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Data Engineering & ai / Ai Engineering
Get AI from idea to production, faster
Most organizations don't lack AI ambition - they lack a path to production. We use modern AI tooling to move use cases from idea to working system faster than a traditional build cycle.
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TECHNICAL CAPABILITIES
What it takes to get AI into production
AI use case strategy
We identify and prioritize AI use cases by business impact, then embed them into the systems you already run - not as standalone tools.
AI application development
We build AI applications, copilots, and agentic workflows on foundation models, grounded in your enterprise data through retrieval frameworks and vector databases.
Production deployment & governance
We deploy models into production with MLOps tooling, monitoring, evaluation, and governance appropriate to each use case.
OUR Approach
How we move from idea to impact
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01
Identify
Identify and prioritize AI use cases by business impact, embedded into systems you already run - CRM, ERP, Billing, customer service - not standalone tools.
02
Build
Build AI applications, copilots, and agentic workflows on foundation models - Claude, GPT, Gemini - grounded in your enterprise data through retrieval frameworks and vector databases.
03
Accelerate
Use AI-assisted engineering tools, including Claude Code, in our own delivery process to move from use case to working system faster than a traditional build cycle.
04
Deploy
Deploy models into production using MLOps tooling - MLflow, Kubeflow, Amazon SageMaker - with monitoring and evaluation to keep systems performing after launch.
05
Govern
Apply governance, evaluation, and oversight appropriate to each use case before scaling deployment.
Outcomes
What changes when AI actually reaches production
Impact over novelty
AI use cases identified and prioritized by business impact, not novelty.
Embedded, not bolted on
AI capability built into systems teams already use, not a separate tool.
Faster to production
Faster time from concept to a working production system.
Governed at scale
AI deployment that scales with governance and oversight appropriate to each use case.
Tech stack
The technology behind the transformation
FAQ
Frequently asked questions
How fast can you start?
No. We build the substrate environments, reward models, eval harnesses, data pipelines, feedback loops and hand it to your training infrastructure. You run the GPUs. We run the engineering around them. That lane discipline is part of why we work as a partner, not a vendor.
Can the work be co-authored or made public?
No. We build the substrate environments, reward models, eval harnesses, data pipelines, feedback loops and hand it to your training infrastructure. You run the GPUs. We run the engineering around them. That lane discipline is part of why we work as a partner, not a vendor.
How do you handle confidentiality and data?
No. We build the substrate environments, reward models, eval harnesses, data pipelines, feedback loops and hand it to your training infrastructure. You run the GPUs. We run the engineering around them. That lane discipline is part of why we work as a partner, not a vendor.
Do you run the actual training?
No. We build the substrate environments, reward models, eval harnesses, data pipelines, feedback loops and hand it to your training infrastructure. You run the GPUs. We run the engineering around them. That lane discipline is part of why we work as a partner, not a vendor.
Ready to modernize your core tech without the risk?
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