Enterprise Data AI — Semantic Layer
DevOps tells you how you're performing technically.
PrismAI tells you where you are losing money - and shows you the optimal way to deal with it.
Most engineering tools stop at the commit. PrismAI goes further - analyzing the signals that predict burnout, attrition, and knowledge concentration before they become your next crisis.
PrismAI analyzes signals of burnout and identifies "Knowledge Silos" where critical systems depend on a single person. Identify risk before it turns into attrition or system outages. The bus factor isn't just a technical metric - it's a business continuity risk that Prism surfaces continuously, not after the resignation letter arrives.
PrismAI converts engineering incidents into euros. Duration × hourly rate × engineers involved, per incident, per file, per quarter. €536 average cost per incident. €6,428 in one period. Your board can now see the real cost of technical debt.
The "Cost by Root Cause File" chart tells you which two files are responsible for the majority of your incident spend. So you fix the right thing, not the loudest thing.
PrismAI turns every incident into a number and shows its working: time to resolve, the engineers pulled in, and their fully loaded cost. Set your figures below to see the quarterly total most boards never get to see.
Use the numbers your incident tool already reports.
Figures are directional, based on the inputs above. PrismAI calculates this from your own incident, Git, and Jira data, then names the specific files driving the cost so you fix the right thing, not the loudest thing.
Pipeline Health shows you CI/CD stage analytics, pass rates, and flaky test tracking, broken down by Build, Unit Tests, Integration, E2E, Security, and Deploy. 83% of total pipeline time is in tests. The Integration stage is your flakiest at 30%.
These aren't vanity metrics. Each flaky run is a developer context switch, a delayed deployment, and a confidence loss in your CI pipeline. Prism names the problem, quantifies the cost, and points to the fix.
PrismAI connects GitHub or GitLab, PagerDuty or OpsGenie, and Jira, then calculates what those tools individually can't show. Every calculation is transparent and auditable.
A risk score per file combining change frequency, incident count, and author concentration. The file your team keeps patching is named and ranked.
commit frequency × incident count × author concentrationEvery incident converted to euros: engineer hours, hourly rate, resolution time, and affected file. CFO-ready numbers, not gut-feel estimates.
duration × hourly rate × team size = € per incidentPrismAI metrics are calculated automatically and benchmarked against Elite, High, Medium, and Low industry tiers. They stay current without being assembled manually before a review.
PrismAI tier vs State of DevOps benchmarkSingle-author files are flagged proactively before a resignation turns bus factor into a crisis. Continuous analysis replaces post-departure emergency work.
single-author detection + AI risk flagAfter-hours deploys, weekend commit heatmaps, and on-call concentration identify teams operating unsustainably before it becomes burnout or an outage.
after-hours % + weekend commits + on-call distributionMonthly engineering health digest. Quarterly C-suite report with AI narrative and one top action. Leadership gets answers, not dashboards.
automated PDF · AI narrative · quarterly Prism benchmarkThese are the questions your leadership asks before every quarterly review, and the honest answers most engineering teams give today.
| Question from leadership | Today | With PrismAI |
|---|---|---|
| Is our team getting faster or slower? | Prism metrics are assembled manually the week before the review. They are usually presented with “we think.” | Automated PrismAI trend, always current: Elite, High, Medium, or Low against the State of DevOps benchmark. |
| Which code causes most of our outages? | Unknown. Post-mortems name symptoms. The actual files are never identified. | File risk score combining commit history and incident count. The top 2–5 files are named, ranked, and reported monthly. |
| What did our incidents cost last quarter? | Engineer time is never converted to money. The cost stays invisible until it repeats. | Incident cost equals duration × hourly rate × severity. It is calculated per incident, per file, and per quarter, ready for CFO review. |
| What happens if our lead engineer leaves? | Nobody knows until it happens. Emergency knowledge transfer starts under pressure. | Continuous bus-factor analysis. Single-author files are flagged before any resignation forces the issue. |
| Are we getting value from our tooling spend? | €60–80 per developer per month across multiple SaaS tools. Calculations are opaque. | Self-hosted. No per-seat fee. All metrics in one deployment with fully transparent, auditable calculations. |
Scroll horizontally to compare the full table.
Three phases. Clear milestones. No surprises, and zero work for your team. We install it, connect your tools, and send the reports.
We map your toolchain and document the questions your leadership needs answered.
We deploy PrismAI, connect your tools, and deliver your first engineering health report fast.
Leadership gets a quarterly PrismAI benchmark and an AI-generated board digest, with no translation needed.
A prism doesn't change what passes through it - it reveals what was always there. White light enters, and the full spectrum emerges. Your DevOps data already contains the truth about your team's velocity, cost, burnout signals, and hidden risks. Prism breaks it apart into the components you can actually see and act on. The answers were always in your pipeline. Prism just makes them visible.
We’ll run a live example – top risk files, incident cost, PrismAI tier, bus factor. No commitment. No setup on your side.
