Analytics Engineering · White-Label Subcontracting · Boutique Digital Agencies



Your client just replied.

"Quick question, why does Column D show 40% nulls this week when it was clean last month?"


You need the error caught BEFORE it reaches them NOT after.


The Embedded Analytics Model injects analytics engineering capacity directly into your delivery workflow. Fixed scope. Fixed price. White-labeled output. Your clients never know we exist.

3 days → 11 min
Client reporting pipeline rebuilt, now runs unattended, every week
~20 hrs recovered
Per sprint returned to billable scope after manual QA was eliminated
0 incidents
Data-quality production incidents across 9 validated ML models post-deployment
See services & pricing


Book a 15-Minute Capacity Check →
No channel conflict, ever. Clearline does not maintain a client acquisition book and does not pitch your clients directly. Every engagement is structured to be invisible to your end client, by design, not just policy.

The cost of getting it wrong

The most expensive data error isn't the one that crashes the pipeline. It's the one that reaches the client looking completely clean.

Manual QA has a ceiling. Schema drift, type coercions, and statistical outliers buried in 50,000 rows are above it. These aren't errors your team missed, they're structurally invisible to human review under deadline pressure.

When an error reaches a client, you're no longer managing a data problem. You're managing a relationship problem. Those don't resolve in the same conversation.

What it actually costs your agency

  • ⏱
    2-4 hours of unplanned rework per error that reaches a client, and that's before the explanation email
  • 📊
    Manual QA running 20+ hours per sprint across two analysts who get different results from the same check
  • 🔁
    Delivery capacity tied up in QA that could be scoped, billed, and shipped as client-facing work
  • 📋
    3-6 months and $85K-$120K/year to hire an analytics engineer in-house, if the market delivers at all

Is this a fit?

Clearline works for a specific type of agency

Right fit

  • Boutique digital agency, 2-20 person team
  • Active data delivery work but no dedicated analytics engineer in-house
  • Client sprint expanded faster than your bench can absorb
  • You need white-labeled output, clients see your agency's work
  • You want fixed scope and fixed price, not open-ended hourly
  • You're turning down or under-delivering data work right now

Not the right fit

  • Enterprise companies with existing internal data teams
  • Projects requiring on-site presence or real-time pair programming
  • Scope that changes weekly with no defined deliverable
  • You want someone to own strategy, not execution

How it works

From call to delivery in days, not months

1
15-minute capacity check
You describe the engagement, source systems, deliverable, timeline. I come to the call prepared from your intake answers. No pitch, no discovery theater. If there's a fit, I scope it on the call. If there isn't, I'll tell you that directly and point you toward what will actually help.
2
Fixed-scope proposal within 24 hours
You receive a written scope document: deliverable, timeline, price, and exactly what I need from you to start. No hourly ambiguity. No change orders unless you change the scope. You decide yes or no on a single number.
3
Delivery under your brand
Every file, PDF, dashboard, and repo arrives without Clearline branding. File naming conventions, folder structure, and documentation standards are configured to match your agency's output. Your client sees your work, because that's what it is.

Services & Pricing

Fixed scope. Fixed price. No sourcing overhead.

Most new agency partners start with the Analytics Audit, low risk, fast result, immediate signal on whether this is a fit.

Offer Price Turnaround
Data Validation Kit
Production-ready Python scripts: schema drift detection, null flagging, row-count reconciliation, audit log. Catches 14 categories of structural errors. White-labeled PDF output, ready to share with your client.
Get it instantly → 		$97
vs. 40+ hrs to build equivalent from scratch
 Instant download
Analytics Audit Start here
Complete data infrastructure health review: top 5 structural vulnerabilities, 3 actionable schema fixes, before/after pipeline visualizations. White-labeled PDF, ready to pass to your client. Most common entry point for new agency partners.
 $1,500 fixed
vs. $2,500-$4,500 via boutique consultancy
 1 week
Data Sprint
Production pipeline, dashboard, or Streamlit app. Scoped to a defined deliverable, delivered under your brand. Includes documentation and transition session for your internal team.
 From $5,000 fixed 2 weeks
Monthly Retainer Most popular
10-15 hrs/month async analytics engineering support. 24-48 hr response. Ongoing infrastructure, monitoring, pipeline maintenance, or delivery capacity. Includes monthly 60-minute strategy call and full white-label delivery standards pack, file naming, folder structure, GitHub README templates, PDF report templates.
⚠ Limited to 3 active clients at any time.
 From $1,500/mo
vs. $8,400-$11,000/mo
for an in-house analytics engineer (fully loaded)
 Ongoing
White-Label Subcontracting
Direct injection into your delivery pod. Senior analytics engineering capacity without the sourcing overhead. Your client never knows Clearline exists. Best for agencies with active delivery pipelines and recurring data work.
 $95-$110/hr On-demand
Book a 15-Minute Capacity Check →

