Services

Practical technology services built around operational outcomes.

Engagements begin with the process, decision, or reporting problem. Technology is selected only after the actual requirements and constraints are understood.

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Analytics and BI Modernization

Replace fragile reporting processes with governed data, consistent metrics, and reporting that leaders can trust.

Intended result

A clearer reporting model with less manual work, stronger metric consistency, and better traceability from source data to business decisions.

BigQuery
dbt
Looker Studio
Power BI
SQL
Python

Problems this addresses

Reporting depends on manual spreadsheet consolidation.
Different teams calculate the same metric differently.
Critical reports depend on undocumented knowledge.
Existing dashboards show activity without supporting decisions.

Possible deliverables

Current-state reporting and data-flow assessment
Target-state analytics architecture
KPI and metric definitions
Semantic model or governed reporting layer
Dashboard or reporting prototype
Migration and implementation roadmap

Workflow Automation

Turn repetitive, disconnected work into structured processes that are easier to operate, monitor, and improve.

Intended result

A repeatable workflow that reduces manual coordination while preserving visibility, accountability, and human review where it matters.

Google Forms
Apps Script
Cloud Run
Google Workspace
Pub/Sub
Python

Problems this addresses

Employees repeatedly copy information between systems.
Requests arrive through inconsistent channels.
Documents, approvals, and status updates are manually coordinated.
Important work depends on reminders and individual follow-up.

Possible deliverables

Workflow and process assessment
Trigger, action, and exception design
Automated intake and notification workflows
Document or report generation
Operational logging and error handling
Process documentation and support guidance

Google Cloud Architecture

Design practical cloud solutions around business requirements without introducing infrastructure the organization does not need.

Intended result

A documented cloud architecture that is proportionate to the problem, maintainable by the organization, and defensible to technical and business stakeholders.

Firebase
Cloud Run
BigQuery
Cloud Storage
Pub/Sub
GitHub Actions

Problems this addresses

A cloud project has tools but no coherent architecture.
Teams are unsure which managed services fit the requirement.
Security, deployment, and operations are considered too late.
A prototype needs a credible path toward production.

Possible deliverables

Requirements and constraint analysis
Service selection and architecture design
Architecture diagrams and decision rationale
Security and access-control recommendations
Deployment and environment strategy
Implementation roadmap or working prototype

AI-Enabled Knowledge Workflows

Apply generative AI to research, documentation, and knowledge-intensive work where the sources and review process can be clearly controlled.

Intended result

A controlled AI-assisted workflow that accelerates knowledge work without treating generated output as automatically accurate or complete.

Vertex AI
Gemini
NotebookLM
Cloud Run
Google Workspace
Python

Problems this addresses

Employees spend significant time assembling background information.
Useful knowledge is scattered across documents and systems.
Documentation is difficult to create, maintain, or consume.
AI experimentation is occurring without a defined operating process.

Possible deliverables

Use-case and source-data assessment
Prompt and workflow design
Research or document-generation prototype
Source-grounding and human-review controls
Output quality and failure-mode evaluation
Implementation and governance recommendations

Engagement approach

Move from uncertainty to a working, supportable solution.

The exact scope depends on the problem. Not every engagement requires a full implementation.

Understand

Clarify the operating problem, affected users, constraints, risks, and decisions the solution must support.

Design

Define the smallest credible architecture and document why each major component is needed.

Demonstrate

Build a prototype, pilot, or working implementation that proves the design against real requirements.

Operationalize

Add documentation, deployment practices, controls, and ownership needed to sustain the solution.

Deliberate boundaries

What the work should avoid.

Credible consulting includes knowing when complexity, automation, or a particular technology is not justified.

Technology selected before the business problem is understood
Large custom platforms when a managed service can meet the need
AI output presented as reliable without source grounding or review
Dashboards built without agreed metric definitions
Automation that removes visibility into controls or exceptions

Find the right starting point

Clarify what should improve before deciding what should be built.

Use the readiness assessment when the problem is real but the correct initiative is not yet clear. If the scope and desired outcome are already defined, start a direct project conversation instead.

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