. What Maven can do is determined by which Actions the team configures and approves.
Maven works alongside existing teams, not in place of them. The starting point is making the team more effective at the work they already do — and expanding from there at whatever pace the team chooses.
Why Maven AGI
The technology that makes 'one front door' safe to actually ship — not just safe to talk about.
100+ pre-built connectors including Salesforce, Snowflake, Jira, Slack. No per-integration fee. Reads and writes, not one-way.
Maven doesn't guess. Every response is grounded in your Salesforce records, KB, and contracts — and escalates when unsure.
Sub-2-minute average resolution on tickets Maven handles. Consistent SLA regardless of the day's ticket mix.
Agent Studio lets ops teams build and tune agents without engineering. SDK for the deep customizations you actually need.
You get a team, not a login. Category-by-category rollout, live tuning, and dedicated implementation support.
Knowledge, agents, analytics, and integrations on a single platform — no stitching, no separate vendors, one Graph of Record.
Continuous Learning
Every interaction makes Maven smarter — and Copilot is the learning layer. Every agent-edit, accepted draft, and escalated ticket feeds back into the knowledge graph so the AI improves on the categories Jeppesen ForeFlight cares about most.
Agents and customers interact across channels.
Maven Inbox surfaces recurring intents and knowledge gaps.
Suggested KB articles generated from real conversations.
Updated knowledge improves future answers automatically.
A continuous loop — every conversation improves the next.
- The team always stays in control. Suggested content is reviewable, editable, and approved before it goes live.
- No new tools to learn. Knowledge improvements flow into the systems agents already use.
- Improvements compound. Every conversation makes the next one better.
- Copilot accelerates learning. Agent edits and accepted drafts on Copilot-touched tickets become the next training signal, so autoresponse gets smarter faster without manual tuning cycles.
Trust and control
Jeppesen ForeFlight's support requires precision and control — so Maven was built for that.
Questions about account deletion, billing disputes, or any regulatory matter always escalate to Zendesk automatically.
Maven only answers when it's certain. Otherwise, it routes to an agent.
Every AI response shows which knowledge was used and why.
Every interaction is logged, reviewed, and explainable.
Why it works for Jeppesen ForeFlight
Each capability removes a specific way the current support operation makes work harder — and replaces it with something rule-based, not judgment-dependent.
Intent- and complexity-based routing
Segments commit each conversation to a purpose-built path before triage reaches a human. Password reset ≠ weight-and-balance calc — routed differently, deterministically. SLA stops depending on the day's ticket mix.
Tiered-account segments
The $2.7M ARR account gets flagged and routed by rule, not by whether the agent on shift recognizes the name. Revenue-at-risk protected by system logic, not tenured memory.
Segment isolation across Jeppesen ForeFlight
One Graph of Record, one reasoning engine — but segment-specific knowledge and general knowledge never bleed into each other's answers. The technical answer to 'one front door' without cross-segment contamination.
Graph of Record replaces tab-switching
Plan, purchase history, and usage from Center — scoped to the conversation and available in the same interface. This is the same mechanism behind the 25–40% agent productivity proof point.
Unified knowledge, scoped by segment
The 1,200-page pilot's guide, help center, and Jeppesen ForeFlight docs live in one graph. Segments decide what's eligible per conversation — unification without new risk.
Copilot-safe internal vs. customer knowledge
Internal runbooks visible when Maven assists an agent, invisible when it talks directly to a customer. The same investment funds Copilot and autonomous resolution — no separate build.
Product identification across 80+ Jeppesen ForeFlight SKUs
Simplest path: Maven injects a first message asking which product the customer is inquiring about — and if that's already on the customer record in Salesforce, we skip the question and use it. For the long tail of 80 Jeppesen ForeFlight products, Intelligent Fields classify the product at every turn of the conversation and confirm before escalating. One capability handles the whole catalog — no per-product build.
Recommended Footprint
Maven Copilot installs inside the team's ticketing systems. Agent Maven can serve every major customer-facing channel. What's variable is what the team turns on first — not what Maven is capable of.
Real-time reply suggestions, ticket summarization, KB surfacing, and one-click context — natively embedded in the Zendesk agent workspace.
Every reply still reviewed and sent by a human. No customer-facing autonomy in this surface.
Agent Maven handles inbound email tickets autonomously on the intents the team has graduated through Agent Designer. Everything else escalates to a human agent with full context.
Sensitive intents (billing disputes, account deletion, regulatory questions) stay off autonomous handling. Each graduated intent must meet the team's configured accuracy threshold before turning on.
Maven Copilot installs natively in Zendesk, Salesforce, HubSpot, and Freshdesk — same reasoning engine, same guardrails, same Agent Designer. If other parts of the organization eventually want Maven Copilot inside their own ticketing system, the underlying product is the same and the existing knowledge graph carries over.
Implementation
Three phases: CoPilot + email validation first, then Center integration and actions, then expansion scoping once ForeFlight proves value.
