Services

What We Build

Four service lines covering the full range of data and AI system needs for operations-intensive businesses.

Data Architecture & Engineering

Build the data infrastructure that actually reflects how your business operates.

When to engage

Your team makes decisions from spreadsheets, your reporting takes days to produce, or your data sources don't connect into any coherent picture.

·Modern data warehouse design (Snowflake, BigQuery, Postgres, Redshift)

·ETL/ELT pipeline engineering — dbt, Airflow, Prefect, custom pipelines

·Operational data modeling: designing schemas around business processes, not just source systems

·Real-time vs. batch pipeline design and trade-off analysis

·Data quality frameworks and observability (Great Expectations, dbt tests, Monte Carlo)

·Self-service analytics layer design for non-technical stakeholders

·Data warehouse migration and modernization from legacy systems

AI Workflow Engineering

Production AI systems — designed around your actual workflow, not a generic template.

When to engage

You have a clear knowledge or workflow problem that AI could solve, but your previous pilots never made it to production or didn't deliver the expected ROI.

·RAG (Retrieval Augmented Generation) system design and deployment

·Vector database architecture — pgvector, Pinecone, Weaviate, Qdrant

·LLM integration and prompt engineering for production workloads

·AI agent design for bounded, well-defined operational tasks

·Document processing pipelines: extraction, chunking, embedding, indexing

·Human-in-the-loop workflow design for AI-assisted decision systems

·AI system observability: logging, evaluation, drift detection, cost management

·Fine-tuning assessment — most use cases don't need it; we'll tell you honestly

Solution Engineering

Custom tooling and integrations for the operational gaps off-the-shelf software doesn't cover.

When to engage

Your team is working around a tool instead of through it. You've duct-taped too many systems together. Or you're maintaining a process that a well-built piece of internal tooling could eliminate entirely.

·Internal operational dashboards and reporting platforms

·Custom workflow tooling for ops and revenue teams

·System integrations and API connectivity between disparate tools

·Geospatial platforms, mapping layers, and location intelligence systems

·Internal knowledge bases and document management systems

·Process automation: reducing manual work across operations, finance, customer success

·Data entry and validation tooling to reduce errors at the source

Technical Advisory & AI Adoption Strategy

Vendor-neutral guidance for leaders making high-stakes decisions about data and AI infrastructure.

When to engage

You're making a significant investment in AI or data infrastructure and want a technical perspective that isn't coming from a vendor trying to sell you something.

·Build vs. buy assessment for AI and data tooling decisions

·Vendor evaluation and RFP support — we review contracts and tell you what the fine print means operationally

·AI readiness assessment: where your data, team, and processes are relative to where AI can help

·Architecture review for existing data/AI systems before scaling

·AI strategy development grounded in operational reality, not theoretical capability

·Technical due diligence for acquisitions and partnerships involving data systems

·Quarterly advisory retainers for ongoing technical leadership support

Engagement Model

How we work

Focused sprints, clear deliverables, and a working system at the end. Not a retainer that generates monthly reports.

Scoping Call

One call, no agenda beyond understanding your situation. We ask about operational pain, existing systems, team structure, and what you've already tried. We'll tell you honestly whether we can help.

30–45 minutes

Diagnostic Sprint

A structured 1–2 week deep-dive into your current state. We map workflows, audit data systems, interview stakeholders, and produce a scoped engagement plan with a specific deliverable and measurable outcome.

1–2 weeks

Build Sprint

Focused execution against the scoped deliverable. Regular check-ins. Working demos as soon as possible. No big reveals at the end — you see progress throughout.

2–8 weeks depending on scope

Handoff & Documentation

Clean handoff with thorough documentation, training where needed, and a clear operational runbook. Ongoing advisory available if you want continued support, but we don't create dependency.

Included in every engagement

Start Here

Not sure which service fits your situation?

Start with a scoping call. We'll map the problem and tell you what's actually warranted.

Schedule a Scoping Call