Author: snp

The Hidden Cost of “Simple” Search Apps

Many organizations already leverage Azure AI Search—a powerful service that makes text and documents searchable through semantic and vector search.

But time and again, we see a familiar pattern:

• A new web app is built (maybe in Streamlit or React).
• It’s hosted on Azure App Service or virtual machines.
• Authentication, hosting, monitoring, and DevOps pipelines are duplicated.
• And in the end, users get a search box and a results table.

The outcome delivers business value, yes—but at a high operational cost and unnecessary complexity.

A Simpler, Smarter Alternative

Now imagine a different approach.

What if your users could:

• Type a search query,
• Get semantic results with highlights and summaries, and
• See them directly inside the Power BI dashboards they already use every day?

No new app.
No separate portal.
No extra infrastructure to maintain.

Just a familiar search box embedded in Power BI, powered by Azure AI Search and AI summarization—running entirely inside Power BI, with no Power Apps or Power Automate required.

What the Solution Includes

This integrated Power BI solution offers:

• Free-text input for users to type search queries.
• Native Power BI filters (date, region, product, etc.) alongside semantic search results.
• Paging support for navigating large result sets.
• Export options to Excel, CSV, PDF, and more.
• Multiple report pages to query across different indexes, documents, or datasets.
• Native Power BI authentication for secure, seamless access.

In short, it combines semantic intelligence with Power BI’s interactivity and security—all within a single analytics experience.

High-Impact Use Cases

Organizations can unlock immediate value with this approach in areas such as:

• Customer Service: Analyze complaint text for recurring issues like refunds, delays, or product defects.
• Compliance & Legal: Instantly surface contract clauses or policy excerpts in dashboards.
• Operations & IT: Search across incident logs and root cause analyses.
• HR & Internal Communications: Make employee policies and FAQs instantly discoverable.

All this—without building or maintaining another app that IT must support.

Why It Matters

This design delivers impact where it counts most:

• 💰 Cost Savings: Eliminate redundant web app hosting, authentication, and DevOps overhead.
• 🙌 User Adoption: Users already know Power BI—no new tools or training required.
• ⚡ Speed: What once took weeks of development can now be delivered in days.

By keeping it simple, this approach dramatically reduces time-to-value and improves accessibility.

Where This Approach Works Best

This solution is intentionally streamlined and optimized for clarity, speed, and integration. It excels when you need:

• A single query to retrieve relevant, contextual results.
• Concise summaries and highlights displayed directly in Power BI.
• Seamless blending of search results with existing metrics and dashboards.

It’s built for search + analytics, not conversational AI.

If your use case requires:

• Multi-turn or follow-up Q&A,
• Chat-style context retention, or
• Dynamic document uploads and reasoning,
  then a different, more interactive architecture would be a better fit.

Think of this solution as:

One search → One set of insights → Instantly displayed in Power BI

Fast. Clean. Insightful.

Your Next Step Toward Smarter Search

What’s exciting about this solution isn’t that it’s cutting-edge—it’s that it’s surprisingly simple. And in that simplicity lies its biggest advantage: faster delivery, lower cost, and easier adoption.

If you’re managing multiple priorities, this is the ideal lightweight, high-impact approach for proving value quickly. You don’t need a full-blown application—just a Power BI report and your existing Azure AI Search index.

You can have it running on your own data in days, not weeks.

Bring us your use case, and we’ll help you stand it up fast—through a proof of concept or full implementation.

Data resilience is the foundation of every modern analytics platform. In Microsoft Fabric, implementing automated backup, retention, and restoration strategies is essential to strengthen data protection and ensure operational continuity.

While Microsoft Fabric includes built-in disaster recovery (DR) capabilities, these are not intended to address everyday operational issues such as data refresh failures, accidental deletions, or data corruption. To bridge these gaps, an automated backup and retention approach becomes critical—helping organizations maintain seamless operations and data reliability.

Note: The methods described here are not replacements for Microsoft Fabric’s disaster recovery features. Instead, they serve as a complementary layer to enhance resilience, streamline restoration, and minimize downtime through automation and proactive configurations.

Setting Up Backup and Retention Policies

Microsoft Fabric’s Lakehouse and OneLake architectures provide robust foundations for data management. However, to ensure durability and compliance, automated backup and retention policies must be thoughtfully designed.

