Data, Analytics & AI Consulting

AI Doesn't Lack Intelligence.
It Lacks Context.

Whether you're building a data foundation for the first time or replacing one that's outgrown its origins, the work is the same: a pipeline and warehouse architecture underneath a semantic layer that gives every tool the same view of what your business actually means. We build that layer.

Selective engagements  ·  Limited availability

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What We Do

One Specialty.
Every Altitude.

Whether the goal is a foundation built right from the start or a stack that's outgrown its origins, the underlying work is the same. Data infrastructure, analytics, strategy, and enablement / twenty years of depth across all of it, from the warehouse to the boardroom.

Hands-On Technical
Executive Strategy
Pipeline & Infrastructure Builds Analytics Engineering Data Strategy & Governance AI Readiness Executive Briefings & Roadmaps

Building the Right Foundation

Design and build modern, cloud-native data infrastructure from scratch, or replace a stack that's outgrown its origins. Either way, we architect for a team that can own and maintain it long after the engagement ends, built to support reliable reporting and whatever AI layer comes next.

Pipelines That Actually Work

Pipelines are the part of the stack everyone takes for granted until they fail. We build them with observability and maintainability designed in from the start, so your team spends time analyzing data rather than firefighting it.

Analytics Engineering & BI

Semantic layers, metrics definitions, and reporting surfaces that turn warehoused data into reliable, decision-ready analytics. Deep experience across Looker, Sigma, Tableau, and PowerBI. Built to be trusted, maintained, and used as a foundation for AI-layer work.

Data Strategy & Governance

Roadmaps, team design, data contracts, documentation frameworks, and governance structures. We help organizations understand what they actually need, then build a clear and achievable path to get there without overengineering it.

AI Readiness

Making Your Data AI-Ready

The layer between your data infrastructure and your AI tools. We build the semantic and context foundations that give language models and agents a real understanding of your business: your metrics, your logic, your definitions. Without this layer, AI tools are guessing. With it, they know your business.

AI Readiness

AI Agents for Data Workflows

Design and deploy agents that automate analysis, surface insights, and interact directly with your data infrastructure. Built on top of a well-architected stack rather than bolted alongside it. The difference is significant: agents that know your data model are far more useful than generic wrappers.

AI Readiness

AI Strategy & Use Case Discovery

Selecting the right AI use cases is harder than building them. Most failed AI investments lose value at the selection layer, well before any code gets written. We work through your operations, your data, and your team's actual workflows to identify where AI will produce real leverage and what to sequence first. You leave with a practical roadmap shaped by your actual stack and the team you have today, not by vendor claims about what's theoretically possible.

Use Case Mapping Sequencing & Prioritization Stack-Aware Roadmaps Build vs Buy Pilot Design
AI Readiness

Adoption & Change Management

A data foundation only matters if the team meant to use it actually does. Most analytics and AI initiatives quietly stall at the human layer, well after the technical work is done. We treat adoption as core engineering work and design the change management scaffolding that gets a team trusting and using the systems we've built together. The work spans hands-on dbt workshops for analytics engineers, AI literacy sessions for executives, internal documentation written to be read, and the cultural reinforcement that keeps new behaviors stable. The goal is to leave you with a team that owns this work for the long run.

Change Management AI Literacy for Executives Tool Onboarding Analytics Team Upskilling Internal Documentation
Where to Start

Context Sprint

A two-week, fixed-fee sprint that maps where your business context currently lives, identifies what's blocking your AI tools from reaching it, and surfaces the quick wins your team can execute right away. Action-oriented, not just diagnostic.

No infrastructure build required, and no commitment to anything bigger.

Most teams are past the question of whether AI matters. The sticking point is usually where to start and what's actually realistic with the tools and data they already have. That's what this sprint is designed to answer.

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01

System Inventory

Every tool in your stack reviewed for native AI capabilities and connector availability, with a clear assessment of what business context each one currently has access to.

02

AI Usage Audit

How your team is actually using AI tools today and what context those tools have been given, mapped against what they would need to know to be genuinely useful.

03

Context Gap Map

Where your business logic actually lives today and what's blocking your AI tools from accessing it in any consistent or reliable way.

04

Quick Wins List

Three to six prioritized opportunities actionable with existing tools, each with clear implementation instructions your team can execute immediately.

The Context Problem

Every AI Tool You Add Is
Asking the Same Question.