Weeks 1–8. Kickoff and success metrics; core knowledge ingestion across Help Center, product guides/PDFs, Confluence, guardrail guides, and curated sample tickets with per-product tagging and validation; CoPilot in Zendesk (Marketplace app) as the validation engine; Maven email auto-response enabled category-by-category as CoPilot validates; Center API discovery; architecture/security review kickoff; product-disambiguation groundwork (glossary + intelligent fields).
Weeks 10–16. Center read connector (ForeFlight data) per Phase 1 discovery; account and entitlement lookups; first write action (nav-data counter reset) behind accuracy gates with confirmation notifications; conversations unified by user identity across surfaces; additional email categories enabled; customer-facing chat validated in parallel behind login; Amplitude usage-data availability explored alongside chat.
Weeks 18+. Scoping memos only — no delivery commitment. After ForeFlight proves value across CoPilot, email, and Center actions, Maven expands into Jeppesen-branded teams — starting with Zendesk-using groups, then Jeppesen's Salesforce environment via native integration, unifying both ticketing ecosystems under one shared source of truth. Candidates also include additional read/write actions, proactive feature-adoption, voice self-service, and in-app ForeFlight chat. Any promotion to build requires a separate SOW amendment.
POC Validation
Proof-of-concept results on real Jeppesen ForeFlight Tier 1 tickets — see the conversation, the demo, and the accuracy we achieved.
Maven, resolving a real Jeppesen ForeFlight ticket
An authenticated subscription-change question — the exact scenario where a day-old Center sync would fail today. Maven reads the fresh entitlement state, resolves it, and surfaces the next relevant capability without a sales push.
See it running against Jeppesen ForeFlight's exact scenarios
A 45-minute walkthrough covering the current-state gaps we heard — triage, VIP segmentation, 80-product classification, entitlement fixes, and low-engagement nudges — end to end in one interface.
Maven pulls product and account context from Salesforce before the user types a second message — no product picker, no clarifying round-trip.
Watch classification update turn-by-turn as the conversation evolves, with a confirmation gate before any escalation or entitlement action.
Maven resolves a subscription mismatch against Center in-flow — no handoff, no ticket, agent stays on the high-value queue.
Amplitude signal ties into the workflow: Maven surfaces a proactive nudge for a user who's never touched daily weather or logbook.
POC results on real Jeppesen ForeFlight Tier 1 tickets
Maven was tested against the top 100 Tier 1 questions sourced from actual Jeppesen ForeFlight ticket volume — tuned iteratively until accuracy reached 85%.
on the top 100 Tier 1 questions from live Jeppesen ForeFlight volume.
- •Test set: top 100 Tier 1 questions from real ticket volume
- •Approach: iterative tuning rounds — test, identify misses, tune knowledge and Charters, retest
- •Validation: regression run after each pass to protect previously passing answers
The live test set used for each tuning pass: 100 real Tier 1 questions from Jeppesen ForeFlight ticket volume, re-run after every knowledge or Charter change to catch regressions on previously passing answers.
Teams already doing more with Maven
"The question wasn't 'can it deflect tickets,' it was 'can it deliver an interaction our customers will be genuinely glad they had.' AskQ cleared that bar and kept raising it."
"Maven runs agents across Support, Cross-sell, Upsell, and Onboarding for Clio — same reasoning engine, same interface, no separate stack per use case. 80% of inquiries answered autonomously."
"The Copilot pattern that reduced onboarding time by nearly 90% at ClickUp — the same shape we're proposing for Jeppesen ForeFlight."
"In a world oversaturated with AI support solutions, Maven is second to none."
Two lines. Email & Copilot.
A flat annual platform fee plus a per-ticket-touched usage rate on email. Voice and chat are out of scope for this proposal — the ROI model, the order form, and this pricing all use the same email pool and the same unit economics.
Fixed annual fee, independent of volume.
- ✓Unlimited agents & channels within email scope
- ✓100+ integrations, no per-connector fee
- ✓Agent Designer + Analytics
- ✓SSO, RBAC, SOC 2 / HIPAA controls
- ✓Named CSM + implementation team
Billed on every email ticket Maven touches — whether Maven auto-resolves it, drafts a Copilot reply, or classifies and routes it. No separate per-resolution rate.
Volume-touched % is controlled by the ROI lever — this line scales linearly with it.
Resolution = the AI-sent reply fully addresses the inquiry with no further customer follow-up and no agent handoff. Because email is asynchronous, resolution leans on non-reopen behavior rather than real-time confirmation.
- •Auto-resolved: AI sends the reply directly (no agent touch) and the customer doesn't respond again on that thread within the window.
Current model: 1 ticket = 1 billable resolution. Every ticket where Copilot generates a response counts as one billable resolution.
- •Research tab: counts as its own, independent resolution.
- •Thumbs down: if the agent gives a thumbs down on the generated response anywhere in the ticket, it is not billed.
Voice and chat are explicitly out of scope for this proposal and are not billed under this order form. Final figures confirmed in the signed order form.
