Grounded in your governed data and running inside your security perimeter: large language model applications that answer with your numbers and your documents, under the access controls you already enforce.
Grounding is the difference between a plausible answer and a defensible one. We connect models to your governed tables and your documents, and the data never leaves the platform to be useful.
Diagram of a grounded generative AI architecture: structured warehouse data and unstructured documents such as tickets and contracts feed a grounding layer of retrieval and governed semantic definitions, which feeds Claude running inside Snowflake Cortex AI. The output is a cited, verifiable answer. A dashed boundary around the system is labelled inside your security perimeter.
Inside your security perimeter
StructuredWarehouse tables · metrics
UnstructuredTickets · contracts · docs
→
GroundingRetrieval + semantic definitions
→
ClaudeInside Snowflake Cortex AI
→
AnswerCited · role-scoped · auditable
Structured and unstructured sources feed retrieval and semantic definitions; the model answers with citations, inside the same security boundary as the warehouse.
What’s included
What generative AI consulting includes
We treat generative AI as an engineering discipline with an evaluation harness, not a demo with a roadmap.
Use-case selection with a paper trail: value, risk, and feasibility
Retrieval-augmented generation (RAG) over documents, tickets, and contracts
Grounding in semantic definitions, so the numbers come back right
In-warehouse LLM functions for classification, extraction, and summarization at scale
Agent orchestration across structured and unstructured sources
Guardrails: role-scoped access, auditability, and human review paths
Model selection and cost tuning per workload, tested rather than assumed
Evaluation harnesses, so quality is measured instead of felt
Our approach
How we work
01
Select
One use case with real value and bounded risk. Not a platform-wide rollout on day one.
02
Ground
Connect the model to governed data and documents, with definitions it cannot misread.
03
Guard
Access scoping, audit logging, and human review where the stakes warrant it.
04
Evaluate & expand
Measured quality against a test set, then the next use case, carried by the first one’s results.
Outcomes
Answers your teams can check, from data that never left your platform. That combination is what turns a pilot into a rollout.
In the architectures we implement, no. Models are called against your data under your existing controls, and platforms like Snowflake Cortex AI are designed so data is not used to train the underlying models and does not leave your governed environment. We walk your security team through the specifics before anything ships.
Why Claude, and do we have a choice?
We often deploy Anthropic’s Claude models through Snowflake Cortex because it keeps data inside the perimeter and the models are strong on reasoning over business context. Model choice is workload-dependent, though, and we test options against your evaluation set rather than assume.
How do you prevent wrong answers?
You reduce them and make the rest visible, because no honest implementer promises zero. Grounding in governed definitions, verified queries for high-stakes questions, citations users can check, and human review where it matters. Set up that way, generative AI accelerates analysis without asking anyone to trust it blindly.
Where do most generative AI projects fail?
Before the model is even involved: they point a capable model at fragmented, undefined data and industrialize the confusion. Foundations first is not a slogan, it is the failure analysis.
