Category guide
AI product intelligence for better decisions.
Coby connects product behavior, customer feedback, account value, and product knowledge in a private product brain. Product teams and their AI agents use that context to investigate customer problems and decide what to fix or build next.
Last reviewed
A useful definition
Product intelligence is the connected evidence a team uses to understand a product problem and make a better decision. The output is not “an insight.” It is a defensible next step.
Behavior
Events, sessions, funnels, feature adoption, errors, and other evidence of what users actually did.
Voice
Support tickets, calls, messages, surveys, and feedback that explain friction in the customer's own words.
Business context
Account, plan, lifecycle, renewal, and value data that change the priority of otherwise similar product problems.
Product context
Product areas, owners, roadmap work, code, incidents, and prior decisions that explain what the team can do about it.
Three questions connected product evidence can answer
Start with a decision your team already struggles to make. These are evaluation workflows, not claims of automatic diagnosis or guaranteed revenue uplift.
| Product question | Evidence to connect | Decision to support |
|---|---|---|
| Why are accounts failing to adopt a feature? | Define the eligible cohort and activation event in analytics. Join the affected accounts to support reports and interviews from the same period. Check release and configuration changes. | Distinguish a usability issue, missing capability, configuration problem, or instrumentation gap before choosing an intervention. |
| Which customers are affected by this bug? | Join failure events and support reports to resolved customer accounts. Add current recurring revenue and renewal context only for accounts with a verified match. | Prioritize the response using severity, unique affected accounts, recurrence, and revenue exposure—not ticket count alone. |
| Which feature gap is blocking expansion? | Link recorded deal objections and customer requests to the account, current usage, related roadmap work, and the owner who can validate the commercial evidence. | Separate a documented product blocker from an inferred one. Keep booked revenue, open pipeline, and speculative opportunity in different totals. |
Follow the worked example of customer impact and ARR exposure to see how to avoid double-counting accounts or presenting exposed revenue as predicted churn.
Why another dashboard does not solve it
Each source is useful on its own. The difficult questions live between them.
| Question | One source can answer | Connected product intelligence adds |
|---|---|---|
| What happened? | A product analytics event or support ticket. | Whether both signals describe the same user, account, product area, and moment. |
| How widespread is it? | The records returned by one query. | A denominator: how much evidence was examined, what was excluded, and which affected accounts were resolved. |
| Why did it happen? | A symptom inside one tool. | Behavior, friction, product history, incidents, and prior decisions with provenance. |
| What should we do? | A chart, cluster, or generated summary. | Severity, reach, account context, ownership, constraints, and an auditable recommendation for a human to decide. |
How Coby builds usable context
Connect only the agreed sources
Coby uses read-only or scoped access defined with the client. The original systems remain the sources of record.Resolve shared entities
Users, accounts, product areas, owners, issues, and decisions are matched across tools instead of being treated as unrelated records.Preserve evidence, provenance, and time
The product brain records where a claim came from, when it was true, and the evidence available to support it.Serve context at decision time
Product teams and their AI agents can investigate a question without rebuilding the same joins and definitions in every session.Carry the outcome forward
The investigation can remain linked to the owner, product decision, shipped change, and later outcome instead of ending as an isolated summary.
What Coby is not
Not a replacement for analytics
Use PostHog, Amplitude, or your warehouse for canonical product metrics. Coby uses those facts in a wider investigation.
Not another feedback inbox
Feedback collection and tagging are inputs. Coby is useful when the team must connect feedback to behavior, accounts, ownership, and decisions.
Not a general-purpose AI platform
Broader enterprise AI platforms also offer context, agents, and actions. Coby focuses on the repeated product investigation: resolve customer identities, scope the evidence, and connect it to a product decision.
Not an autonomous product manager
Coby assembles evidence and proposes a path. A human remains accountable for product judgment and action.
A buying checklist
Whether you evaluate Coby or another approach, ask vendors to demonstrate these properties on your own difficult cases.
| Property | Evidence to request |
|---|---|
| Identity resolution | Show how one person and account are matched across product, support, CRM, and billing identifiers. |
| Coverage | Show the number of records examined, the total available, exclusions, and failed source reads. |
| Provenance | Open every important claim back to its source and timestamp. |
| Temporal accuracy | Demonstrate how changed or superseded facts stop being presented as current. |
| Human control | Show exactly where AI suggests, where a person decides, and how corrections persist. |
| Outcome memory | Show whether an investigation remains connected to the later decision and result. |
For the underlying design, read how Coby's customer context graph works.
Frequently asked questions
Which product intelligence workflows is Coby designed for?
Coby is designed for recurring investigations that span customer and product systems: diagnosing an escalation, explaining adoption friction, finding accounts affected by a bug, and connecting feature gaps to commercial context. The exact source coverage and workflows are agreed for each engagement.
What is product intelligence?
Product intelligence is the evidence layer used to understand product behavior, customer friction, commercial context, and prior decisions together. Its purpose is to improve a product decision, not merely produce another dashboard.
How is product intelligence different from product analytics?
Product analytics explains behavior inside an instrumented product. Product intelligence adds feedback, support, account value, product ownership, delivery history, and decision context so a team can investigate why something is happening and what to do next.
Does Coby replace PostHog, Amplitude, Intercom, Linear, or a CRM?
No. Those systems remain the sources of record. Coby connects their evidence, resolves shared entities, and preserves the context needed by product teams and their AI agents.
When should a team build this itself?
DIY connectors are often enough for occasional lookups. A dedicated context layer becomes useful when the same cross-source investigations recur, identity differs between tools, answers must be traceable, or the team needs shared context that persists across agents and decisions.
Bring one difficult product question.
We will show which sources Coby used, how much evidence it covered, what it could not establish, and where a human still needs to decide.
Test a question with Coby