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Current-state map
Happy path, exceptions, owners, systems of record, and the handoffs where work actually stalls.
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We map the real process, remove the broken handoffs, and deploy reliable AI on top of the systems you already use. No generic transformation deck. No migration required to prove value.
The control layer coordinates deterministic software, model judgment, human approvals, and evaluation around the systems already running the business.
One workflow. One measurable baseline. One architecture. Enough evidence to decide whether building is worth it.
Happy path, exceptions, owners, systems of record, and the handoffs where work actually stalls.
Touch time, elapsed time, error cost, exception rate, and the KPI that should improve.
Each step classified as deterministic software, agentic judgment, or a human decision.
A narrow proof, golden examples, pass criteria, failure modes, and the production go / no-go.
AI is one ingredient. The higher-value work is deciding what should disappear, what should become normal software, what needs model judgment, and what should stay human.
Observe the workflow as it actually runs. Capture exceptions, handoffs, system conflicts, touch time, elapsed time, and the KPI that matters.
Remove needless steps first. Then decide what belongs in deterministic code, what needs AI judgment, and where a human must remain accountable.
Ship the smallest useful system, create golden examples, test failure paths, measure quality, latency, and cost, and keep actions auditable.
Integrate with the systems already running the business, expand autonomy gradually, monitor drift and exceptions, and measure the business result.
APIs and orchestration over your CRM, warehouse, inboxes, documents, and operational tools.
A prototype is not production until quality and failure behavior can be measured against real examples.
Start with read, draft, recommend, or queue. Expand write privileges only when evidence supports it.
These are workflow classes, not canned solutions. The sprint proves whether a specific process has enough pain, volume, and measurable upside to justify a deployment.
Best fit today: teams with measurable handoffs across CRM, email, spreadsheets, analytics, documents, or internal systems.
Lead routing, CRM hygiene, deal-desk coordination, pipeline updates, forecast assembly, and handoffs that still live across Slack, email, and spreadsheets.
Reconciliation, reporting preparation, data-quality triage, source conflicts, exception queues, and recurring analytical workflows across systems.
Email, PDF, form, and document-heavy processes where inputs vary but the operating rules are still repeatable.
Evidence gathering, response drafting, case routing, knowledge retrieval, and escalation flows with explicit human checkpoints.
Golden datasets, evals, permission boundaries, model routing, cost controls, audit trails, and release criteria for AI systems already being built.
Structured data, KPI standards, QA, executive reporting, and AI-assisted insight workflows built on the analytics stack you already use.
The first engagement is intentionally narrower than a transformation program. We want production evidence before expanding scope.
Teams with CRM, email, analytics, support, and internal systems creating repeated operational handoffs.
Small teams carrying a disproportionate amount of coordination, reconciliation, chasing, and exception handling.
You have pilots or agents, but need evals, guardrails, integration, monitoring, and a path from demo to production.
You want value from AI without replacing the systems of record the business already depends on.
The goal is not to maximize how much AI appears in the architecture. The goal is a better operating process.
An engineering-led workflow transformation engagement with a measurable baseline, staged deployment, explicit evals, and operational ownership after launch.
The company learns from real deployments. Repeated paid patterns can become managed services or software later. They do not start as a platform assumption.
Map one high-value workflow, redesign it, build the minimum reliable system, evaluate it, deploy it, and operate it against a business KPI.
A narrow founding pilot for US HVAC and plumbing teams focused on bilingual inbound lead response, booking truth, and recovery when a handoff fails.
See the founding pilot ↗Digital reporting systems for agencies and ecommerce teams that want KPI standards, SQL starters, QA, dashboards, and reusable reporting assets.
Browse reporting products ↗MB Data Automation designs and deploys AI-enabled operating workflows for US B2B teams. We combine process redesign, data engineering, systems integration, evaluation, and production operations so the work actually functions inside the business.
The goal is not to put AI everywhere. It is to make one important workflow materially better, prove it with evidence, and expand only when the economics and reliability hold.
Start narrow with an AI Workflow Sprint. If the workflow has enough measurable upside, move into implementation and ongoing operation.
Code, integrations, evals, data quality, and production evidence come before a polished transformation narrative.
Data engineering, analytics engineering, cloud platforms, orchestration, APIs, CRM workflows, and enterprise integration are part of the same delivery surface.
Deterministic logic, orchestration, model calls, and evals stay separated so the workflow can evolve without becoming hostage to one model.
No invented case studies, customer logos, ROI, or deployment claims. We label founding-stage offers honestly until production evidence exists.
The category is new. The operating model is straightforward: map the workflow, build the right mechanism, evaluate it, and measure the result.
It is a hands-on engineering model where the team works close to the operators, maps how a workflow actually runs, redesigns the process, builds the required software and AI components, deploys them into the existing stack, and measures the result in production.
Not by default. We prefer to build on top of the systems already running the business using APIs, integrations, deterministic software, and AI only where judgment is useful. A migration is recommended only when it is independently justified.
Each step is classified by what it requires. Predictable rules usually belong in deterministic software. Variable inputs or bounded judgment can be good agent candidates. High-risk or poorly supported decisions stay human-in-the-loop with the evidence prepared for the reviewer.
We focus on one workflow. We map the happy path and exceptions, identify handoffs and systems of record, establish baseline metrics, design the future-state process, classify the steps, and define the smallest prototype and eval plan that can prove whether deployment is worth continuing.
The first commercial beachhead is Revenue Operations and Data Operations for US B2B SaaS and tech-enabled service teams. We are looking for handoff-heavy workflows across CRM, email, spreadsheets, analytics, documents, and internal systems where delays and exceptions are measurable.
No. We define the baseline and success metric before implementation so the result can be measured honestly. If the workflow does not show enough expected value, the correct outcome of the sprint may be not to build it.
Yes. The reporting kits remain available as self-serve products. Forward Deployed AI Engineering is the primary custom service for teams that need deeper workflow redesign, integration, evaluation, and production ownership.
Have a workflow in mind? Start with a scoped sprint ↗
We map it, challenge the unnecessary steps, establish the baseline, and decide whether the right answer is software, an AI agent, a human checkpoint, or a combination.
Our engagement process
Bring one process, its pain, and the systems involved. We will decide whether a scoped AI Workflow Sprint is a fit.