Key Components:

• Daily Incremental Backups: Capture daily snapshots to minimize data loss and maintain version history.
• Retention Policy Configuration: Define retention tiers—daily, weekly, monthly, and yearly—to balance compliance requirements and storage efficiency.
• Automation via Notebooks: Utilize Fabric notebooks or scheduled Spark jobs to automate backup creation, enforce retention policies, and clean up obsolete data.

Automation Highlights:

1. Backup Creation: Scheduled scripts trigger snapshot creation at defined intervals. For instance, Spark jobs leveraging APIs such as mssparkutils can efficiently copy and version datasets.
2. Retention Enforcement: Policy-based automation ensures outdated backups are automatically purged while preserving those required for compliance or audits.
3. Logging and Monitoring: Every backup, cleanup, and restoration task is logged for transparency, traceability, and audit-readiness.

Restoration: Recovering from Data Loss

Restoring data in Fabric can be performed at multiple levels—either the entire Lakehouse or specific tables—depending on the recovery need.

Restoration Best Practices:

• Restore data directly from automated snapshots or archived backups.
• Leverage structured logs to diagnose and resolve issues encountered during restoration.
• Minimize downtime by using predefined scripts or automation workflows that accelerate recovery and reduce manual intervention.

Why Automate Backup and Retention in Microsoft Fabric?

Automation introduces consistency, reliability, and efficiency—key pillars of a resilient data ecosystem.

Core Benefits:

• Data Integrity: Automated, incremental backups safeguard critical datasets against accidental loss or corruption.
• Operational Continuity: Rapid, scripted restorations minimize business disruption.
• Cost Optimization: Automated cleanup routines remove redundant backups, optimizing storage utilization.
• Scalability: Policy-driven automation scales effortlessly with expanding data volumes and evolving business needs.

Conclusion

Microsoft Fabric empowers organizations with a unified, intelligent data platform—but ensuring data durability and operational continuity requires more than built-in DR. By automating backup, retention, cleanup, and restoration processes, organizations can strengthen data resilience, minimize downtime, and maximize the business value of their Fabric investments.

In essence, automation transforms data protection from a reactive process into a proactive, scalable strategy—one that keeps pace with modern analytics demands and ensures the integrity of your most valuable asset: data.

Executive Summary

Retrieval-Augmented Generation (RAG) has quickly become one of the most glorified terms in enterprise AI. RAG is typically showcased over a handful of PDFs which seems to be easy and simple. But operating at enterprise scale—lakhs (hundreds of thousands) of records, low-latency retrieval, strict accuracy, and predictable costs—is hard. Real limits show up in four places: embeddings, vector indexes (e.g., Azure AI Search), retrieval/filters, and LLMs themselves. You’ll need hybrid architectures, careful schema/ops, observability, and strict cost controls to get beyond prototypes.

Embedding Challenges at Scale

Cost & volume. Embedding large datasets quickly runs into scale issues. Even a moderately sized corpus—each record carrying a few hundred to thousands of tokens—translates into tens of millions of tokens overall. The embedding phase alone can run into significant dollar costs per cycle, and this expense only grows as data is refreshed or re-processed.

Throughput & reliability. APIs enforce token and RPM limits. Production pipelines need:

• Token-aware dynamic batching
• Retry with backoff + resume checkpoints
• Audit logs for failed batches

Chunking trade-offs.

• Too fine → semantic context is lost
• Too coarse → token bloat + noisy matches Use header/paragraph-aware chunking with small overlaps; expect to tune per source type.

Domain relevance gaps. General-purpose embeddings miss subtle, domain-specific meaning (biomedical, legal, financial). Dimensionality isn’t the cure; domain representation is. Without specialization, recall will feel “lexical” rather than truly semantic.

Vector Databases: Strengths… and Constraints for RAG

Immutable schema. Once an index is created, fields can’t be changed. Adding a new filterable/tag field → recreate the index.

Full reloads. Schema tweaks or chunking updates often require re-embedding and re-indexing everything—expensive and time-consuming (parallelism will still hit API quotas).

Operational sprawl. Multiple teams/use cases → multiple indexes → fragmented pipelines and higher latency. Unlike a DB with views/joins, AI Search pushes you toward rigid, static definitions.

(Reference: Microsoft docs on Azure AI Search vector search and integrated vectorization.)

Retrieval & Filtering Limits

Shallow top-K. Even at top-K, relevant items can fall just outside the cut-off. In regulated domains, a single miss matters.