Every AI tool (your CRM, your analytics platform, your support agent, your code assistant) now has a field asking you to describe your business. Each one is filling a gap that shouldn't exist: they don't know your customer definitions, your business rules, your logic, your metrics. You're being asked to re-explain your business to every tool, every time.

Most companies handle this tool by tool. One definition of "customer" in Salesforce, a different one in Looker, a third in your data warehouse. When AI tools query across these, they get inconsistent answers, or none at all.

An AI-Ready Data Foundation solves this at the source. We build the semantic and context layer that encodes your business logic once: your metrics, your definitions, your rules, your relationships. Every tool that needs it can draw from it. You stop re-entering context. You start getting consistent, accurate, business-aware AI output.

This is the infrastructure problem that's defining enterprise AI right now. The companies that solve it first are the ones whose AI investments actually compound.

Don't take our word for it

Each link opens a new tab with a balanced question already typed. The honest read on whether we're right for you, from the AI you already trust.

Ask Claude Ask ChatGPT Ask Perplexity

We don't see the conversation, by design.

How We Work

Data expertise.
Applied at every level.

Seek Analytics isn't a generalist shop. The specialty is data and AI readiness, but the range is deliberately wide. The same practitioner who builds your data foundation can present the strategy to your board, train your team on what it means for them, and deploy the AI layer on top of it.

  • The same person who builds your data foundation can present the strategy to your board and train your team on what it means for them. The engagement covers the full range, with no handoffs between the technical work and the strategic framing.
  • The roadmap and the implementation come from the same person. What gets planned is what gets built.
  • Twenty years of data infrastructure experience is what makes the AI layer credible. The foundation has to hold up before anything built on top of it can.
  • Everything is built for your team to own and maintain. The goal is to leave you more capable than we found you, not to make ourselves necessary.
  • Strategy work can stand on its own. Some engagements end with a roadmap, a use case prioritization, or a stack assessment, and your team executes from there. The work doesn't need to come with a build commitment for it to land.
20+
Years of hands-on data & analytics experience
Depth
Across retail, commerce, fintech, media, and consumer tech. The underlying data challenges look similar regardless of industry.
E2E
Delivery: from data strategy through production deployment
Few.
At a Time.
Engagements are kept small by design. Every client gets full attention
Technologies

The right tools.
Applied to the right problems.

Most organizations already have more data than they know what to do with. The gap isn't data. It's the infrastructure to make it usable and the expertise to connect it to the tools your teams actually rely on.

Where Your Data Lives
Snowflake BigQuery Redshift Databricks
How It Gets There and Gets Shaped
dbt Airflow Fivetran Airbyte Portable
How Your Teams See and Use It
Omni Sigma PowerBI Looker Hex
How AI Connects To It
LLM Agents Semantic Layer Vector Databases RAG Pipelines n8n
Vertical Depth

Particular depth in retail, omnichannel commerce, and performance marketing, where customer journey complexity and attribution challenges demand the most rigorous data foundations. The same principles apply across every industry we've worked in.

Omnichannel Retail DTC & E-commerce Performance Marketing & Attribution Media & Publishing Marketing Technology Consumer Technology Financial Services Health & Wellness
How We Engage

Structured for impact.
Flexible by design.

Every engagement takes a different shape. The model that fits depends on what you're trying to solve and how your team operates. Here's how most partnerships come together.

Get in Touch

Most good engagements start with a conversation.

Seek Analytics takes on a small number of engagements at a time. That's intentional. It's the only way to give each one the attention it actually needs.

If you're building a new data capability, untangling one that's become a bottleneck, or trying to figure out what AI can actually do for your business, reach out. We'll quickly figure out whether there's a fit and what it could look like.

Message received.

We'll be in touch shortly to see if there's a fit.

Common Questions

How do you typically get started?

Most engagements begin with a focused discovery conversation. We look at your current state, what you're trying to accomplish, and where the gaps are. From there we scope the right type of engagement together.

Who is this right for?

Senior leaders at growth-stage or mid-market companies who know their data situation isn't where it needs to be, whether that's for day-to-day operations, AI initiatives, or both. Also larger organizations with data teams that are new to AI tooling and want experienced outside perspective.

Do I need to know exactly what I need?

No. Many of the best engagements start with 'we know something is wrong but aren't sure exactly what.' Diagnosis is part of the work.