23 modules · 3 layers · 12 integrations
See · Understand · Act
The hybrid architecture that turns your engineering data into concrete, AI-powered actions.
Is your AI tooling actually paying off? Now you know.
License costs vs. measured productivity impact, side by side. Total cost, time saved, productivity gain, net value and ROI trend over time — with a per-tool cost breakdown so you can see exactly where the money goes.
- Total cost vs. time saved & productivity gain
- Net value and ROI trend over the period
- Per-tool cost breakdown (seat vs. usage-based)
- Lead time, PR throughput & accepted-lines deltas
From AI token spend to the work it produced — and its real cost
Go beyond usage counters: attribute every dollar of AI spend to merged PRs, commits and accepted lines. Get a keep / scope / cut verdict per tool, cost per PR and per 1,000 lines, per-team budgets with alerts, and the wasted spend from sessions that produced nothing.
- Cost per merged PR, per commit, per 1,000 lines
- Keep / Scope / Cut verdict + routing recommendations
- Per-team token budgets with alerts & wasted-spend detection
- Measured billing (real $) vs estimated, per model
Prove whether AI code reviewers actually help — with a controlled cohort
DevPrism detects which PRs were reviewed by an AI bot — CodeRabbit, Qodo, Sourcery, Claude Review and more — and compares them against PRs without one. See the real delta on merge rate, cycle time, pickup time and rework, plus a breakdown by review tool.
- With-AI vs. without-AI cohort comparison
- Deltas on merge rate, cycle time, pickup time & rework
- AI review coverage across all your PRs
- Breakdown by tool: CodeRabbit, Qodo, Sourcery, Claude Review…
Cross-correlate AI adoption with every engineering metric
See how Copilot/Cursor usage impacts velocity, code quality, coverage, technical debt, and lead time. Alignment indices quantify each correlation — no other platform connects AI adoption to your full engineering health.
- Alignment indices: AI vs. quality, velocity, debt
- Copilot, Cursor, Devin Desktop, Claude Code, Codex
- Positive / Neutral / Negative impact classification
- AI token cost ↔ quality & velocity correlation
Independent research backs the cost axis: cleaner code measurably lowers AI agent token cost for the same task. Sonar, 2026
6 AI agents that investigate, not just display
An Investigation Agent performs 4-step root-cause analysis. A Quality Guardian catches regressions before your users do — then acts on them under policy. A Capacity Planner spots burnout risk. A Token Efficiency Guardian recommends how to cut AI spend. All coordinated by a MAF Agent Coordinator.
- Root-cause analysis in 4 steps
- Quality regression → causal PR attribution → governed action under policy
- AI reviewer impact: Qodo, CodeRabbit & Claude Review measured
- Token Efficiency Guardian: proactive AI-cost routing
- DevPrism Agent: ask questions in natural language
Risk-score every PR before it ships
8-factor risk scoring, blocker detection, stale PR alerts, auto-assign reviewers. Write-back to GitHub, Azure DevOps, GitLab — with dry-run by default so nothing happens without your approval.
- 8-factor risk scoring
- Auto-assign & orchestration
- Excel/CSV export for audits
Your IDE becomes your engineering command center
Talk to DevPrism directly from VS Code, Cursor, Devin Desktop, or Claude Desktop. Investigate alerts, read DORA metrics, diagnose quality regressions — all without leaving your code. 23 tools exposed via Model Context Protocol.
- 23 tools: dashboards, diagnostics, write actions
- Natural-language investigation from your IDE
- Secure: tenant-isolated, rate-limited, audited
- Compatible: Copilot, Cursor, Devin Desktop, Claude Desktop
All 23 modules at a glance
Three layers — from observation to autonomous action.
See
BI dashboards synced from 12 providers
Understand
6 AI agents + 6 automated workflows
Act
Graduated autonomy: Suggest → Approve → AutoAct
Enterprise · NEW
Bring Your Own LLM — your key, your data
Route every AI agent, workflow and chat to your own LLM provider — or keep DevPrism's managed model. Your inference runs on your contract, under your data-residency rules.
- Your API key, encrypted (AES-256) — never shown in clear
- "My data doesn't go to Google" — meet infosec & DPA requirements
- Control your LLM spend via your Google / OpenAI / Azure / Anthropic / Mistral contract
- Same audit trail, guardrails & function-calling on every provider
The platform in action
AI-powered insights, real-time data, unified dark-mode interface.
