Clavinci vs Faros

Clavinci joins AI sessions, git, Jira, pull requests, CI, deployments, and incidents in a versioned graph for AI cost, delivery, quality, and governed access decisions.

Faros creates a unified engineering knowledge and data model spanning people, agents, tools, processes, services, products, initiatives, delivery, quality, and business outcomes.

Nine local collector implementations cover Claude Code, Codex, Cursor, Gemini, Kiro CLI and IDE, OpenCode, OpenClaw, and Every Code with sessions, token classes, caches, models, tool events, and compaction.

Faros Token Intelligence publishes spend across teams, tools, and models, maps tokens to work and outcomes, and combines AI tool, code, delivery, and custom-source data through flexible ingestion.

Clavinci 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.

Faros traces AI spend across teams, tools, and models to work and outcomes and provides AI ROI, productivity, quality, maturity, and investment analysis.

Clavinci uses time, file overlap, explicit session trailers, and Jira keys, supports proportional multi-session links, replays versioned logic, and lets engineers dispute incorrect attribution.

Faros provides intelligent attribution across teams, services, organization structures, products, initiatives, and AI outcomes through its unified data model.

Clavinci combines DORA with AI lead time, AI change failure, rework, rollback and hotfix evidence, and 30-day survival of attributed code.

Faros provides DORA, throughput, cycle time, rework, quality, deployment, failure, MTTR, human-versus-AI code analysis, and internal or industry benchmarks.

Clavinci compares AI and manual velocity, spillover, cycle count and time, time in status, aging WIP, stale issues, story-point economics, epics, and issue attribution.

Faros unifies non-standardized planning data for initiatives, features, epics, agile health, say-do ratios, planned and unplanned work, scope creep, capacity, dependencies, schedules, budgets, and forecasts.

Clavinci 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.

Faros provides PR velocity, cycle-time breakdowns, review speed, load, patterns, cross-team and cross-geography dependencies, quality correlations, and customizable analytics.

Clavinci joins hotfix rate, incidents, MTBI, on-call page rate, reopen rate, MTTR, and change failure through PagerDuty, Opsgenie, Incident.io, or file adapters.

Faros connects incident management, service health, code coverage, test coverage, test flakiness, change failure, MTTR, deployments, vulnerabilities, and on-call context through its wider data model.

Clavinci combines re-roll rate, tool-task fit, native and inferred acceptance, prompt complexity, churn, compaction, rework, incidents, and 30-day survival.

Faros provides human-versus-AI code analysis, rework, bugs, incidents, code quality, root-cause analysis, developer sentiment, and AI-agent context and guardrails.

Clavinci compares tool and model mix, mix shift, concurrent use, adoption curves, session duration, cost, quality, and attributed outcomes across supported assistants.

Faros supports AI transformation programs with adoption tracking, power-user and dormant-license identification, cohort comparisons, tool tests, before-and-after metrics, benchmarks, enablement, and executive visibility.

Engineers see personal sessions, spend, outcomes, sprint and DORA context, quality, dark work, export, deletion, disputes, and an audit of who accessed their data.

Faros combines developer surveys, self-serve metrics, workflow and sentiment correlations, AI recommendations, IDE and chat access, and context delivery to coding agents.

Clavinci provides direct-manager ROI, delivery, review, stability, quality, and adoption views plus privacy-floored aggregates for skip-level leaders.

Faros provides team, service, organization, product, initiative, portfolio, DevEx, AI transformation, finance, roadmap, and executive views with custom dashboards and AI summaries.

Direct-report reads are engineer-visible. Skip-level drill-down needs a reason, peers cannot inspect individuals, aggregates have privacy floors, and anti-metrics block rankings and automated people decisions.

Faros publishes robust RBAC, customizable permissions, automatic role-based redaction, enterprise authentication, compliance controls, and secure deployment options.

Clavinci excludes prompts, responses, source, review comments, issue descriptions and comments, CI logs, environment variables, and secrets by schema and regression test.

Faros can ingest any-source engineering context and its agent-context product uses past pull requests, tickets, and architectural decisions. Field scope follows connectors, configuration, and the selected use case.

Clavinci exposes uptime, collector and sync coverage, freshness, silent engineers, parser errors, rate limits, crashes, latency, Jira health, and notification delivery.

Faros publishes monitored data feeds, near-real-time freshness, performance at enterprise scale, high-performance connectors, enterprise security certifications, support, and large-volume infrastructure.

Clavinci sends stale-PR, aging-WIP, dark-session, and hotfix triggers through in-app, Slack, email, or webhooks. It does not provide a general analytics, planning, catalog, or workflow builder.

Faros provides custom metrics and dashboards, natural-language queries, workflow automation, SLA enforcement, catalogs, initiative tracking, capacity and budget forecasting, capitalization, surveys, and AI recommendations.

Clavinci is a commercial company-operated hosted service with local collectors. Customers control integrations and governance but do not deploy or maintain the application stack.

Faros supports SaaS, hybrid, and on-premise deployments plus cloud connectors, Source CLI, Events CLI, webhooks, APIs, and custom sources.