Context window pressure. As best practice we should send only required fields, then join externally on predicted Answers which will have key identifiers Like ID to finish the answer. (In our Scenario we sent 1-2 columns having Id, descriptions out of 25 columns in a table)

Filter logic ceiling. Basic metadata filters work, but you’ll miss nested conditions, dynamic role-based filters, and cross-field joins that are trivial in SQL.

LLM Limitations You Will Hit

Input limits. Even with large-context models (450k+ tokens),However, even within this boundary, we observed that certain records — particularly those in lower relevance ranges may be implicitly skipped during reasoning .This behaviour is non-deterministicand poses serious challenges for enterprise-grade tasks where every data point is critical

Output limits.Many models cap useful output (e.g., 2k–16k tokens). This affects multi-record responses, structured summaries, and complex decision-making outputs — leading to truncated or incomplete responses, particularly when returning JSON, tables, or lists.

Latency & variance. Complex prompts over hundreds of records can take minutes. Stochastic ranking creates run-to-run differences—tough for enterprise SLAs.

Concurrency & quotas. Enterprises face quota exhaustion due to shared token pools, Concurrent usage by multiple users or batch agents can quickly consume limits Smaller organizations with access to 8K input / 2K outputtoken models (e.g., LLaMA, Mistral) face even tighter ceilings — making RAG challenging beyond pilot projects

Productionisation, Observability — and Evaluation That Actually Matters

While most RAG demos end at a “right-looking” answer, real deployments must be observable, traceable, resilient, and continuously evaluated. GenAI pipelines often underinvest in these layers, especially with multiple async retrievals and LLM hops.

Operational pain points

• Sparse/unstructured LLM logs → hard to reproduce issues or inspect reasoning paths.
• Thin vector/AI Search telemetry → silent filter failures or low-recall cases go unnoticed.
• Latency tracing across hybrids (SQL + vector + LLM) is messy without end-to-end spans.
• Failure isolation is non-trivial: embed vs ranker vs LLM vs truncation?

What good observability looks like

• End-to-end tracing (embed → index → retrieve → rerank → prompt → output).
• Structured logs for: retrieval sets & scores, prompt/response token usage, cost, latency, confidence, and final joins.
• Quality gates and alerts on recall@K, latency budgets, cost per query, and hallucination/citation signals.

Evaluation: beyond generic metrics

• Partner with domain experts to define what “good” means. Automatic scores alone aren’t enough in regulated or domain-heavy settings.
• Build a domain ground-truth set (gold + “acceptable variants”) curated by SMEs; refresh it quarterly.
• Establish a human-in-the-loop loop: double-blind SME review on sampled traffic; escalate low-confidence or low-evidence answers by policy.
• Maintain an error taxonomy (missed retrieval, wrong join, truncation, unsupported query, hallucination) with severity labels; track trends over time.
• Run canaries/A-B tests in prod; compare quality, latency, cost, and SME acceptance before full rollout.
• Log evaluation metadata (query id, versioned index, chunking config, model/runtime version) so you can pinpoint regressions.

In practice (our pattern)

• We co-defined a gold set with SMEs and require evidence-backed answers; low-evidence responses are auto-routed to review.
• We track recall@K + citation coverage for retrieval, and field-level precision/recall for extraction tasks, alongside cost/latency dashboards.

Bottom line Without observability + domain-grounded evaluation, production RAG stays brittle and opaque. With them, you get a system you can debug, trust, and scale—not just a demo that looks good once.

Conclusion: Beyond the Buzzwords

While Retrieval-Augmented Generation (RAG) has gained mainstream attention as the future of enterprise AI, real-world adoption reveals a wide gap between expectation and execution. From embedding inconsistencies and rigid vector schema constraints to LLM context bottlenecks and high operational latency, the challenges compound rapidly at production scale. Even in enterprise environments with access to high-token models, limitations around completeness, determinism, and runtime stability remain unresolved.

This doesn’t mean RAG is fundamentally flawed—it’s a powerful paradigm when paired with the right retrieval tuning, agent orchestration, hybrid pipelines, and system-level observability. But as engineers and architects, it’s time we shift the conversation from aspirational posts to grounded, production-aware designs. Until embedding models evolve to be truly domain-specific, vector systems allow dynamic schemas, and LLMs deliver predictable performance at scale, RAG should be treated not as a plug-and-play solution—but as a custom-engineered pipeline with domain, data, and budget constraints at its core

This blog represents the collective strength of our AI team—where collaboration, innovation, and expertise come together to create meaningful insights. It is a testament to the value SNP delivers through its AI practice, showcasing how we help our customers turn possibilities into impact.