What We Build
Every generative AI engagement is different. What’s consistent is the level of engineering — and the standard that it has to ship.
LLM Product Strategy
Roadmaps, model selection, architecture decisions, and build plans grounded in what actually ships — not what demos well. From greenfield AI products to LLM integration in existing enterprise systems.
Multimodal AI Workflows
Text, image, audio, and video pipelines designed for production environments. Built for the edge cases, the load, and the failure modes that don’t appear in development.
Internal AI Tooling
LLM-powered creation tools, workflow automation, and internal platforms that real teams actually use. Built for adoption and sustained use, not just demonstration.
Production Delivery
Senior engineering embedded in your team through the full build cycle — deployment, monitoring, iteration. Not a handoff. A partnership through to live.
Challenges We Solve
The gap between an AI demo and an AI system that works in production is where most enterprise builds stall. These are the specific problems we’re brought in to solve.
The Demo-to-Production Gap
A model that performs well on clean test data often fails under the distribution of real production inputs. We architect for the production environment from day one, not as an afterthought.
LLM Evaluation at Scale
Measuring whether an LLM-powered system is actually working in production requires purpose-built evaluation infrastructure. We design these from the ground up for each use case.
Latency and Cost at Enterprise Scale
A response time that works in a demo is often unacceptable in production. We design for real throughput requirements and real cost envelopes from the first architecture discussion.
Integration With Existing Enterprise Systems
Enterprise AI doesn’t run in isolation. We design for the full integration surface — authentication, data pipelines, compliance requirements, and existing infrastructure — not just the model.
Who It’s For
Not every generative AI initiative needs senior embedded engineering. This one does if:
- The build has a fixed timeline and failure is expensive
- The requirements are novel, complex, or not yet fully defined
- A typical dev agency or experimental team isn’t the right fit
- You need someone who has shipped AI products at enterprise scale before
“Category leaders bring us in when the stakes are too high for guesswork — technology that has to ship, timelines that can’t slip, and problems no one has solved yet.”
Common Questions
How do you approach LLM selection for enterprise builds?
Model selection is a function of the use case, the performance requirements, the cost envelope, and the compliance constraints — not a default. We evaluate the full landscape of available models against the specific production requirements of your build, and we document the reasoning so the decision is auditable as the landscape changes.
How do you handle proprietary data and security requirements?
Data handling, retention, and security architecture are designed to meet the specific compliance requirements of each engagement from the first architecture discussion — not retrofitted in at the end. We work within your existing security frameworks and bring experience with enterprise data environments where the answer to “can this leave our environment” is always no.
What does a generative AI engagement look like end-to-end?
Engagements typically begin with an architecture and scoping phase to align on the production environment, the integration surface, and what “done” means. From there, we move into embedded development — design, build, test, and deploy — with senior engineering in the room throughout. We don’t hand off at launch; we stay through the first production cycles to close the gap between delivery and stability.
If the Build Has to Work, Let’s Talk
Serious generative AI builds start with a conversation, not a proposal. Tell us what you’re trying to ship and when.
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