Operations
Close the Books
Ingest your accounting data, reconcile transactions across systems, flag anomalies, produce an investor-ready monthly close on day 5.
Replaces · outside controller / fractional CFO close ops
We don't sell software.
We replace your data consultancy.
Cloudbricks is an AI-native services firm built on an Enterprise Databricks foundation. Swap your offshore data team, your BI implementation partner, your fractional analytics shop — for a flat monthly outcome with an SLA. Vendor swap, not reorg.
The thesis
For every $1 spent on data tools, six are spent on the people who make them useful.
A company spends $10K a year on a BI seat and $120K on the analyst who actually answers the question. Spends $50K a year on a data warehouse and $500K on the engineers who keep it from drifting. Spends a fortune on Databricks credits and a larger fortune on the consultancy that turns those credits into a working pipeline.
The work budget dwarfs the tool budget. The next great data companies won't sell another seat. They'll just close the books. Score the leads. Stand up the warehouse. Ship the model. The customer is buying the outcome, and every improvement in the underlying model makes that service faster, cheaper, and harder to compete with.
Cloudbricks is built around that bet. We hold the Enterprise Databricks license. We run the infrastructure. We do the data engineering, ML work, and operational judgement. You get the deliverable.
What we replace
We're a vendor swap. The budget already exists.
We're not asking your CFO to fund a new line item or your CTO to greenlight a hire. We're substituting the invoices you already sign — the ones for outcomes that should have been productized years ago.
Today you're paying for
Cloudbricks delivers
A Databricks SI partner at $250/hr→A flat monthly fee, work included
An offshore data-engineering shop→Pipelines we build, run, and own
A BI implementation consultancy→Dashboards delivered on a cadence
An ML consulting engagement→A productionized, monitored model endpoint
A fractional CFO's monthly close→An investor-ready close on day 5
A staff data scientist req you can't fill→The deliverable that req was meant to produce
An offshore BPO running a back-office queue→A multi-agent system clearing the queue under your audit log
Replacing an outsourced contract is a vendor swap. Replacing headcount is a reorg. We start with the swap.
"A copilot sells the tool. An autopilot sells the work. The work budget in any profession dwarfs the tool budget."
— Sequoia Capital, Services: The New Software
How the work splits
AI does the intelligence. We do the judgement.
Modern models cleared the bar for intelligence work — code, reports, forecasts, summaries. What they still can't do is decide which metric matters, when to ship, and what to tell the board. That's the half we take responsibility for.
Intelligence — AI
- Writing the SQL, Spark, dbt
- Drafting the weekly report
- Producing the forecast
- Summarizing the customer ticket
- Coding the integration
- Running the model evaluation
Judgement — Cloudbricks
- Choosing what metric to track
- Knowing what to flag to the CFO
- Deciding when the model is good enough
- Trading off accuracy vs latency vs cost
- Owning the SLA when something breaks
- Telling you when to stop a project
What we deliver
Outcomes, packaged as a subscription.
Each service is scoped to a clean deliverable, run on our Databricks Enterprise workspace, and priced flat per month. Each one substitutes a budget line you're already paying.
Revenue
Lead Scoring & Routing
Unify CRM, product usage, and enrichment data; train a scoring model on your closed/won history; feed prioritized leads back into your sales tools every hour.
Replaces · RevOps consultancy + part-time data scientist
Forecasting
Demand & Inventory Forecasting
Continuously retrained forecasts across SKUs, channels, and regions, delivered as dashboards plus an API your ops team can plug into. Drift monitoring and quarterly model reviews included.
Replaces · planning consultancy + forecast analyst seat
ML productionization
Model-to-Production
You have a notebook that works. We turn it into a versioned, monitored, retraining production endpoint on Mosaic AI Model Serving — feature store, MLflow registry, on-call.
Replaces · ML consulting engagement + MLOps hire
Data platform
Lakehouse-as-a-Service
Bronze/silver/gold Delta Lake, Unity Catalog governance, source connectors, SQL endpoints — stood up in your account or ours, and operated by us. You query; we keep the lights on.
