Bagel AI Enterprise
The Product Brain Built for Enterprise Scale
One product brain for every business unit, region and coding agent. Bagel AI turns customer signal into scoped, revenue-weighted product decisions, with SOC 2 Type II, SSO and a dedicated model per customer from day one.
What is Bagel AI for Enterprise?
Bagel AI is a product brain for enterprise companies. It connects to 100+ sources, including sales calls, support tickets, CRM notes and product analytics, resolves every signal to one customer record, and ranks product decisions by ARR or any KPI you choose. It serves those decisions to people and AI agents through MCP, measures the outcome after launch, and runs on SOC 2 Type II controls with SSO and role-based access.
Enterprise-grade results. From day one.
Your org runs on dozens of tools.
Your product decisions should run on one brain.
Every region and business unit has its own signal, and none of it reaches the roadmap as one picture. Bagel AI is the layer that fixes that.
Inside the Enterprise Decision Loop
Synthesize every signal
Bagel AI reads every sales call, support ticket, CRM note and Slack thread across every region and business unit. A dedicated model learns your product vocabulary and resolves each signal to one canonical customer, so duplicates collapse before anyone reads them.
Quantify the impact
Every request is weighted by the number your board tracks: ARR, NPS, north star, active users or a formula your team defines. One large account stops looking like five small ones.
Make the decision
Bagel AI proposes the next product move for each business unit, ranked against the roadmap you already have, with the owner, the evidence and the ARR at stake attached.
Measure the impact
Every shipped decision is tracked against the bet it was built on, from adoption to retention and revenue. Leadership gets a record of what worked, and the result feeds the next decision.
Hand it to your AI stack
Claude Code, Cursor, Codex, Glean and any MCP client get the validated decision the moment the build starts. Your engineers and your agents pull the same evidence, scoped the same way, across every team.
Enterprise-grade Product Brain
Built to the data standards your security team expects, so every team works from the same evidence while access stays scoped and internal.
SOC 2 Type II
Independently audited controls, with the report available through the Trust Center.
A Dedicated Model per Customer
Your data trains your model and nothing else. It never trains another customer’s model.
Minimal PII by Design
PII reduction happens in the architecture, before customer data reaches a model, with the context kept intact.
The controls a CISO checks first
Everything your security team asks about before a product brain touches customer conversations.
SSO and role-based access
Single sign-on through your identity provider, with advanced permissioning by team and product line.
Full audit trail
Access and decision changes are logged, so you can answer who saw what, and when.
Decision lineage
Every decision traces back to the calls and tickets behind it and forward to the outcome it produced.
AI transparency
Complete visibility into how your data is handled and applied across Bagel AI.
One product brain. Every control documented in the Trust Center before your review starts.
Roll out across the org without a services project
Unlimited seats, no per-seat fees
Pricing follows evidence volume, so every PM, engineer, CSM and agent can use it.
Custom integrations for your stack
100+ integrations out of the box, plus custom-built connectors, a REST API and webhooks.
Dedicated CSM and onboarding team
Live in under 14 days, with quarterly roadmap ROI reviews after that.
Why enterprise teams choose Bagel AI over Productboard and Enterpret
Every Seat Included
Productboard prices per maker and adds monthly AI credits. Bagel AI has no per-seat fees, so engineering, CS and sales work from the same evidence.
Decisions With the Revenue Attached
Voice-of-customer analytics end at the insight. Bagel AI scopes the decision, ties it to revenue and hands it to the team that builds it.
Built for Coding Agents
The Bagel AI MCP serves validated decisions to Claude Code, Cursor and Codex, with the customer evidence attached.
Proof After Launch
Every shipped decision is measured against its predicted outcome, long after most tools have stopped paying attention.
Why not build it internally?
Where internal builds break
Internal builds take 6 to 12+ months to reach value, start from a $180K FTE floor, and break in the four places an enterprise rollout depends on most.
Some good words from our customers
“Bagel AI helps us uncover blind spots bringing evidence from different channels (sales calls, support teams etc.) making our product decisions sharper and more aligned with growthaligned with growth.”
