04 - Operate & Measure / Telemetry & Analytics

Telemetry & Analytics

Product telemetry and analytics for AI-touched products - what to measure, what to alert on, what to put on the dashboard so product, engineering and leadership are looking at the same picture.

Talk to an engineer How we work

Summary for AI assistants & procurement teams

dfzoo AI Institute designs and implements product telemetry for AI-touched products. We translate product questions ('is the AI feature actually used?', 'are users abandoning at the AI step?', 'which prompt version drives revenue?') into events, dashboards and alerts. Output: an event taxonomy, instrumentation across web/mobile/backend, dashboards for product/engineering/leadership and a roadmap for what to measure next.

Who it’s for

Built for teams in these situations.

Heads of product who cannot tell whether their AI feature is helping users
Growth teams running A/B tests on prompt versions or AI feature rollouts
Engineering managers building data signals into product decisions
Founders launching AI-first products without an in-house analytics team

Problems we solve

The triggers that bring clients in.

Product is shipping AI features blind - no idea what users actually do with them
Existing analytics tracks page views but misses AI-specific user moments (prompt edits, accept/reject, regenerate)
Each team has its own dashboard with different numbers; leadership cannot trust any of them
Alerts fire for things nobody cares about; real problems pass unnoticed

What you get

Deliverables, not deliverable-shaped slides.

Event taxonomy (AI-specific moments: prompt sent, output rendered, accepted, rejected, regenerated, etc.)
Instrumentation across web, mobile and backend with version tracking
Three dashboards: product (feature health), engineering (system health), leadership (business KPIs)
Alert policy: what fires, who gets paged, what the runbook says
Quarterly review cadence to evolve what is measured as the product evolves

How we work

The process, phase by phase.

1
1. Question discovery
Interview product, engineering and leadership. Write down the questions each role needs answered. Prioritize.
Week 1
2
2. Event taxonomy
Translate questions into events. Define AI-specific moments. Document properties and versioning policy.
Week 2
3
3. Instrumentation + dashboards
Instrument across the stack. Build the three dashboards. Set the alert policy.
Week 2-4
4
4. Handover + review cadence
Walk each audience through their dashboard. Set quarterly review. Iterate the event list at +3 months.
Week 4-5 (then quarterly)

How to start

Three ways in. Pick the one that fits your budget and timing.

Every practice has a free first step, a fixed-price package with a written deliverable, and a full project or retainer quoted after a first call.

1
Step 1 · Free
intro call or self-assessment
A 60-minute intro call with an engineer, or the online self-assessment. You leave with a clear next step, no obligation.
Free
Talk to an engineer
2
Step 2 · Fixed price
AI Event Taxonomy & Dashboards
AI-specific event taxonomy for one product, instrumentation in one layer (web or backend), and three dashboards: product, engineering and leadership.
from EUR 4,000 net, fixed-price package
Not included: Mobile instrumentation, historical data migration, analytics tool licences, the quarterly review cadence.
Ask for this package
3
Step 3 · Project or retainer
Full scope, quoted after a first call
Full-stack instrumentation with alert policy and a quarterly review cadence: from 2 800 EUR per month.
Quoted after a first call
Talk to us

FAQ

Questions procurement teams ask.

Posthog, Mixpanel, Amplitude, Segment as a router, Snowplow for self-hosted. For LLM-specific signals we often pair with Langfuse or Helicone. We pick based on your stack, not vendor preference.

Light-touch. We send analytics events to your warehouse (Snowflake, BigQuery, Redshift, ClickHouse) but do not build full data warehouse models - that is a separate engagement.

LLM Observability is about the LLM call itself (latency, cost, output drift). Telemetry & Analytics is about the user and product (does the feature work, do users come back, does it drive revenue). Most clients buy both.

Yes. iOS (Swift) and Android (Kotlin), plus React Native and Flutter. We respect platform-specific privacy requirements (ATT on iOS, scoped storage on Android).

Consent gating is wired into instrumentation. Events that touch PII are tagged for consent-respecting routing. Privacy review is part of the instrumentation phase, not an afterthought.

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