Qlik Sense is already a powerful platform for interactive analytics—but the real leap happens when you bring AI inference APIs into the experience. Instead of exporting data to a separate tool (or waiting for a centralized data science team), you can embed AI-driven insights directly inside dashboards: classifications, summaries, anomaly flags, recommendations, and even “why” explanations.

In this guide, you’ll learn how to develop AI extensions in Qlik Sense using inference APIs, with practical architecture patterns, implementation tips, and real-world examples you can adapt quickly.

Why Add AI Inference to Qlik Sense Extensions?

Traditional BI answers “what happened?” AI-enhanced BI can also answer:

• What’s likely to happen next? (predictive signals from a model endpoint)
• What should we do about it? (recommended actions)
• Why did it happen? (explanations and feature importance)
• What changed? (anomaly detection and trend breaks)
• What does this mean in plain English? (summarization and narrative insights)

The big win: users stay in Qlik Sense, and AI becomes a natural part of exploration—filter, slice, and get model outputs in context.

Key Concepts: Qlik Sense Extensions + Inference APIs

What a Qlik Sense extension does

A Qlik Sense extension is a custom visualization or interaction component (typically JavaScript + HTML/CSS) that:

• Reads data from Qlik’s associative engine
• Renders UI elements in the sheet
• Responds to selections and filters
• Can call external services (with the right security model)

What an inference API provides

An inference API is an endpoint that accepts data (features, text, events) and returns a prediction or transformation, such as:

• Binary/multi-class classification (e.g., churn risk)
• Regression (e.g., demand forecast)
• Embeddings (for semantic similarity)
• Text generation (summaries, explanations, categorization)
• Anomaly scores

Architecture Patterns That Work in Production

Pattern 1: Extension → Backend Proxy → Model Endpoint (Recommended)

Best for: security, governance, scaling, and observability.

Flow:

1. Extension collects current selection context + measures
2. Sends request to a controlled backend (API gateway/proxy)
3. Backend authenticates, validates payload, logs usage
4. Backend calls model endpoint (internal model server, cloud AI, etc.)
5. Returns inference to the extension for rendering

Why this is best:

• Keeps secrets off the browser
• Supports rate limits and caching
• Enables audit logs and model versioning
• Reduces CORS headaches

Pattern 2: Extension → Direct Call to Inference API (Use Carefully)

Best for: prototypes or internal-only networks with strict controls.

Risks:

• Token exposure in client code
• CORS complexity
• Harder to enforce governance and usage policies

Pattern 3: Precompute AI Features in the Data Pipeline

Best for: stable metrics, high concurrency, predictable workloads.

Here, you run inference upstream (batch or streaming), then load results into Qlik as fields/measures. Users filter and analyze AI outputs without triggering real-time calls.

If your organization is moving toward real-time signals, you’ll also want an event-driven approach for AI enrichment (especially for fast-changing operational dashboards). A helpful reference pattern is this guide on building modern real-time pipelines: Automating Real-Time Data Pipelines with Airflow, Kafka, and Databricks.

Practical Use Cases (with Examples)

1) Churn risk scoring inside a customer dashboard

What users see: a “Churn Risk” KPI and a table showing risk by segment, with drivers.

Inference API input:

• Usage frequency (last 7/30 days)
• Support tickets
• Plan type
• Tenure
• NPS or CSAT

API output:

• risk_score (0–1)
• risk_bucket (low/med/high)
• Optional: top contributing features (if your model supports it)

Why it works well in Qlik:

Selections (region, plan, cohort) instantly reframe the scoring request, making the AI feel interactive—not static.

2) Automated narrative summaries for executive dashboards

Instead of expecting every stakeholder to interpret charts, embed a “What changed?” summary:

Prompted summarization input:

• Current period KPIs
• Previous period KPIs
• Top movers (product, region, channel)

Output:

• 4–6 bullet insights in plain English
• Suggested questions to explore next

This is especially useful when your dashboards are complex and the audience is time-constrained.

For broader context on agentic and generative AI capabilities that can complement BI, see: AI Agents.

3) Anomaly detection on operational metrics

Example: detect unusual spikes in order cancellations by location.

Inference API input:

• Time series values (daily/hourly)
• Optional: holidays, campaigns, operational incidents

Output:

• anomaly score
• anomaly flag
• baseline expectation
• confidence level

The extension can highlight anomalies directly in a line chart or a “Watchlist” table.

Step-by-Step: How to Build an AI-Powered Qlik Sense Extension

1) Define the AI interaction model (real-time vs. near-real-time)

Ask:

• Do we need inference per selection, or can we cache per segment?
• What’s acceptable latency (e.g., <500ms, <2s)?
• How many users will trigger calls concurrently?

Rule of thumb: if it’s likely to be called frequently, implement caching and consider precomputing.

2) Design the request payload around “selection context”

The most common mistake is sending too much data (entire tables) rather than aggregated features.

A good payload typically includes:

• Current filters (region, product, time window)
• Aggregated features (sums, averages, counts)
• A stable “key” for caching (e.g., hash of selection state)

Example payload (conceptual):

• filters: { region: ["EMEA"], plan: ["Pro"], period: "last_30_days" }
• features: { active_users: 18200, ticket_rate: 0.12, expansion_mrr: 42000 }
• model_version: "churn-v7"

3) Implement a secure backend proxy (strongly recommended)

Your proxy should handle:

• Authentication (JWT/OAuth/service-to-service)
• Authorization (who can call what model)
• Input validation (schema checks, limits)
• Caching (Redis or in-memory)
• Observability (metrics, logs, traces)

If you want to keep extensions maintainable and governed over time, treat this proxy like a product—not a quick script.