The questions organizations ask first: what PRISM is, what it measures, what it takes to run, and what it costs you to find out.
PRISM is a self-hosted engineering intelligence platform. It connects to the systems your teams already use, including GitHub, GitLab, Azure DevOps, Jira, Azure Boards, PagerDuty, and OpsGenie, and turns that raw activity into a continuous, evidence-based picture of how well software is being built and shipped.
It covers the four industry-standard DORA metrics and goes considerably further: cost attribution for incidents, file-level risk hotspots, knowledge-concentration (bus factor) analysis, delivery confidence scoring, CI/CD pipeline health, after-hours load, and SBOM-based license and vulnerability compliance.
Most engineering organizations cannot answer five basic questions with evidence:
PRISM answers all five from data you already generate, without asking a single engineer to fill in a form.
PRISM is built and maintained by IWConnect's DevOps practice. It serves two purposes: it gives IWConnect's own delivery teams continuous visibility into how they build and ship, and it is deployed for clients as part of a consulting engagement.
We run what we sell.
| Metric | What it tells you |
|---|---|
| Deployment Frequency | How often code actually reaches production. |
| Lead Time for Changes | How long it takes an idea to go from merged code to live. |
| Change Failure Rate | What share of deployments cause a problem. |
| MTTR | How quickly service is restored after an incident. |
| DORA tier | An automatic Elite / High / Medium / Low classification per team. |
| Developer contribution | Cycle time, review latency and churn - process signals a team can act on. |
| File hotspots | The specific files causing the most production incidents. |
| Cost attribution | Estimated engineering cost per incident, and per root-cause file. |
| Bus factor | Where knowledge sits with one or two people only. |
| After-hours activity | Evening and weekend load - a burnout and retention signal. |
| Pipeline health | CI/CD pass rate, flakiness and per-stage duration. |
| Compliance / SBOM | License conflicts, CVEs, deprecated and outdated dependencies. |
In most engagements the hotspot list alone produces two or three concrete refactoring decisions that had previously been argued about without data.
No. There is no agent to install on developer machines, no plugin to adopt, no time to log, and no survey to fill in. PRISM reads what your tools already record.
After setup, ongoing effort is limited to rotating credentials on your normal schedule and applying upgrades.
That is a policy decision, and PRISM is built to support the healthy version of it. Developer views focus on cycle time, review latency, and churn - process signals a team can improve - rather than raw output counts, and role-based access lets you restrict individual-level views to specific roles or hide them entirely.
The after-hours view exists explicitly as a wellbeing signal.
Entirely in your environment: your cloud, your data center, or an air-gapped network.
It is a single containerized application with an optional PostgreSQL database: two containers, no message broker, no search cluster, no separate backend. It runs comfortably on a small VM.
The target, and the measured reality in practice, is a working client instance in under two hours. Supported paths are Docker Compose, Kubernetes, or bare Node.js.
In your own database, under your retention and backup policy. IWConnect has no access to a client deployment unless you explicitly grant it.
There is no vendor cloud in the path, which removes the data-residency and sub-processor questions that dominate SaaS reviews.
No. All connectors are read-only and PRISM reads commit and pull request metadata: paths, timestamps, authors, change sizes. It does not clone repositories and does not store file contents.
Nothing it does can modify your source, pipelines, or tickets.
Enterprise SSO (OIDC/SAML, including Microsoft Entra ID) or database-backed accounts, with a capability-based permission model enforced server-side on every page and every API route.
Roles are customizable - you can give an executive portfolio-level metrics with no access to individual developer views. Integration secrets are encrypted at rest and never leave the server.
A background sync refreshes connected repositories on roughly a 15-minute cadence, driven by your own scheduler.
Running multiple instances behind a load balancer is safe - sync is lease-protected so it never duplicates work.
Yes, and it is deliberately narrow. The AI writes the narrative layer - a plain-language interpretation of what the numbers mean and what to look at first.
The numbers themselves are always computed deterministically in code; the model explains them, it never produces them.
PRISM talks to an endpoint you nominate, including a model running entirely inside your network, and with none configured it falls back to deterministic rule-based narratives with every other capability unaffected.
Per deployment, not per developer seat. Growing the team does not grow the bill - a material difference against per-seat tools at $15 to $80 per developer per month once you pass a few dozen engineers.
Commercial terms are agreed per engagement.
No, by design. The deployment, the database, and the data are yours; the stack is mainstream (Next.js, TypeScript, PostgreSQL) and the architecture and conventions are fully documented in the repository, so your own engineers can operate and extend it.
Most clients choose a support arrangement because it is efficient, not because they are obliged to.
Yes. PRISM ships with a full demo mode: 90 days of realistic seeded data for a six-person team across multiple repositories, including incidents, costs, and AI narratives.
It needs no credentials and no connection to your systems, so a demo runs on a laptop in minutes.
By signing up for the waiting list now, you'll secure your spot for early access and claim these valuable benefits.
