Generative AI
Large language model applications grounded in your governed data, running inside your security perimeter. We implement generative AI that answers with your numbers and your documents, under the access controls you already enforce.
The problems this solves
Shadow AI is already here
Teams are pasting company data into public chatbots, and nobody signed off on that.
Demos without receipts
A generative AI pilot impresses in the meeting but cannot cite where its answers came from.
Security has questions
Legal and your CISO are asking things the vendor deck does not answer, and they are right to ask.
Knowledge is trapped
The answers your teams need live in tickets, contracts, and documents nobody has time to read.
Grounded answers, inside your perimeter
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 labeled inside your security perimeter.
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
How we work
Select
One use case with real value and bounded risk. Not a platform-wide rollout on day one.
Ground
Connect the model to governed data and documents, with definitions it cannot misread.
Guard
Access scoping, audit logging, and human review where the stakes warrant it.
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.
Frequently asked questions
Is our data used to train the models?
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.
Where this connects
Analytics & Agentic Activation
Dashboards your teams check daily, and AI agents they can question in plain language, both reading from the same governed definitions.
AI & Machine Learning
We build machine learning that earns its keep: forecasting, scoring, and classification on data that is already clean, current, and governed.
Data Architecture & Governance
We design data platforms with security, ownership, and access control built in from the start, so your teams trust the data and your auditors can verify it.
Generative AI, grounded in your data
If you want AI your security team approves and your finance team believes, the path runs through your data foundation. Let’s map it.
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