AI Transformation Partner
Cross into
the AI era
deliberately.
Most organizations are guessing at AI. We partner with you to adopt it with intent — from the data foundation through the systems that run in production.
The Shift
Most AI work stalls between demo and production.
Pilots impress. Few survive contact with real users, real data, and real systems.
We work with you to close that gap — starting with the data layer that AI actually needs, then building the systems that run on it.
AI fails on bad foundations.
Most organizations need the data layer rebuilt before the model can do its job.
Prototypes are not products.
Production AI is constraints, monitoring, and integration — not just a working notebook.
Adoption isn't a launch.
The team has to operate it after we leave. We design for the handover from day one.
How We Partner
Two ways to work together.
Most engagements begin with Advise and continue into Deliver — but you don't have to commit to both up front.
Advise
Help you decide what to do, what not to do, and what it will cost.
- AI strategy and roadmap
- Data and architecture assessments
- Due diligence for build-vs-buy decisions
- Executive workshops and education
Typically 4–12 weeks
Deliver
Build and run the AI systems with you — from data foundation to production.
- Data layer: build, sanitize, normalize, prepare
- Models, pipelines, and integrations
- Production deployment and observability
- Knowledge transfer to your team
Typically 3–6 months, with optional ongoing partnership
Our Approach
We start with the data.
Before AI can do anything useful, the data has to. We build, sanitize, normalize, and prepare the foundation — whatever it takes to get a layer AI can actually run on.
Then every engagement descends through the same five phases — whether we're advising for four weeks or building with you for six months.
Why Stratium
Four things we won't move on.
We start with the data.
Most AI projects fail at the data layer. We don't skip past it — we build, sanitize, normalize, and prepare the foundation that makes the model actually useful.
What this means: data work comes first, every engagement.
We don't ship pilots.
If it can't run in production with real users and real load, it isn't done. We design for the day after launch, not the demo on Monday.
What this means: every system we ship has to survive real use.
We transfer ownership.
You shouldn't need us forever. We build with your team, document the system honestly, and hand it off — runbooks, observability, and training included.
What this means: your team owns it after we leave. Period.
We work alongside, not above.
No deck-and-disappear consulting. We sit in your repo, your standups, and your trade-offs — and we say what we think.
What this means: we're in the work, not above it.
The Work
Problems we take on.
How we'd bring order to each.
An ops team drowning in unstructured documents that an LLM could parse — if the data layer existed.
We rebuild the document pipeline, prepare the data for retrieval, and ship a system the team can actually trust.
A leadership team that knows AI should change their business, but can't tell which ten bets are real.
We assess the landscape, sequence the bets by feasibility and impact, and write the roadmap that survives a board review.
An engineering org with a working prototype that won't survive contact with production.
We harden the data layer, add observability, and ship the system end-to-end — alongside the team that will own it.
A data estate that's been promising AI for years and still can't answer a basic question.
We start with the foundation — cleaning, normalizing, and modeling — until the data layer is something AI can actually run on.
Anonymized. We share more in a first conversation.
Engage
Start the conversation.
We respond personally. No funnels, no scoring, no "nurture sequence" — a real reply from someone who can tell you whether Stratium is the right partner.