Comparison guide
Choose the context layer by the decision it must improve.
Company brains, customer memory graphs, enterprise search, product intelligence, and direct AI connectors overlap in language. They are not interchangeable. This guide compares their public emphasis and the first job each should be evaluated on.
Last reviewed
The short version
This is Coby's selection guide, not an independent ranking or benchmark. Coby at joincoby.com is an AI product intelligence layer. The domains below identify the specific products being compared. Descriptions reflect their public positioning, not verified head-to-head performance.
| Approach | Public emphasis | Evaluate first when |
|---|---|---|
| Coby (joincoby.com) | Product and customer context for product teams: behavior, feedback, account value, product structure, ownership, decisions, and outcomes. | The recurring job is a cross-source product investigation and the answer must show identity, coverage, provenance, uncertainty, and an actionable owner. |
| Sentra (sentra.app) ↗ | A company-wide memory layer for teams and agents, centered on interactions, decisions, commitments, drift, identity, and bi-temporal context. | The priority is shared organizational memory across functions, agents, and a broad set of company interactions. |
| Sento (sentohq.com) ↗ | Governed customer data for AI: approved fields, definitions, thresholds, deltas, and access rules shared across agents. | The priority is making customer-facing AI consistently use the same approved business definitions and data. |
| Hyperengage (hyperengage.io) ↗ | A customer memory graph for post-sales agents, fusing CRM, usage, support, communication, billing, and customer events. | The first workflows belong to customer success, renewals, expansion, account briefing, and post-sales automation. |
| Glean (glean.com) ↗ | An enterprise AI platform spanning search, workplace context, assistants, agents, actions, and governance. | The priority is company-wide AI adoption and workflows across functions, with broad application access and enterprise governance. |
| Shiplog (useshiplog.com) ↗ | Agentic customer intelligence oriented toward net revenue retention and commercial customer workflows. | The desired outcome is owned primarily by GTM, customer success, or revenue operations. |
| Dust (dust.tt) ↗ | A multi-model platform for people and agents to work together, with shared company knowledge, connected tools, reusable skills, and governance. | The team wants to build and operate agent workflows across functions. Test whether that setup already supplies enough product context before adding another layer. |
| Enterpret (enterpret.com) ↗ | Customer intelligence built around customer signals, adaptive taxonomy, a context graph, and feedback-to-action workflows. | The starting point is customer feedback and its relationship to product, usage, and commercial outcomes. Verify the needed source coverage and joins on your cases. |
| DIY: LLM + connectors or MCP | Direct access to source tools from Claude, ChatGPT, or another agent, assembled by the team. | Questions are occasional, the team is small, identity joins are simple, and rebuilding context or operating the connectors costs less than a dedicated layer. |
How to choose for your workflow
Coby vs Sentra
Both care about persistent, identity-resolved context. Sentra's public scope is company-wide memory. Coby's intended depth is narrower: the relationship between product behavior, customers, product structure, and product decisions.
Coby vs Sento
Sento's public strength is governed customer meaning for agents. Coby should be tested where the question also needs product taxonomy, behavior, delivery, ownership, and decision history—not only a consistent customer snapshot.
Coby vs Hyperengage
Hyperengage is explicitly post-sales. Coby should earn the product-team use case: why the product issue happened, what evidence supports it, which product area owns it, and what prior decisions matter.
Coby vs Glean
Glean's scope includes agents and enterprise context, not only retrieval. Evaluate Coby when the buyer is a product team investigating customer problems. Ask both systems to show the resolved accounts, supporting evidence, gaps, and product owner on the same investigation.
Coby vs Shiplog
This comparison is with the customer intelligence product at useshiplog.com. Its public framing is revenue and NRR. Evaluate Coby when commercial context needs to inform a product investigation, prioritization, or shipped change.
Coby vs Dust
Dust already provides shared context and agent workflows; it is not a stateless chat wrapper. A separate product brain is justified only if maintaining cross-source identities, product definitions, and investigation history adds measurable value to the setup you already operate.
Coby vs Enterpret
Enterpret explicitly offers a customer context graph, including usage and business context. The useful test is not “feedback versus context.” It is how each handles your source joins, contradictory evidence, product ownership, and the decision that follows.
Coby vs DIY agents
DIY is the default and often the best baseline. Coby must prove incremental value from maintained identity, definitions, provenance, temporal history, evidence coverage, and memory of what happened after a decision.
Run the same evaluation on every option
Do not compare demos. Compare the quality distribution on the same difficult cases.
| Test | Question to ask |
|---|---|
| Representative cases | Can it handle at least 30 real historical cases, including edge cases, incomplete sources, and ambiguous identities? |
| Completeness | Does it report the evidence examined, the total available, source failures, and exclusions—or only a plausible answer? |
| Identity | Can a reviewer inspect and correct how users and accounts were matched across systems? |
| Provenance and time | Can every important claim be opened at its source, and do superseded facts stop appearing as current? |
| Decision usefulness | Did the result change or accelerate a real decision for the intended owner? |
| Human boundary | Is it explicit where AI retrieves, infers, recommends, acts, and requires approval? |
| Operating burden | Who maintains connectors, definitions, permissions, mappings, corrections, and evaluation as the stack changes? |
What Coby must prove
Coby should not be selected because “a brain sounds strategic.” It should be selected only when the connected context materially improves a product decision over the best practical baseline.
Start with the customer escalation diagnostic: same inputs, same cases, blind review, explicit coverage, and human verification. If Coby cannot produce a meaningful lift over your current operators and DIY agents, the dedicated context layer has not earned its place.
Frequently asked questions
Is Coby an alternative to Glean?
Sometimes, but the starting jobs differ. Glean is an enterprise AI platform with search, enterprise context, assistants, agents, and actions across functions. Coby focuses on product and customer investigations that connect behavior, feedback, account context, ownership, and decision history. Evaluate both on the same cases; Glean is not just document search.
Do we need Coby if we already use Dust?
Not necessarily. Dust supports shared company context, people working with agents, connected tools, and reusable workflows. Consider a separate product context layer only when repeated product investigations need maintained customer joins and evidence that your existing setup does not supply well enough. Test the incremental value; another layer is not automatically better.
How is Coby different from Enterpret?
Enterpret connects customer signals through an adaptive taxonomy and customer context graph, with product, usage, and business context. It is not merely feedback tagging. Coby focuses on a private product brain for cross-source investigations. Compare the actual identity joins, evidence coverage, product ownership, and decision history on your own cases rather than assuming either vendor lacks context.
How is Coby different from Sentra?
Sentra publicly positions as a company-wide memory layer for teams and agents, capturing interactions, decisions, commitments, and drift. Coby is narrower by design: product teams, product and customer semantics, cross-tool identity resolution, and evidence for product decisions.
How is Coby different from Sento or Hyperengage?
Sento emphasizes governed customer data and approved definitions for AI. Hyperengage emphasizes a customer memory graph for post-sales agents. Coby emphasizes product investigations and decisions that connect customer evidence to behavior, product structure, ownership, and prior outcomes.
Why not connect Claude or ChatGPT directly to our tools?
That is often the right starting point for occasional questions. A persistent context layer earns its cost when cross-source work repeats, identities need maintained resolution, answers require provenance and coverage, definitions are shared across people and agents, or corrections and outcomes must compound over time.
Competitor descriptions are based on the official product pages linked above, reviewed on September 7, 2026. Capabilities and deployment terms can change; confirm them with each vendor. Coby has no affiliation with the companies listed. Names and trademarks belong to their respective owners.
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