AI product & platform teams
Track AI features across apps, services, and markets. See which experiences are healthy and which silently burn budget.
Private beta Designed for AI product & data teams
Stop guessing if your AI is paying off .
AIPIMetrics connects model telemetry with business impact. One unified AIPI score plus a live Business & Impact dashboard that shows if your AI products are actually saving time, reducing cost, and increasing capacity.
No credit card. We’re onboarding a small number of teams to shape the roadmap.
- Built for
- AI product, data & platform teams
- Works with
- LLM apps, chatbots, internal copilots, automation flows.
- What you get
- AIPI score, latency/cost/reliability, time saved, ROI proxy, capacity gain.
AIPI score · last 7 days
0.92 ▲ On track
Events analysed
1,452
Technical health
- Latency
- 705 ms (41% under target)
- Cost / req
- $0.0020 (19% under)
- Accuracy
- 82.3%
- Reliability
- 88.7%
Business & impact
- Time saved
- 73.5 h / week
- Capacity gain
- +26.3%
- AI utilization
- 11.3%
- ROI proxy
- 250×
AIPI trend (7d) +3.3 pts vs. last week
Daily AIPI score based on events window. Calculated in your own Firestore project.
Built for teams who own AI products end‑to‑end.
AIPIMetrics gives you a shared language for AI performance – from latency and cost to time saved and capacity gain. Perfect for teams who need to justify AI investments to leadership without drowning in raw logs and ad‑hoc dashboards.
Data & analytics leaders
Move from sandbox experiments to accountable AI. Report AI impact in language that finance and business stakeholders understand.
You're a good fit if…
- ✓ You already run one or more AI-powered products in production (LLM app, chatbot, internal copilot, automation).
- ✓ You want to go beyond prompt tweaks and focus on business KPIs, unit economics, and strategic impact.
- ✓ You prefer keeping data in your own Firebase/Firestore project instead of shipping it to yet another SaaS.
During the private beta we’ll onboard only a small number of teams and work closely with you on your AI impact model.
How AIPIMetrics works in your stack.
You keep your infra. AIPIMetrics adds a thin, opinionated layer to structure your telemetry and compute AIPI and business metrics on top of your own Firestore data.
- 1
Instrument your events
Call
/collectfrom your apps or backend. Send latency, errors, cost, and – where possible – business signals like time saved or units processed. - 2
We aggregate & score
A Cloud Function calculates your AIPI score and aggregates (7d/30d window) directly in Firestore: performance breakdown, trend, and a dedicated Business & Impact block.
- 3
You monitor & decide
Use the dashboard for daily health checks and business reviews. Answer questions like “Is this AI feature actually worth it?” without building custom reports from scratch.
The metrics AIPIMetrics gives you out of the box.
No more screenshots of Grafana and random spreadsheets. AIPIMetrics merges technical telemetry with business‑oriented signals into a single, opinionated model.
Technical performance (feeds AIPI)
- Latency & efficiency – average latency vs. your target, normalized into an efficiency score.
- Cost per request – effective AI infra cost per request vs. target.
- Reliability – error rate and its impact on your AIPI score.
- Accuracy proxy – average accuracy estimate (0‑1) where you have it.
Business & impact (separate block)
- Time saved (h) – cumulative hours saved compared to manual work.
- Capacity gain (%) – output growth vs. baseline units.
- AI utilization – share of processes already powered by AI.
- ROI proxy – time saved × cost per hour vs. AI infra cost.
- Request volume – total AI events in the window.
- Industry benchmark delta – your AIPI vs. your benchmark.
Business metrics never override AIPI. The core AIPI score stays a clean technical performance index; the Business & Impact block tells the ROI story around it.
Where AIPIMetrics is a no‑brainer.
Anywhere you’re using AI at scale and need to answer a simple question: “Is this actually worth it?”
Customer support copilots
Measure handle‑time reduction, resolution reliability, and cost per conversation for GPT‑powered support flows.
Internal productivity tools
Track hours saved and capacity gain in AI‑assisted code review, document drafting, or research workflows.
Public LLM products
Keep latency, cost, and reliability within budget while showing leadership a clear, consistent impact narrative.
Join the AIPIMetrics private beta.
Tell us a bit about your AI stack and what you want to measure. We’ll onboard a small number of teams, help you wire the SDK, and iterate on the impact model together.
- • Keep data in your own Firebase/Firestore project.
- • Simple HTTP /collect endpoint and TypeScript SDK.
- • Designed to work alongside your existing observability tools.
Request early access
We’ll get back to you with next steps, a short onboarding call, and access to the dashboard.
Frequently asked questions
Do I need to move my data to AIPIMetrics? →
No. Events and aggregates live in your own Firebase/Firestore project. AIPIMetrics provides the event schema, Cloud Functions, and dashboard that sit on top of it.
What does AIPI actually measure? →
AIPI is a normalized index (0–1) combining four pillars: accuracy, efficiency (latency), reliability (errors), and cost per request – against your own targets. Business metrics are tracked separately in the Business & Impact block.
Do I have to provide all business inputs? →
No. Every business field is optional. AIPIMetrics works even if you only send core telemetry (latency, cost, errors). Where data is missing, we fall back to safe defaults and explain them via notes in the dashboard.
Who is behind AIPIMetrics? →
AIPIMetrics is built by a developer who has spent years building data products and internal tools, frustrated by how hard it is to answer a simple question: “Is this AI feature actually creating value?” The private beta is intentionally small so we can work closely with early teams.