Plain verdict
Choose Clavinci when you need session and cache economics, actual and API-equivalent cost, durable survived-code ROI, replayable and disputable attribution, explicit content exclusions, engineer-visible access audit, cross-pillar AI quality, and collection-health proof. Choose LinearB when the platform must also review code, route pull requests, enforce merge policies, automate approvals and tests, collect sentiment, forecast delivery, expose data through MCP and APIs, or support cloud, hybrid, and on-premise environments.
Choose Clavinci when
- You need direct session, token, model, cache, actual-cost, and API-equivalent-cost evidence from multiple local assistants.
- ROI must reach cost per delivered issue, story point, CI run, and merged code that remains after 30 days.
- Attribution must be explainable, replayable after rule changes, and contestable through an engineer dispute workflow.
- DORA, sprint spillover, PR review, incidents, re-rolls, acceptance, prompt complexity, tool-task fit, churn, and compaction must share one graph.
- The measurement platform must not read prompts, responses, source, reviews, issue descriptions, logs, environment variables, or secrets.
- Individual access must be visible to the engineer and bounded by reporting lines, privacy floors, and prohibited anti-metrics.
Choose LinearB when
- AI code review, security and performance findings, stateful review comments, and recommended fixes are product requirements.
- You need policy-as-code for PR routing, expert assignment, approvals, tests, labels, estimated review time, and merge governance.
- Developer surveys, DSAT, benchmarks, customizable dashboards, MCP analysis, and natural-language reporting belong in one platform.
- Delivery forecasting, resource allocation, capitalization, project cost, and executive ROI reporting are required.
- You need a mature product with broad AI detection and cloud, hybrid, or on-premise deployment support.
01 / evidence
Decision ledger
01
Evidence graph and decision scope
ClavinciClavinci joins AI sessions, git, Jira, pull requests, CI, deployments, and incidents in a versioned evidence graph for cost, delivery, quality, and governed access decisions.
LinearBLinearB combines AI and developer productivity insights, APEX metrics, developer experience, executive reporting, MCP analysis, forecasting, and workflow automation.
Decision implicationClavinci is a deep evidence and governance system. LinearB is both an intelligence platform and an active delivery control plane.
02
AI tool and session telemetry
ClavinciLocal collectors cover Claude Code, Codex, Cursor, Gemini, Kiro CLI and IDE, OpenCode, OpenClaw, and Every Code with sessions, models, token classes, caches, tool events, and compaction.
LinearBLinearB publishes detection across more than 50 AI tools through pattern matching and direct integrations, plus native Copilot and Cursor usage, suggestions, acceptance, and AI-assisted PR analysis.
Decision implicationLinearB has broader published tool detection. Clavinci provides normalized local session and cache detail across its supported collector set.
03
Cost and ROI
ClavinciClavinci calculates actual and API-equivalent cost, dark sessions and tokens, tokens per outcome, cost per issue or story point, AI-attributed CI cost, and cost per merged line that survives 30 days.
LinearBLinearB connects AI adoption and assisted PRs to cycle time, throughput, refactor, change failure, productivity, project cost, resource allocation, and executive ROI reporting.
Decision implicationLinearB frames ROI across engineering and business reporting. Clavinci makes session economics and durable attributed units explicit.
04
Attribution and disputes
ClavinciClavinci uses time, file overlap, explicit session trailers, and Jira keys, supports proportional multi-session links, replays versioned attribution, and lets engineers dispute incorrect links.
LinearBLinearB identifies AI-assisted work through direct tool data and pattern detection and correlates it at pull request level with delivery and quality outcomes.
Decision implicationLinearB has broad automated detection. Clavinci goes further on replayable session-to-work provenance and formal attribution correction.
05
DORA and code survival
ClavinciClavinci combines DORA with AI lead time, AI change failure, AI rework, hotfix and rollback evidence, and 30-day survival of attributed code.
LinearBLinearB provides DORA, cycle time, throughput, planning accuracy, rework, refactor, change failure, code retention, and AI versus non-AI delivery analysis with benchmarks.
Decision implicationBoth cover delivery and quality deeply. Clavinci ties a fixed 30-day survival outcome directly to originating sessions and cost.
06
Jira and sprint flow
ClavinciClavinci compares AI and manual velocity, spillover, cycle count and time, time in status, aging WIP, stale issues, story-point cost, epics, and issue attribution.
LinearBLinearB connects project systems to planning accuracy, predictability, delivery risks, forecasting, resource allocation, capitalization, and project costs.
Decision implicationLinearB is stronger for forecasting and business planning. Clavinci is more specific about AI-attributed sprint mechanics and cost per issue outcome.
