Audit
We map your most expensive queries, your LookML, and your ad-hoc backlog. Read-only. Output is one shared doc.
Week 1
Data & Analytics · Looker + BigQuery
Your BigQuery is expensive and slow — fixable in 6 to 8 weeks.
We've been the team drowning in ad-hoc report requests — so we'll be straight with you. If you run on Google BigQuery and Looker, we cut that cost 30–60%, measured on your own bill, not a slide. Then AI takes the repetitive reporting off your team. No warehouse migration, no tool changes.
−30 to 60% 6 to 8 weeks measured on your billing
The health check is read-only. You install nothing, you migrate nothing, and the data is yours.
Typical reduction — your exact range comes from the audit, measured on your billing.
The tension · 01
Three questions you'd rather not answer in the next board review.
- 1 Finance asked why the cloud bill grew 70% this year. Do you have a clean answer?
- 2 Your dashboards take 8+ seconds to load. Do you know which queries are doing it?
- 3 Half your Explores haven't been opened in 90 days. Are you still paying for them?
If any of these landed, the cost is hiding in the same three places it always does.
The method · 02
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Four steps. Six to eight weeks. No migration, no rebuild.
Refactor
Partitioning and clustering on heavy tables, LookML moved to derived tables with persistence, n+1 queries killed, scheduled jobs with no viewer turned off.
Weeks 2–6Measure
We compare against your baseline billing. The cost reduction goes in the SOW as a committed number — not an estimate.
Weeks 6–8Reporting agent
An agent takes the ad-hoc reporting queue. Your team answers questions in Slack, not in a dashboard request backlog.
HandoffWhat we don't do first: migrate your warehouse. Your CIO doesn't fight us.
The reporting agent isn't the only one.
Step 04 is the agent your business users see. Behind it, the same fleet works on the delivery itself — the mechanical half of a data team's week, run continuously instead of whenever someone has an afternoon free.
LookML pull request review
Reads every PR against your conventions and posts what it found — including which dashboards and Explores break if that field changes. It comments; it doesn't merge. How it works on LookML →
Data validation
Compares row counts and aggregates before and after a refactor or a migration, figure by figure, with both queries attached. Full inventory, not a sample. How it works on a migration →
Dashboard validation
After a change ships, renders the affected dashboards and checks that they load, that filters apply, and that the numbers still match the previous run.
Documentation
Field descriptions and model maps generated from the LookML itself and regenerated on every merge, instead of a wiki that starts aging the day it's written.
Ticket analysis
Reads the "this is slow" and "I need a report" queue, matches each request to the query or Explore behind it, and groups them by root cause — including the ones a dashboard already answers. How it works on performance →
Cost before the merge
Dry-runs the query on the pull request and posts what it will scan at production volume. A missing partition filter becomes a review comment, not a line item next month. How it works on cost →
Analyst onboarding
Answers which Explore holds which metric and why, so a new hire ramps up without interrupting the one person who knows the model.
Agents read, measure, and report. Approving, merging, and deciding stay with your team: the point is to take the mechanical work off your most expensive people, not to take the judgment out of the loop.
The proof · 03
Mid-market SaaS · US
B2B SaaS · US
Two stacks. Two numbers. Both measured on the client's billing.
Measurable cost reduction in 6 weeks
BigQuery optimization across a 6-week cycle — query refactor, LookML rework, FinOps.
Figures under NDA, shared on the call.
What we didn't do: no warehouse migration, no LookML rebuild on the client's side.
Agentic reporting live in the pipeline
Agentic ingestion and reporting running inside their pipeline — the reporting agent in production.
Details under NDA.
What we didn't do: no rip-and-replace of the existing pipeline.
Numbers shared under NDA on the next call. We'd rather show you the billing than quote a slide.
The audit · 04
Half your Explores are never opened.
You're paying for dead weight — and nobody gets an alert. Your senior engineers are doing FinOps by hand when they should be building product. A single misconfigured Explore can cost thousands a month in BigQuery, silently.
30–60% of your bill, found in ~15 minutes — read-only
15-minute read-only check. The dead weight, surfaced before finance asks.
| The manual status quo | The Health Check |
|---|---|
| Senior engineers auditing LookML by hand | 15 minutes, read-only, no engineer time |
| One bad query, a costly surprise, zero alert | We find it before finance does |
| Half your Explores billed, never opened | We name the dead weight |
15 minutes. Read-only access. Zero commitment. No findings, no charge.
Prefer to self-assess first? Use our BI Infrastructure Audit Checklist.
Read-only, scoped to the minimum: we read to analyze, we don't touch anything. We pull only the data the work needs, never a full dump, and we never use it to train models.
How it works · 05
Transparent pricing. The scope is defined by your stack.
Free Looker Health Check
15 minutes, read-only. We name the recoverable cost in your BigQuery billing. If we find nothing, we tell you — and you owe us nothing.
Scoped audit · fixed scope, 1 week
5 business days. One shared document and a 90-minute session. A complete map of your most expensive queries and Explores, with concrete action items.
Fractional data engineering · monthly retainer
Ongoing optimization plus a reporting agent on top. Measured monthly against your actual BigQuery billing — not a generic benchmark.
