Is this for you?
- Users complain that search "just doesn't find things."
- You run an audio, music, or media platform with messy catalog data.
- Ingestion is a pile of cron jobs nobody fully trusts.
- Relevance was tuned once years ago and nobody remembers by whom.
- You're about to bolt on LLM or RAG features and want retrieval solid first.
- Downstream teams routinely complain about data quality or search results.
Content Ingestion Pipeline Audit
Deep review of how content gets from source to searchable, for platforms where assets and metadata are a mess.
What you get
- Current-state architecture diagram.
- Data quality assessment with specific defect categories and frequency.
- Normalization and deduplication strategy recommendation.
- Observability recommendations — what to monitor, where to alert.
- Prioritized remediation plan with effort estimates.
- 60-minute walkthrough.
Good fit: audio/music/media platforms with messy catalog data, ingestion pipelines that are a pile of cron jobs nobody fully trusts, or downstream teams (search, recommendations, billing) complaining about data quality.
Elasticsearch Relevance Audit
A written audit of why your Elasticsearch results are wrong and a prioritized plan to fix them.
What you get
- Written report (15–25 pages) with findings grouped by severity.
- Annotated mapping and analyzer review.
- Query review with 10–20 representative queries, scored and diagnosed.
- Prioritized remediation roadmap with effort estimates.
- 60-minute walkthrough with your engineering team.
Good fit: users complain search "just doesn't find things"; relevance was tuned once and nobody remembers by whom; planning to add LLM/RAG features and want retrieval solid first. OpenSearch is supported.
Scope
What's in
- Content ingestion architecture and data-quality assessment
- Normalization, deduplication, and observability design
- Elasticsearch/OpenSearch mapping and analyzer review
- Query structure and relevance scoring review
- Prioritized remediation roadmap for either or both
What's out
- Implementing the remediation (separate engagement)
- Rights and licensing logic beyond what affects ingestion
- Cluster infrastructure tuning and ES version upgrades
- Hybrid search / vector integration beyond scoping
- Full platform rearchitecture
Process
Intake
Day 1Kickoff, access to repo, sample data, pipeline dashboards or index settings, sample queries, intro to the team.
Discovery
Days 2–7Pipeline trace or mapping/analyzer audit, sample data analysis, representative-query generation with stakeholders, scoring baseline.
Analysis
Days 8–12Remediation design, observability planning, fix design, ranking experiments where possible.
Report
Days 13–17Draft, review, finalize.
Walkthrough
End of engagement60-minute call with your team.
Pricing
End-to-end search & discovery: ingestion pipeline + Elasticsearch relevance together, vs. €18,000 if purchased separately.
50% to start, 50% on report delivery. Includes one 30-minute follow-up call within 30 days of delivery. End-to-end bundle invoiced as one engagement.
One fixed price. No surprises, no “starting at” language. If we agree on scope and you pay the deposit, the engagement is locked in.
Questions
Which scope should I pick?
If content is getting into the system reliably but results are bad, take the Relevance scope. If ingestion itself is unreliable (duplicates, missing metadata, broken normalization), take the Ingestion scope. If both are in doubt, the end-to-end bundle tells the full "content enters the system → content is found by users" story — closer to how buyers think about the problem than the technology-centric split.
Do you work on OpenSearch?
Yes.
Do you work on non-audio platforms?
Yes — the patterns translate. Audio and music are where my deepest experience is; the methodology is the same.
Do you need production access?
No. Repo read access, sample data (sanitized is fine), and a representative environment are enough.
Do you implement the fixes?
Separately, yes.
Will you sign an NDA?
Yes.
About
I've built and scaled content ingestion and Elasticsearch-based discovery for audio and content platforms — catalog ingestion, normalization, rights-aware delivery, relevance tuning. The audit is domain-specific, not generic data-pipeline or config-checklist advice.
More about Paper Scissors & GlueReady to start?
Book an intro call. If we're not a fit, I'll tell you on the call.