07
PR and review system
ClavinciClavinci measures PR size, first-review time, merge after approval, iterations, self-merge, reviewer turnaround, review load, cross-team review, and stale PRs by AI cohort.
LinearBLinearB measures PR flow and also acts on it through AI code review, expert assignment, routing, generated descriptions, review-time labels, approvals, and merge policies.
Decision implicationClavinci observes review mechanics in the evidence graph. LinearB is the stronger product when review intervention and enforcement are required.
08
Stability and incidents
ClavinciClavinci joins hotfix rate, incidents, MTBI, on-call page rate, reopen rate, MTTR, and change failure using PagerDuty, Opsgenie, Incident.io, or file adapters.
LinearBLinearB connects AI and delivery to change failure, rework, refactor, production bugs, deployment metrics, incidents, and quality risks.
Decision implicationBoth address downstream quality. Clavinci publishes a specific incident and on-call metric set tied to session attribution.
09
AI quality signals
ClavinciClavinci combines re-roll rate, tool-task fit, native and inferred acceptance, prompt complexity, churn, compaction, rework, incidents, and 30-day survival.
LinearBLinearB combines suggestions and acceptance with AI-assisted PR cycle time, review depth, refactor, rework, change failure, code review findings, and team health.
Decision implicationLinearB adds active code review and broad PR quality. Clavinci adds cross-assistant session behavior and durable provenance without reading source.
10
Adoption and tool choice
ClavinciClavinci compares tool and model mix, mix shift, concurrent use, adoption curves, session duration, cost, quality, and attributed outcomes across supported assistants.
LinearBLinearB tracks broad tool usage, active and engaged users, suggestions, acceptance, AI-assisted PRs, delivery correlations, and benchmark trends across its AI integration estate.
Decision implicationLinearB has broader detection and benchmarks. Clavinci offers deeper cost, session, dispute, and survival analysis for supported tools.
11
Engineer experience
ClavinciEngineers see personal sessions, spend, outcomes, sprint and DORA context, quality, dark work, export, deletion, disputes, and the audit trail of who accessed their data.
LinearBLinearB combines developer surveys and DSAT with workflow automation, AI review feedback, developer coaching, delivery insights, and friction reduction.
Decision implicationLinearB is stronger for active workflow improvement and sentiment. Clavinci is stronger for personal AI evidence, correction rights, and access transparency.
12
Manager and executive views
ClavinciDirect managers see ROI, delivery, review, stability, quality, and adoption for their teams and reports. Skip-level leaders begin with privacy-floored team aggregates.
LinearBLinearB provides organization, team, project, repository, user, tool, and workflow views plus executive ROI, forecasting, coaching, and customizable MCP-generated reports.
Decision implicationLinearB provides a broader management control plane. Clavinci binds leader visibility to a specific reporting-line and audit contract.
13
Governance and access
ClavinciDirect-report reads create engineer-visible audit rows. Skip-level drill-down needs a reason, peers cannot inspect individuals, privacy floors protect aggregates, and anti-metrics refuse rankings and automated people decisions.
LinearBLinearB provides enterprise access controls and individual segmentation. Its MCP documentation states that permission-scoped MCP access is not yet available and recommends validating generated numbers in the UI.
Decision implicationLinearB offers broad access and query surfaces. Clavinci goes further on sensitive-read accountability and consistent query-layer refusals.
14
Content boundary
ClavinciClavinci excludes prompts, responses, source, review comments, issue descriptions and comments, CI logs, environment variables, and secrets by schema and test.
LinearBLinearB's AI code review and PR-description features inspect code or pull request context because that content is necessary to produce their value.
Decision implicationChoose LinearB when content-aware code intervention is desired. Choose Clavinci when the measurement system must be structurally unable to perform that inspection.
15
Service operations
ClavinciClavinci exposes uptime, collector and sync coverage, freshness, silent engineers, parser errors, rate limits, crashes, latency, Jira health, and notification delivery.
LinearBLinearB publishes 24/7 monitoring, enterprise support, configurable retention, broad integrations, and mature cloud, hybrid, and on-premise operations.
Decision implicationClavinci exposes evidence completeness in-product. LinearB brings wider established service, deployment, and support capabilities.
16
Planning and automation
ClavinciClavinci sends privacy-aware stale-PR, aging-WIP, dark-session, and hotfix triggers through in-app, Slack, email, or webhooks. It does not route, approve, label, or review code.
LinearBLinearB provides programmable YAML workflows, AI code review, expert routing, approval and test policies, reminders, MCP reports, forecasting, capitalization, and resource allocation.
