Where we've shipped. Map each to a real reference engagement.
Consulting
FDE Pods
Staff Augmentation
Project Pods (Fixed-Fee Delivery)
Practice Areas
We've gone narrow on purpose. Each practice has senior engineers with real production scars across F500 deployments, no generalists, no junior bodies padding the bench.
The tension
Every AI initiative dies in the same place: the data underneath it. Models are fine. The lakehouse is a swamp, the semantic layer is six conflicting definitions of “revenue,” and nobody trusts the numbers enough to let an agent act on them. You cannot automate on top of data you have to double-check by hand.
What we do
We build lakehouses, warehouses, semantic layers, and governance that survive real workloads. Migrations off legacy systems with the numbers validated on the way out. Modeling that gives the whole company one definition of each metric. Data quality and lineage so an agent or an auditor can trust what it reads.
The stack
Snowflake, Databricks, Azure Synapse, dbt, Collibra.
What you get
A platform your engineers can own the day we leave, with documented models, tested pipelines, and governance that holds up in a regulated audit.
Proof
99.9% validated data accuracy on a Fortune 500 cloud migration.
Who it is for
VPs of Data and Data Engineering leaders who need the foundation right before they put AI on top of it.
The tension
Everyone has a slick agent demo. Almost nobody has one running in a regulated production environment, because that is where the real work lives evals, guardrails, governance, and the unglamorous plumbing that keeps an agent from doing something expensive and wrong at 2am. A demo is a magic trick. Production is engineering.
What we do
We build agents that pass evals and run in production. MCP servers that connect agents to your real systems. RAG pipelines grounded in your governed data. Eval frameworks so you can prove an agent behaves before it touches a customer. Governance so security and compliance say yes.
The stack
Claude API, MCP servers, RAG pipelines, eval frameworks, Snowflake Cortex.
What you get
An agent your compliance team has signed off on, with the evals and guardrails documented, running on your stack not a prototype that lives on a laptop.
Proof
We built and run Preleap, our own AI product, in production on Snowflake Cortex and we prototype agents on synthetic data before they ever touch a client system. We ship AI ourselves. That is the proof.
Who it is for
Heads of AI and engineering leaders who have proven the demo and now have to make it real, safe, and auditable.
The tension
Cloud bills climb, deploys get scarier, and every migration carries the quiet fear of downtime that ends up in a board deck. Most teams are not short on cloud tools. They are short on the discipline that makes infrastructure boring and boring is exactly what you want from infrastructure.
What we do
Infrastructure as code so your environment is reproducible, not a thing one person remembers how to fix. CI/CD that makes deploys routine. Observability so you see problems before customers do. FinOps so the cloud bill is a decision, not a surprise. Zero-downtime cutovers when the migration cannot blink.
The stack
AWS, Azure, GCP, Terraform, GitHub Actions.
What you get
Cutovers with no operational downtime, a cloud bill under control, and a deploy pipeline your team trusts.
Proof
Enterprise cloud migration delivered with zero operational downtime.
Who it is for
Platform and infrastructure leaders carrying a migration or a runaway cloud bill they need handled by people who have done it at scale.
The tension
In regulated data, security is not a feature you add at the end, it is the thing that decides whether you ship at all. Bolt it on late and every audit becomes a fire drill. The teams that move fast are the ones where compliance and lineage were designed in from day one.
What we do
Cloud security and identity that fit how your engineers actually work. Compliance and data governance built for regulated data, HIPAA and beyond. Lineage so you can answer “where did this number come from” in seconds. Audit readiness as a standing state, not a quarterly scramble.
The stack
Cloud security, IAM, Azure WAF, Collibra.
What you get
Governance and lineage that make audits routine, and a security posture your compliance team helped design instead of inherited.
Proof
Governance built for HIPAA-regulated data environments.
Who it is for
CISOs and compliance leaders in healthcare, payers, and financial services who cannot afford a surprise in an audit.
The tension
Most companies are drowning in dashboards nobody opens. A wall of charts is not insight, it is decoration with a refresh schedule. The hard part is a semantic model everyone agrees on and reporting an executive actually acts on Monday morning.
What we do
Semantic models that give the business one agreed definition of every metric. Executive reporting built around the three decisions a leader actually makes, not forty they ignore. Embedded analytics inside the products and workflows where the decision happens.
The stack
Power BI, Tableau, Looker.
What you get
Reporting people open because it answers their real question, on a semantic layer the whole company trusts.
Proof
Executive reporting platforms running at Fortune 500 scale.
Who it is for
Analytics and data leaders whose executives keep asking for “one number we can agree on.”