"Why not hire an in-house AI/data team?"
A serious in-house team is a heavy monthly cost and takes 6–9 months to be productive. We get in within 2 weeks; the first result is on your dashboard in 6–8. The infrastructure we leave is yours, documented, transferable. We accelerate your hire — we don't compete with it.
"How long until I see results?"
6 to 8 weeks to the first cut measured on your BigQuery bill. If it's not on your dashboard by week 8, we don't bill the next phase. That goes in the SOW.
"Isn't this just a big consultancy?"
They start in the high six figures and move in quarters. We start small and deliver the first measured result in 6–8 weeks. They send 40 people; we send 3 partners running a documented agent fleet.
"Is it safe to give you access to my stack?"
The Health Check is read-only on your Looker and your BigQuery: we read to analyze, we don't touch anything. After that, the data engineering and the reporting we build run on our platform, on Google Cloud — you install nothing and migrate nothing. We pull only the data the work needs, never a full dump; everything travels and is stored encrypted, every operation is logged, and nothing is used to train models. The data is yours: when the contract ends we hand it over and access is revoked. The standard we built it to is the same one Martín worked under at Stitch Fix, MadHive, Human Cybersecurity, XPO, and DealerOn — companies where our founder worked, not RavencoreX clients.
Larger engagements are scoped through the SOW. We talk for 30 minutes, understand your stack, and tell you what makes sense for your case.
Book a 30-min demo
FAQ · 06
What prospects usually ask before the first call.
How much does Looker consulting cost?
It depends on scope. The entry point is free: a 15-minute read-only health check of your Looker and BigQuery setup — if we find no optimization opportunities, there's no charge. From there you get a fixed-scope proposal sized to your data volume, LookML complexity, and the number of Explores involved. Book the free check and we'll scope it — you'll know the cost before any work starts.
How long does BigQuery optimization take to show results?
First measurable results appear in 6 to 8 weeks. That number goes in the SOW as a commitment, not an estimate: if the cost reduction is not on your dashboard by week 8, we do not bill the next phase. Quick wins — misconfigured queries, unused Explores — are typically visible from week 2.
What does a free Looker health check include?
The health check takes 15 minutes with read-only access to your Looker and BigQuery instance. We identify Explores unused in the last 90 days, disproportionately expensive queries, slow dashboards, and scheduled jobs consuming without generating value. The output is a concrete number: the estimated recoverable cost in your monthly BigQuery bill. No findings, no charge.
Is it safe to give an external consultant access to my stack?
For the health check we ask for read-only access to your Looker and BigQuery, scoped to the minimum necessary, with an individual permission per component: we read to analyze, we don't touch anything. Whatever gets changed afterwards goes through your change management process and your approval. The data engineering and the reporting we build run on our platform, on Google Cloud, so you install nothing and migrate nothing. We pull only the data the agreed work needs, never a full dump, and we never use it to train models. Every operation is logged. And the data is yours: if the contract ends, we hand it over and access is revoked.
What is the difference between a data retainer and hiring an in-house team?
A serious in-house team takes 6 to 9 months to become productive and carries high fixed monthly costs including salaries, benefits, and management overhead. With a fractional retainer we are operational in 2 weeks and the first measured result is on your dashboard in 6 to 8. The infrastructure and documentation we deliver are yours: they accelerate your future hiring instead of competing with it.
What do I need in Looker for consulting to make sense?
Having a Looker instance connected to BigQuery is enough to get started. The most common opportunities appear when the monthly BigQuery bill exceeds $3,000 USD, when dashboards take more than 5 seconds to load, or when the team has more than 20 Explores configured. The LookML does not need to be in good shape — in general, the messier it is, the higher the potential savings.
How much can a BigQuery audit reduce your bill?
The typical range is a 30 to 60 percent reduction in the monthly bill, measured on the client's actual billing before and after the work. The exact number depends on the current state of the stack: unused Explores, unpartitioned queries, scheduled jobs with no viewers, and unclustered tables are the four patterns that account for most of the waste. Your specific range comes from the free health check.
Does RavencoreX only work with large companies?
No. We work with companies ranging from 20 to 10,000 employees with a BigQuery bill that justifies the investment — typically starting at $3,000 USD per month. The entry point is the free health check: if the savings potential does not clearly exceed the cost of the work, we tell you on the first call. It makes no sense for either of us to move forward if the numbers do not close.
Let's talk · 08
Read-only access. Your number in 15 minutes.
No pitch. We point read-only access at your billing and tell you what's recoverable. If there's nothing there, we tell you that too — and you owe us nothing. It's read-only access: we read to analyze, we don't touch anything.
Specialized services · 07
Go deeper: four ways we work on Looker and BigQuery.
LookML development and refactoring, embedded analytics, dashboards, and team training — knowledge your team keeps.
Performance optimization for Looker and the BigQuery bill behind it — 40%+ faster dashboards in our founder's work before RavencoreX.
Partitioning, slot strategy, query tuning, and FinOps monitoring — measured on your own billing.
Migrate to Looker from Tableau or Power BI
Zero-downtime BI migrations: parallel run, validated data, trained team, fallback plan.