Decision implicationLinearB is decisively stronger for intervention and planning. Clavinci keeps action focused on operational evidence and preserves a passive measurement boundary.
17
Deployment model
ClavinciClavinci is a commercial company-operated hosted service with local collectors. Customers control integrations and governance but do not deploy or maintain the product stack.
LinearBLinearB publishes cloud, hybrid, and on-premise support plus enterprise SSO, custom APIs, provisioning, retention, and monitoring.
Decision implicationLinearB serves more infrastructure and integration models. Clavinci offers one vendor-operated responsibility boundary.
02 / custody
Deployment and data custody
Clavinci operates the hosted application, API, database, upgrades, backups, and service health while local collectors retain full-fidelity engineer history and send only the structured event contract. Customers control connector access, project scope, identity, retention requirements, and permitted use. LinearB publishes cloud, hybrid, and on-premise support, custom APIs, provisioning, and configurable retention. Buyers should identify which LinearB components run where, which features inspect code or PR content, who operates upgrades, and how residency, deletion, subprocessors, and incident ownership differ by deployment.
03 / attribution
AI telemetry and outcome attribution
LinearB has meaningful AI measurement depth: direct Copilot and Cursor data, acceptance, AI-assisted PRs, broad tool detection, cycle time, refactor, rework, change failure, and workflow outcomes. Clavinci adds normalized local sessions, token classes, cache behavior, actual and API-equivalent cost, re-rolls, prompt complexity, tool-task fit, and compaction, then provides a versioned and disputable path to issues, delivery, incidents, and 30-day survival.
04 / access
Privacy, access, and individual visibility
LinearB's code review and PR automation features create value by reading and acting on code and workflow context. Clavinci creates value from structured metadata while excluding those content classes. Its direct-manager reads are logged for the engineer, skip-level drill-down requires a reason, aggregate views have privacy floors, peers cannot inspect individuals, and ranking or automated people decisions are blocked. This is a scope and governance distinction, not a claim that LinearB lacks enterprise security.
05 / competitive advantage
Where each product goes further
Where Clavinci goes further
- Clavinci provides normalized local session, token-class, cache, actual-cost, and API-equivalent-cost evidence across nine collector implementations.
- Clavinci provides replayable multi-signal attribution, proportional session links, engineer disputes, admin resolution, and cost per merged line that survives 30 days.
- Clavinci joins sprint spillover, incidents, on-call load, re-rolls, tool-task fit, prompt complexity, churn, and compaction to AI-attributed work in one graph.
- Clavinci structurally excludes code and work text and provides engineer-visible individual-read audit, reasoned skip-level access, privacy floors, and anti-metric refusals.
- Clavinci exposes collector coverage, data freshness, sync failures, silent engineers, rate limits, crashes, latency, and integration health.
Where LinearB goes further
- LinearB provides AI code review with findings and fixes plus stateful review behavior that Clavinci does not offer.
- LinearB provides programmable PR routing, expert assignment, labels, approval and test enforcement, generated descriptions, and merge automation that Clavinci does not offer.
- LinearB provides developer surveys, DSAT, industry benchmarks, MCP access, customizable reporting, and broad AI detection beyond Clavinci's supported collector set.
- LinearB provides delivery forecasting, resource allocation, capitalization, project-cost reporting, and a wider executive control plane.
- LinearB supports cloud, hybrid, and on-premise environments, while Clavinci is a company-operated hosted service only.
06 / buyer questions
Questions to take into evaluation
- 01
Can the product trace one assistant session through tokens, cost, commit, Jira issue, pull request, CI, deployment, incident, and 30-day survival?
- 02
Which AI tools provide direct session and token data, and which are detected through repository or pull request patterns?
- 03
Can attribution be recomputed and disputed, and how are corrected links reflected in historical ROI?
- 04
Can we compare AI and manual work across sprint spillover, review turnaround, change failure, incidents, rework, and survival?
- 05
Which features read source, diffs, PR descriptions, review comments, issue text, CI logs, or other work content?
- 06
Does access policy apply consistently across UI, API, MCP, exports, and generated reports, and can engineers see individual reads?
- 07
Do we need AI code review, PR routing, approvals, test enforcement, labels, and generated descriptions from the same platform?
- 08
Do we need surveys, DSAT, benchmarks, forecasting, resource allocation, capitalization, and project-cost reporting?
- 09
Can admins distinguish missing collection from a genuine trend through freshness, coverage, and integration-health evidence?
- 10
Which cloud, hybrid, or on-premise responsibility model and commercial package fit procurement?