Gross savings scenarios — Email & Copilot
All figures annualized. Modelled on 150,000 annual EMAIL tickets at $33/ticket fully loaded. Automation targets the ~40% Tier 1 commerce bucket; whatever Tier 1 tickets automation doesn't resolve funnel into Copilot's eligible pool alongside the ~60% Tier 2 aviation-complexity bucket. Every ticket lands in exactly one pool — no double counting.
| Line item | Conservative | Target | Growth | Full integration |
|---|---|---|---|---|
| 1. Tier 1 commerce support — autonomous resolution | ||||
Tier 1 resolution rate Share of the ~40% Tier 1 commerce bucket Maven autonomously resolves | 50.0% | 65.0% | 80.0% | 90.0% |
Tickets auto-resolved (annual) Tier 1 commerce bucket (60,000) × resolution rate | 30,000 | 39,000 | 48,000 | 54,000 |
Gross automation savings Resolved tickets × $33 fully loaded | $990,000 | $1,287,000 | $1,584,000 | $1,782,000 |
| 2. Copilot value (Tier 2 + leftover Tier 1 — non-overlapping with auto-resolved) | ||||
Copilot resolution rate Drag the lever to set the share of Copilot's eligible pool it fully resolves (accepted draft = billable resolution at $33). The remaining eligible tickets are Copilot-assisted — agent still handles, but faster (scenario-specific productivity gain). 40.0% of eligible pool resolved 20%30%40%50%60%70%80%90% | ||||
Copilot eligible pool Tier 2 (90,000) + leftover Tier 1 (bucket − auto-resolved). Shrinks as Tier 1 automation improves. | 120,000 | 111,000 | 102,000 | 96,000 |
— fully resolved (draft accepted) Eligible pool × 40.0% Copilot resolution rate. Billed at $1 per resolution; credited full $33 deflection value. | 48,000 | 44,400 | 40,800 | 38,400 |
Copilot productivity gain (scenario-specific) Scenario-specific productivity gain applied to Copilot-assisted tickets (agent still handles, but faster). | 15.0% | 20.0% | 25.0% | 30.0% |
— assisted only (agent still handles) Remaining eligible pool — agent still handles, with Copilot assist. Credited only the scenario-specific productivity gain [gain/(1+gain) × $33]. | 72,000 | 66,600 | 61,200 | 57,600 |
Total Copilot value (resolutions + assist) Resolutions × $33 full deflection + assisted × $33 × gain/(1+gain). Productivity gain by scenario: 15% / 20% / 25% / 30%. No double-counting: each ticket sits in exactly one bucket. | $1,893,913 | $1,831,500 | $1,750,320 | $1,705,846 |
FTE-equivalent capacity freed (Copilot resolution $ value) ÷ $70,000 fully loaded rep cost — headcount to be confirmed | 22.6 FTE | 20.9 FTE | 19.2 FTE | 18.1 FTE |
| Note on the trend: Total Copilot value declines slightly as Tier 1 automation climbs (Conservative → Full) because the eligible pool shrinks faster than the scenario's productivity gain compensates. Expected dynamic, not a modeling gap. | ||||
| 3. Maven cost (variable — $1 per resolution + platform fee) | ||||
Resolution usage cost (Auto-resolved Tier 1 + Copilot resolutions) × $1 per resolution. Scales with the Copilot lever and Tier 1 scenario. | $78,000 | $83,400 | $88,800 | $92,400 |
Platform fee Fixed annual platform fee. | $50,000 | $50,000 | $50,000 | $50,000 |
Total Maven investment (variable) Resolution usage ($1 × total resolutions) + platform fee. Fully variable — moves with the Copilot slider and scenario. | $128,000 | $133,400 | $138,800 | $142,400 |
| 4. Total business case | ||||
Total gross savings (automation + Copilot + productivity) Automation savings + Copilot resolutions + productivity value on assisted tickets. | $2,883,913 | $3,118,500 | $3,334,320 | $3,487,846 |
Less: Total Maven investment | ($128,000) | ($133,400) | ($138,800) | ($142,400) |
Net annual benefit | $2,755,913 | $2,985,100 | $3,195,520 | $3,345,446 |
| 5. Return metrics | ||||
ROI multiple Net benefit ÷ Maven investment | 21.5x | 22.4x | 23.0x | 23.5x |
Payback period | 0.6 mo | 0.5 mo | 0.5 mo | 0.5 mo |
Range across scenarios: $128,000 (Conservative) → $142,400 (Full).
Beyond deflection dollars, three benefits Steven surfaced on the call that make the price defensible on their own.
Steven referenced ClickUp's Copilot rollout as the proof point — agent onboarding dropped from 9 months to 1. The same shape is on the table for Jeppesen ForeFlight's agent ramp, directly compressing the largest fixed cost in support.
Called out as a benefit in its own right. Agents pick it up without a training curve, and the same interface serves Copilot today and autonomous resolution tomorrow — no re-platforming between phases.
The Jeppesen ForeFlight team is already using Maven AGI's Agent Designer internally — traction and buy-in that pre-dates the customer-facing rollout. The tooling has already cleared the "will our team actually use this" bar.
Total email volume (150,000) and $33 fully loaded cost/ticket are the two fixed inputs driving every scenario; the lever controls the share Maven touches. Voice tickets are explicitly excluded from this model.