Replaces · Databricks SI partner + offshore data engineering
AI agents
Knowledge Agent
A retrieval-augmented assistant trained on your documents, tickets, and runbooks — deployed behind your SSO. We curate the corpus, run evals, ship weekly improvements.
Replaces · KM consultancy + GenAI pilot vendor
Agent operations
Agent-Run Workflow
Pick a back-office queue you currently outsource — ticket triage, invoice coding, vendor onboarding, claims review — and we run it as a multi-agent system with identity passthrough into your CRM, ERP, and ticketing. Every action is logged, scoped to the right user's permissions, and reversible. Humans stay on the judgement calls; the queue clears on SLA.
Replaces · offshore BPO contract + workflow automation vendor
Custom LLM
Fine-Tuned Model
Domain-specific fine-tunes of open-weights models (Llama, Mistral, DBRX) on your proprietary data, served from a private endpoint with versioning, guardrails, and a quarterly retrain cadence.
Replaces · ML R&D contract + GPU vendor commitment
Anything else
Bring us the outcome.
If it's a data or ML deliverable, we'll scope it as a flat-fee service. Tell us what "done" looks like and we'll quote it back as a monthly number with an SLA attached.
Replaces · whatever invoice you're already signing
The found-money wedge
The work you've been deferring is suddenly economical.
Every company has a backlog of analytics asks that never get staffed. The 19 dashboards no one built. The churn model the team keeps "getting to next quarter." The reconciliation that gets eaten by manual cleanup every month-end. The vendor-spend audit that would pay for itself ten times over but doesn't justify a hire.
That work isn't insourced and it isn't outsourced. It's abandoned — left on the floor because no single one of these tasks justifies the human cost of taking it on.
Flat-fee outcomes change the math. A $3,900/month subscription makes a previously-uneconomical project economical. The contract leakage in your procurement, the upsell signal hiding in your product telemetry, the forecast you've been running in a spreadsheet — these stop being "next year" line items and start being deliverables.
Found money. No incumbent to displace. No reorg to negotiate.
The opportunity map
Where intelligence-heavy work is already outsourced.
We focus where the budget already exists, the scope is well-defined, and the work is mostly intelligence — the surfaces where AI can compound fastest. We start at the wedge and expand into the deeper judgement-heavy work as the data flywheel turns.
Vertical
Outsourced TAM
Wedge work
Cloudbricks status
Accounting / close ops
$50–80B
Reconciliation, monthly close, anomaly review
Live
Revenue ops
$30–50B
Lead scoring, attribution, pipeline forecasting
Live
Demand & supply planning
$40–60B
SKU forecasting, inventory optimization
Live
Healthcare RCM
$50–80B
Coding, claims scrubbing, denial prediction
2026 H2
Insurance brokerage
$140–200B
Quote shopping, form-fill, renewal triage
2026 H2
Procurement long-tail
$200B+ (leakage)
Spend audit, contract abstraction, vendor scoring
2026 H2
IT managed services
$100B+
Patch & alert triage, ticket auto-resolution
Roadmap
Tax advisory (multi-juris)
$30–35B
Cross-border filings, compliance review
Roadmap
Legal transactional
$20–25B
NDA, contract drafting, regulatory filings
Roadmap
The outsourced surface is the wedge. The insourced spend behind it is the long-term TAM. Start where the budget exists; expand as the data flywheel compounds.
How it works
Four steps from "we should do something with our data" to a working service.
-
01
Scope the outcome
A 45-minute call to define the deliverable, the inputs we'll need, the cadence, and what success looks like. You leave with a one-page SOW and a flat monthly price.
-
02
We build on our Databricks
We provision a workspace under our Enterprise license, build the pipelines and models, and integrate with your source systems. You don't see a Databricks bill — ever.
-
03
You receive the deliverable
A report, an API, a dashboard, a forecast, a model endpoint — whatever the contract says. Delivered on the cadence we agreed, monitored 24/7, with humans on call.