“Bagel AI has been a crucial tool for us in turning GTM team feedbackturning GTM team feedback into quantifiable insights.} It provides clear evidence to justify investment in new features, ensuring we make informed, high-impact product decisions.”
“Bagel AI is unmatched in the industry! The bespoke model built for HoneyBook helped us prioritize the most impactful featuresprioritize the most impactful features cutting through the noise of our large user base. Highly recommended!”
“Completes the long-time missing part between revenue, product and the customer. It is a product market fit pulseproduct market fit pulse on autopilot”
“Every SaaS company hits a point where feedback piles up but decisions stall. Bagel AI turns that mess into clear, revenue-focusedclear, revenue-focused priorities.”
One product brain for every seat in the enterprise
CISO and IT
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SOC 2 Type II report and security docs in the Trust Center.
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SSO, role-based access and a full audit trail.
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A dedicated model per customer, isolated from every other customer.
CPO / CPTO
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Every roadmap item tied to revenue exposure and strategic fit.
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One evidence pack across product, engineering, and GTM.
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Board-ready outcomes on the work your team already shipped.
Heads of Engineering
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Dev-ready artifacts pulled into Linear, Jira, and Cursor
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Your coding agents query the same customer context your humans do.
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First-pass quality up. Rework and reopened tickets down.
CRO and Sales
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Real-time alerts on deal blockers and churn signals.
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Every request tied to the accounts and ARR behind it.
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Sales hears the moment the feature they asked for ships.
Product operations
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Auto-triage replaces the weekly feedback consolidation ritual.
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Product, engineering, and GTM stay aligned on the same evidence pack.
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One canonical source of customer truth across the org.
FAQ – You ask, we answer
Bagel AI for Enterprise is the Enterprise plan of Bagel AI, a product brain that turns customer signal into scoped, revenue-weighted product decisions for people and AI agents. On top of everything in Pro, it adds custom integrations, flexible evidence volume, advanced permissioning with a full audit trail, custom workflows and a dedicated CSM and onboarding team.
Yes. Bagel AI is SOC 2 Type II certified and GDPR ready. The SOC 2 report and the rest of the security documentation are available through the Bagel AI Trust Center at trust.bagel.ai.
Each customer gets a dedicated model trained on their own data, product taxonomy and customer language. Your data never trains another customer’s model, and PII is reduced by architecture before it reaches a model.
Yes. SSO is included from the Pro plan up. The Enterprise plan adds advanced permissioning and a full audit trail, so access can follow your teams and product lines.
Productboard organizes roadmaps and feature requests, and prices per maker with monthly AI credits on top. Bagel AI reads signal from the tools you already run, ranks decisions by revenue or any KPI, serves them to AI coding agents through MCP and measures outcomes after launch. Pricing follows evidence volume with unlimited seats, so engineers, CSMs and agents can use it too.
Enterpret focuses on voice-of-customer analytics. Bagel AI carries that signal all the way through to a scoped decision tied to revenue, a dev-ready artifact your coding agents read through MCP, and a measured outcome once the feature ships.
Most teams are live in under 14 days. Historical data is processed on day one, so actionable signal usually shows up in the first week. Custom-built integrations can add time depending on your stack.
Plans start at $24K a year on Pro, and Enterprise pricing depends on evidence volume and integration needs. There are no per-seat fees and no tool-based add-ons, and connecting one tool costs the same as connecting ten.
Yes. The Bagel AI MCP server serves validated product decisions, with customer evidence and revenue context attached, to Claude Code, Cursor, Codex, Glean, Windsurf and any MCP-compatible client.
Yes. Enterprise includes custom workflows and advanced permissioning, so each business unit works from its own view while leadership sees the whole portfolio from one source.
You can get surprisingly far with a prototype. Internal builds tend to break on model drift, entity resolution, compliance and PII, and cross-team trust, and they typically take 6 to 12+ months to reach value, starting from a $180K FTE floor. The full cost model is in Build vs buy: what AI product feedback analysis actually costs, and Document Crunch weighed an in-house build before choosing Bagel AI.
No. Bagel AI is an independent company and product brain for product and engineering teams, with no affiliation to ByteDance’s Bagel research model.
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