4) Build the extension UI with “AI states” in mind

Users don’t just want results; they want trust and clarity. Design for:

• Loading state: “Calculating prediction…”
• Success state: show score + explanation + timestamp
• Error state: actionable message, retry option
• Stale data state: “Last updated 12m ago” if cached

This small UX layer dramatically improves adoption.

5) Handle performance: batching, caching, and rate limits

To keep dashboards fast:

• Batch requests (send one payload for multiple items instead of calling per row)
• Cache by selection hash
• Use server-side aggregation (send features, not raw rows)
• Implement a circuit breaker (if model is slow, return cached or fallback)

Governance and Trust: Don’t Skip This Part

Explainability and transparency

If you’re scoring customers or flagging anomalies, stakeholders will ask “why?” Consider returning:

• top drivers (feature contribution)
• confidence scores
• model version and timestamp

Data privacy and access control

Even if Qlik users can see certain fields, your inference system may have different compliance requirements. Enforce:

• payload whitelisting (only approved fields go to the model)
• masking rules for PII
• per-tenant controls (if applicable)

Observability for AI inside BI

Once AI is embedded in dashboards, failures become business-visible. Monitor:

• latency
• error rate
• call volume
• cache hit ratio
• model drift indicators (if available)

For dashboards that track system health in a clean, actionable way, this practical guide is useful: Technical Dashboards with Grafana and Prometheus.

Real-World Example Blueprint (Putting It All Together)

Scenario: “Sales Coach” extension in Qlik Sense

Goal: show reps what action to take next based on account signals.

How it works:

1. User filters to a territory and time period
2. Extension computes account-level aggregates (or queries a prepared table)
3. Backend calls an inference API:

• predicts opportunity win probability
• recommends next best action (NBA)

1. Extension renders:

• prioritized account list
• NBA text
• “drivers” panel (why the model suggested it)

Outcome: reps don’t just view performance—they get prescriptive guidance inside the same workflow.

Common Pitfalls (and How to Avoid Them)

Pitfall 1: Calling the model once per row

Fix: batch requests and return an array of results; or precompute predictions upstream.

Pitfall 2: Shipping API keys in the extension

Fix: use a backend proxy with proper auth and short-lived tokens.

Pitfall 3: Model output changes without warning

Fix: version your models and include model_version in responses; roll out with feature flags.

Pitfall 4: “Black box” scores nobody trusts

Fix: add explanation fields, confidence, and clear UI cues (what the score means, how often it updates).

Final Checklist: Production-Ready AI Extensions in Qlik Sense

• Selection-aware payload design (features > raw data)
• Backend proxy for security, validation, and governance
• Caching + batching for performance
• Model versioning + predictable contracts
• UX states (loading/success/error/stale)
• Observability and auditability end-to-end

FAQ: AI Extensions in Qlik Sense with Inference APIs

1) What is an inference API, and how is it different from training a model?

An inference API is the production endpoint you call to use a model (get predictions, summaries, classifications). Training is the offline process that creates the model. In Qlik Sense extensions, you typically call inference endpoints—not training jobs—because dashboards need fast responses and stable outputs.

2) Should a Qlik Sense extension call the model API directly from the browser?

In most production environments, no. Direct calls can expose secrets and make governance difficult. A backend proxy is the safer approach because it can enforce authentication/authorization, validate payloads, apply rate limits, and log requests for auditing.

3) How do I keep AI-driven dashboards fast if users change filters often?

Use a combination of:

• Aggregation: send features, not raw tables
• Caching: cache by selection state (hash) and model version
• Batching: score multiple items in one request
• Fallbacks: show cached results if the model is slow or temporarily unavailable

This avoids “death by a thousand calls” when users explore interactively.

4) What AI use cases work best inside Qlik Sense dashboards?

The best fits are use cases where AI results are more useful in context:

• churn/propensity scoring
• anomaly detection
• next best action recommendations
• narrative KPI summaries
• classification/tagging of accounts, tickets, or products

If a task requires heavy back-and-forth or complex multi-step reasoning, it may work better as a separate app—then link results back into Qlik.

5) How can I explain predictions so business users trust them?

Return and display:

• top contributing factors (drivers)
• confidence levels or probability
• threshold logic (what “high risk” means)
• model version + last updated timestamp

Also, keep language simple: “High risk because support tickets increased and usage dropped over 30 days.”

6) How do I handle sensitive data (PII) when sending requests to an inference API?

Implement controls in the backend proxy:

• allowlist fields that can be sent to the model
• mask or tokenize PII
• enforce role-based access and tenant isolation
• store audit logs (who called what, when, and with which model version)

Even if users can view data in Qlik, your AI service may have stricter compliance requirements.

7) What’s the difference between real-time inference and precomputed predictions?

• Real-time inference: predictions update instantly with user selections; great for interactive exploration but needs caching and scaling.
• Precomputed predictions: computed upstream (batch/stream) and loaded into Qlik; very fast and stable, but less dynamic.

Many teams use a hybrid: precompute the base scores, then do real-time refinement for specific views.

8) How do I monitor AI features embedded in dashboards?

Monitor both system and model behaviors:

• API latency, error rate, and throughput
• cache hit ratio
• response size and timeouts
• model version adoption
• drift indicators (if you track them)

Treat the inference API like a critical dependency of your BI layer—because once it’s embedded, it is.

9) What technologies can power the inference API behind Qlik Sense?

Common options include:

• containerized model servers (Python/FastAPI, etc.)
• managed ML endpoints from cloud providers
• LLM gateways for summarization/explanations
• feature stores (optional) to standardize inputs

The key is to maintain a stable API contract and strong governance—regardless of what’s behind it.