-
04
The service compounds
Every month the model gets more of your data, judgement gets codified into rules, and the same flat fee buys a sharper outcome. You don't pay more as we get better.
Why this works
Three structural advantages, one promise.
Enterprise license, zero software markup
We hold a full Enterprise Databricks license at no software cost. That entire margin layer disappears from your invoice — you only ever pay for the outcome and the underlying compute, both bundled into one flat fee.
Pure-play autopilot from day one
Every Databricks consultancy that wants to pivot to autopilot has to explain it to its existing billable-hours customers. We don't. No installed copilot base to cannibalize, no innovator's dilemma — just the outcome.
Outcome-priced, not hourly
You don't buy a workspace, a seat license, or a bag of consulting hours. You buy a deliverable with an SLA. If we get faster, you get a sharper outcome — not a smaller invoice (and not a bigger one either).
Pricing
Flat monthly. One invoice. No surprises.
Every engagement starts with a one-page SOW and a single monthly number. Add services, drop services, swap services — pricing recalculates. No DBU bills, no compute reconciliations, no per-seat math.
Single Service
from$3,900/month
- One outcome, scoped and SLA'd
- Databricks Enterprise included
- Monthly review with the team
- Email + Slack support
Most teams start here
Bundle
from$11,900/month
- Three services, one contract
- Shared lakehouse across services
- Weekly review + roadmap
- Dedicated lead engineer
- On-call coverage
Fractional Data Org
from$24,900/month
- We are your data team
- Unlimited service scope changes
- Embedded with your leadership
- 24/7 on-call
- Quarterly board-ready review
Compute beyond the bundled allowance is billed at our wholesale AWS rate with no markup. Most engagements never touch the cap.
FAQ
The questions you're already asking.
How is this different from hiring a Databricks consultancy?
Consultancies sell hours. We sell deliverables. A consultant gives you a workspace and a slide deck; we hand you a closed monthly book, a working forecast, or a deployed model — and we keep running it. The economics flip from "billable utilization" to "did the outcome land."
Why don't you just resell Databricks?
Because the work budget is six times the tool budget. Reselling software is a margin-on-margin game; doing the work is a margin-on-labor game where AI keeps making our labor cheaper. We'd rather be on the right side of that curve.
What about copilots — aren't they doing the same thing?
Copilots sell tools to professionals; autopilots sell outcomes directly to buyers. Most copilots can't easily flip to autopilot because their existing customers are the very professionals being automated away. We don't have that constraint — we're autopilot from day one.
Do we need our own Databricks account?
No. By default, services run inside our Enterprise workspace and we deliver outputs to your systems. If you have data residency or compliance reasons to keep everything in-tenant, we can deploy into your account at no extra software cost — you'd just pay your own AWS compute.
Is this a vendor swap or do we need new budget?
Almost always a swap. Most prospects engage by retiring an existing consultancy retainer, an offshore engagement, or an analytics ask that's been deferred for quarters. We size pricing to land below what you're already spending on the substituted line.
Who owns the models and the data?
You do. Every model trained on your data is yours, exported on request. Raw data and derived datasets stay encrypted, scoped to your engagement, and are deleted on termination per your retention policy.
What happens if AI gets so good our service becomes commoditized?
That's the bet. As models get better, the outcome we deliver gets faster and cheaper to produce — and the gap between "ChatGPT can almost do this" and "the books actually closed and the auditor signed off" stays wide. We capture that gap.
How fast can we start?
Discovery call this week, signed SOW within a few days, first deliverable inside 30 days for most services. Lakehouse and custom-model engagements typically take 6–8 weeks to first delivery.
Tell us what "done" looks like.
Forty-five minutes on a call. You leave with a one-page SOW and a flat monthly number. No salespeople, no slideware — the engineer who'd run your account is the engineer on the call.
Book a discovery callOr email hello@cloudbricks.ai
Built by engineers from
