Agentic automation Multi-agent systems

From stalled pilot to a shipped AI agent in 3 weeks.

We ship multi-agent workflows on your real data: reconciliation, recurring reporting, back-office ops. In production, not a demo.

Trusted data Reliable automation Production AI

Do you trust the data driving your decisions?

We reconcile the systems behind critical decisions, expose what does not agree, and build reliable workflows on numbers leaders can defend.

Revenue Leak Map Contribution margin

Where is your margin leaking?

Discounts, COD/RTO, shipping and consumables, reconciled to true contribution margin, so you see where reported growth is not becoming profit.

A hand holding a phone showing Contribution Margin Analysis: CM 31 percent and a waterfall where discounts, COD/RTO, shipping and consumables are the biggest margin leaks
How we workSenior-led, embedded
MethodAgents + human sign-off
Live inWeeks, not quarters
EvidenceRead case studies →

See what a defensible operating view looks like.

Revenue Leak Map reconciles order, cost, return, fee, and acquisition data so teams can see where reported growth is not becoming profit.

Explore the worked example →
Revenue Leak Map product profitability view showing revenue, true margin, and products losing profit

Warehouse-native. We build where your data already lives.

From messy source systems to trusted decisions and production AI without forcing a platform replacement.

04Decide and activateAnswers, dashboards, and workflows

Power BI · Looker · Tableau · OpenAI · Claude

03Model and orchestrateCanonical metrics and reliable jobs

dbt · Python · Airflow · Fivetran

02WarehouseWhere governed data lives

BigQuery · Snowflake · Databricks · Postgres

01Source systemsThe operational truth, still messy

Shopify · Salesforce · SAP / ERP · Stripe · HubSpot · GA4

The most dangerous number in your business is the confident one that's quietly wrong.

Dashboards and AI can sound certain while the data underneath remains unresolved. We make the uncertainty visible, reconcile what can be proved, and state where the evidence still stops.

01

Estimated costs reported as fact

COGS, freight, and fees filled with assumptions but presented like measured truth.

02

The same record counted twice

Customers, orders, or entities duplicated across systems that never agreed.

03

Three tools, three answers

The same metric reported three ways with no defensible source of truth.

What is happening. Why. What to do next. And where you should not trust the answer yet.

Questions we hear a lot

How much does working with MLDeep cost?

Where an offer has a fixed scope, the price is published on its page, with no form required. For build and retainer work, where the scope varies, we agree a fixed price in writing before any work begins, so you never get an open-ended bill. Tell us what you need and we will point you to the number that applies.

Do I need to hire a data engineer, or can a consultant handle this?

For most growing teams, a scoped engagement gets you further, faster, than a hire. We reconcile the systems behind your critical metrics, build the reliable pipelines, and hand them to your team to own, so you add senior data and AI capacity without a headcount decision or a long ramp. If you hire later, you inherit clean foundations instead of a backlog. See how this plays out in our case studies.

How long does a project take?

Most first engagements ship in one to two weeks, not months. We scope tightly on purpose, so you get a working result, whether that is a reconciled number, an automated workflow, or a 90-day roadmap, quickly enough to act on it this quarter.

Will you have access to our data, and is it secure?

Access is scoped to the engagement and nothing more, granted least-privilege and revoked when we finish. We work inside your existing warehouse and tools rather than copying data out, so your data stays under your control, and we are glad to work within your security review and sign a standard NDA and DPA.

Is MLDeep a fit for a company like mine?

We work best with two kinds of teams: founders from seed to Series B who want one automated workflow shipped fast, and data teams at growing companies getting ready for production AI. If your decisions ride on numbers you are not fully sure you can defend, that is exactly what we fix, and a short fit call is the fastest way to find out if we are right for you.

Tell us what you are working on. We will tell you honestly if there is a fit.

A short working conversation about the problem, the available data, and whether there is a credible next step.