Results

What this looks like in practice

Retail reporting pipeline
3 days → 11 minutes.
Three disconnected sources. Weekly client report. Two people, three full days to compile. Validation scripts caught 14 categories of structural errors, schema drift, null violations, row-count mismatches, before they touched the report. Unified pipeline built in six days. Now runs unattended every week. Agency shipped their first clean client dashboard that same cycle.
Release cycle QA elimination
~20 hours back per sprint.
Analytics team spending 20+ hours per sprint on manual QA. The process was undocumented, inconsistent between team members. Two analysts running the same check got different results. Automated validation layer inserted upstream: single-command execution, standardized thresholds, results logged to a white-labeled audit PDF. Error rate on outbound deliverables dropped to zero. Those hours moved to billable scope.
Enterprise ML environment
9 models. Zero production incidents.
Multi-model ML environment with no shared validation layer, each model had ad-hoc QA built by the engineer who wrote it. Diagnosing model error vs. data error was costing days. Validation architecture built independently of any model: schema checks, input distribution monitoring, row-count reconciliation between feature pipelines and model inputs. Nine models validated. Two in production. Zero data-quality incidents post-deployment.

Why Clearline exists

I know what it costs when an error is discovered after you're out of options.

"Catch the error while it's still in your hands. Once it reaches a reviewer, a client, or a downstream pipeline, you're explaining something you can't fix."

I was a Change Coordinator at Bank of America. Change requests were the authorization layer for anything touching production, and the QA system we used didn't flag what was wrong until after submission, when it was too late to fix it. Rules changed without notice. Every failed submission was visible to the entire team.

Then came a one-day emergency request. I sent it for review. The review didn't come back in time. I submitted without it. If it had failed, I couldn't have changed a thing. It passed. But I got lucky, and luck isn't a process.

That's when I stopped treating this as a carelessness problem. The problem was a process where errors were only visible after submission, in someone else's hands, when it was already too late. So with a few colleagues, I built a tool that ran the check before the request left our hands. It cut roughly 20 hours of rework per release cycle. We stopped submitting blind.

That principle is what Clearline is built on. The same rigor that goes into high-stakes enterprise pipelines, where errors have regulatory consequences, not just client emails, goes into every deliverable that leaves under your agency's brand.

J
Jacobyne Tambe
Founder, Clearline Data Analytics · Former Change Coordinator, Bank of America

The guarantee

🔒

If the Analytics Audit doesn't deliver three actionable improvements, you pay nothing.

If the Analytics Audit doesn't identify at least three specific data infrastructure improvements you can act on immediately, I'll refund the full $1,500. No questions, no conditions, no process. The audit either pays for itself, in clarity, in confidence, in a white-labeled PDF you can pass directly to your client, or it doesn't cost you anything.

Common questions

What agency owners ask before booking

Will my clients find out you exist?
No. Every deliverable, files, PDFs, dashboards, GitHub repos, arrives without Clearline branding. File naming conventions, folder structures, and documentation standards are configured to match your agency's output. Your client sees your work. That's the whole product, not a policy, a delivery architecture.
Will my internal team trust work built externally?
Every deliverable includes full documentation: handoff SOPs, GitHub READMEs, and a 12-point quality evaluation checklist your team can use to review work confidently, even without deep data expertise. Clearline's documentation standards are typically more thorough than in-house-built pipelines, because they have to be. External work earns trust through specificity, not familiarity.
Why not just hire a data analyst in-house?
Hiring a mid-level analytics engineer takes 3-6 months and costs $85K-$120K/year fully loaded, salary, benefits, tools, recruiting fees, ramp time, management overhead. Clearline gives you equivalent mid-to-senior capability in 48 hours for $1,500/month. The question isn't whether you can afford $1,500/month. It's whether you can afford the next two months of turning down or under-delivering data work while you wait for a hire that takes 3-4 months. If you eventually want to hire in-house, use that time to understand exactly what you need. That clarity is worth something too.
What does the 15-minute capacity check actually involve?
You fill out four intake questions when you book, agency type, project type, timeline urgency, how you heard about Clearline. I review your answers before we speak and come prepared with specific questions. No pitch. No capability overview. If there's a fit I scope it on the call. If there isn't, I'll tell you that directly.
What if the scope changes mid-project?
Fixed-scope engagements stay fixed. If the scope turns out to be materially different from what was agreed, we pause, re-scope in writing, and you decide whether to proceed at the new number. No silent scope creep. No surprise invoices.

Ready?

You don't have to wait for a hire that takes six months.
You need the data work done now.

15 minutes. Tell me about the engagement. I'll come prepared and tell you if there's a fit.

Book a Capacity Check →

Retainer slots are limited to 3 active clients. Currently accepting introductory calls.