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AI inside the platform you already trust — Snowflake Cortex, end to end.
For teams already standardized on Snowflake, Cortex keeps your security, permissions, and the ability to trace where numbers came from all inside the platform. We build the AI functions, document-reading pipelines, Cortex Search (an AI assistant over your own documents), Snowpark ML model catalogs, and the Cortex Analyst layer that lets people ask questions in plain English — all without your data leaving Snowflake.
What this means for you
If your company already runs on Snowflake, this is about adding AI to it without your data ever leaving — so you get document reading, an assistant over your own files, and plain-English answers while keeping the exact security and controls you already trust.
When the security perimeter matters, Cortex is hard to beat.
Your data, the search-by-meaning step, the AI calls, retrieval, and the model all stay inside Snowflake's security and permissions. The roles you've set up, the rules that hide sensitive fields, and the ability to trace where a number came from all carry straight over into your AI work — nothing to re-build.
Cortex is the right call when most of your data already lives in Snowflake, your permissions there are already solid, and your AI work is close to everyday reporting — an AI assistant that answers from your own documents, reading invoices and contracts, sorting and pulling details from data at large scale, and letting people ask questions in plain English through Cortex Analyst. We've shipped all of it. The platform isn't a fit for everything — heavy machine learning on messy, free-form data and lots of model-retraining still lean toward Databricks — and we'll tell you when a different setup is the better answer.
The Cortex stack we ship most often.
Each component slots into the Snowflake account you already run.
Cortex AI functions that run right inside Snowflake
Built-in functions to complete text, summarize, pull out an answer, translate, gauge sentiment, and categorize — called directly in your normal Snowflake queries. No data leaves the platform. Handy for enriching data in bulk, sorting it, and anywhere sending data out to an outside AI service would otherwise be slow.
Cortex Document AI for reading documents
Built-in pipelines that read invoices, statements, contracts, and forms — landing clean, structured records in the same Snowflake where your day-to-day data already lives. We design the output shape, the checking rules, the hand-off to a person for review, and the test set.
Cortex Search — an AI assistant over your own documents, in Snowflake
A managed service that searches your documents both by meaning and by keyword, with who-can-see-what enforced through Snowflake's own security. We handle splitting the documents into pieces, labeling them, keeping them up to date, and the test set.
Snowpark ML for training and cataloging models
Preparing the data your models use, training the models (XGBoost, scikit-learn, PyTorch), and a built-in catalog of your models — all inside Snowflake. The models run through Snowflake functions or containers depending on how fast the answers need to come back.
Cortex Analyst + trusted definitions for your numbers
Ask questions in plain English and get answers that aren't made up. We build the agreed, trusted definitions of your business numbers that Cortex Analyst reads from — so when someone asks a question in English, they get the same answer the dashboard would have given.
Security, hiding sensitive data, and monitoring
Rules that hide sensitive fields in both the questions and the answers, permission checks on what each person can retrieve, a log of every Cortex call, and dashboards showing what each use case costs. All built on the controls you already enforce.
Cortex use cases we ship most often at mid-market.
These are the patterns where staying in-platform pays off.
Internal knowledge assistants over your own documents
Policy, procedure, and contract questions where each person's permissions have to carry over into the AI. Cortex Search plus Snowflake's permission rules handle that out of the box.
Invoice, statement, and form processing
Cortex Document AI reads the documents; Snowflake checks the results against your official records; the clean records flow straight into accounts-payable, claims, or order-management work.
Bulk enrichment and sorting
Categorize tickets, sort support requests by what they're about, pull names and details out of free-text fields, gauge sentiment — at large scale, inside Snowflake, without moving the data out.
Self-serve analytics for the business
Cortex Analyst on top of properly governed, trusted definitions of your numbers. The CFO's team gets reliable answers in plain English; finance keeps control of exactly how each number is defined.
When Cortex isn't the right answer.
We're transparent about where the platform fits.
Heavy machine learning on messy data, or lots of model-retraining
If the work centers on training custom models on large amounts of messy, free-form data — images, audio, lots of model-retraining — Databricks Mosaic AI is usually the better fit.
Instant answers at very high volume
Snowflake isn't built for answering huge numbers of requests in a fraction of a second. For real-time fraud scoring, real-time recommendations, and the like, we'll usually run the model on a platform built for that.
Most of your data lives outside Snowflake
If most of the data the model needs lives outside Snowflake, constantly shuttling it in and out cancels out the benefit of staying in-platform. A mixed or platform-independent setup may fit better.
The foundation isn't there yet
Cortex inherits whatever's underneath it. If Snowflake doesn't yet have trusted number definitions, the ability to trace where numbers came from, or each customer showing up as one clean record, that's the work that comes first.
Three reversible phases.
Same engagement model as our migration work. Scoped and quoted, with a real exit.
See the full engagement modelCortex readiness assessment
Quoted by scopeWe look at your Snowflake account, your security setup, and the use cases on your roadmap. You leave with a go / fix-this-first / wait plan for each use case and an estimate for any groundwork.
Foundation & first use case
Quoted by scopeWhatever groundwork the use case needs (trusted number definitions, tracing where numbers came from, a document pipeline, rules to hide sensitive fields) plus the first Cortex build end-to-end, with a test set and monitoring.
Scale & enablement
Quoted by scopeMore use cases on the same foundation, working side by side on the written guides so your team runs it. Post-launch and ongoing support are available by quote.
The questions we get most often.
Cortex vs. calling OpenAI / Anthropic from outside?
Cortex's advantage is keeping data inside your walls — the AI call happens inside Snowflake, under the same permissions and sensitive-field rules your data already follows. The trade-off is choice: you get the models Snowflake offers (which now include Anthropic and a strong set of open models) rather than every service on the market. For work close to everyday reporting, and anywhere security matters, that trade is usually worth it.
How does Cortex Search compare to Pinecone or pgvector?
Cortex Search is a managed service inside Snowflake that searches by meaning and by keyword, with the upkeep handled for you. The advantages are built-in security and less to operate; the trade-off is less control than running your own search index. For most mid-sized document-assistant projects where the documents already live in Snowflake, Cortex Search is the right default.
Can we run private / open-weight models on Cortex?
Yes — Snowflake can run open models inside the platform, through Cortex and containers, which keeps the AI entirely inside your own environment. We've used this for regulated work where even sending data to an outside AI service would be a problem.
What about cost?
Cortex is billed by usage — by the amount of text processed (for the AI functions and Search) and by computing time (for Snowpark ML). We build dashboards that show spend by use case, by warehouse, and by user as part of every project. The readiness check gives you an estimate before you commit.
Are you a Snowflake partner?
We work extensively with Snowflake and ship Cortex builds regularly. We stay deliberately independent on recommendations — when Databricks or a mixed setup is the better answer, we'll say so.
How this shows up in the work we ship.
Case studies and writing from engagements where this thinking actually meets production.
Adjacent topics in AI enablement.
Want to know if this is the right next move for your team?
Take the free AI & Data Readiness Assessment, or schedule 30 minutes to talk through your situation. Either way, you'll leave with a clearer picture of where you are and what the next steps should look like.