AI enablement
Put LLMs and AI agents to work on your commercial pharma data.
Your analysts want to ask an AI tool why the brand moved. Your engineers could hand routine pipeline work to a coding agent. Neither works on commercial data with no tests and no context. We get your team using the LLMs and AI agents it has chosen, on your Snowflake or Databricks stack, and build the tests and context layer that let you trust what they produce.
AI writes the pipeline code and the tests. Engineers review every line and decide what ships.
The framework
DT + DX + CTX = 10x- DT
- Data Trust. Tests and freshness checks on every feed, so the model reads data that is right.
- DX
- Data Experience. A curated set of tables named for what they mean.
- CTX
- Context Engineering. Definitions, example queries, and live state a schema cannot carry.
The problem
Your brand team wants answers from AI. The data underneath is not ready to give them.
Your CEO wants AI in the launch deck. Your analysts want to ask Cortex Analyst or Genie why NBRx dipped in the Southeast. Meanwhile your engineers are still reconciling the IQVIA refresh against claims by hand, and every new request waits in the queue.
A new tool will not fix that. An AI agent on a messy pipeline generates broken code faster. A chatbot pointed at a warehouse with no context layer guesses which of 150 tables to join, then answers confidently and wrongly. Commercial data makes it worse, because you did not create most of it. Partial files, vendor restatements, and territory realignments all load cleanly, and a model reading them inherits every one.
~0%
end-to-end accuracy when leading LLMs that scored above 85% on benchmarks were tested against real enterprise data warehouses.
MIT BEAVER study, Sept 2024
95%
of enterprise generative AI pilots produced no measurable financial return.
MIT NANDA, The GenAI Divide, 2025
64%
accuracy when data that is 80% correct is queried by a model that is 80% accurate. The arithmetic: the two errors multiply.
What we install
Six things we put into your data engineering so AI works on commercial data.
On your Snowflake or Databricks stack, with TestGen for data quality testing and DataOps Process Observability for pipeline monitoring. The six tracks run in parallel. Our engineers work inside your team, and everything transfers to you by the end.
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01
Build the data trust foundation first
DT · Data quality and observability
AI amplifies whatever it reads, so untested data becomes wrong answers faster. We put TestGen tests on your most error-prone feeds and the tables the brand team reads, and monitor freshness, volume, schema, and quality scores. A partial specialty pharmacy file stops in staging instead of feeding an AI answer.
What you get
Test coverage on critical tables, freshness monitoring on every feed, and quality scorecards your team can show a CFO.
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02
Curate the tables that matter
DX · Data experience
If your warehouse has 150 tables and half are deprecated, the AI cannot tell which is which, so it joins the wrong ones. We curate the small set that answers most commercial questions: the interlinked stars on physician, payer, product, time, and territory, with every join key written down.
What you get
A curated front door to your warehouse, tables named for business concepts, and a deprecated registry the AI respects.
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03
Engineer the six-category context layer
CTX · Context engineering
Documentation is not context. We build all six categories between your data and your AI: schema and grain, definitions, validated example queries, live freshness and quality state, history, and entitlements. The model stops guessing what EQ_TRX means, how the payer hierarchy rolls up, or which alignment file is current.
What you get
Context built in CI/CD next to the pipeline that produces the data, and updated when the schema changes.
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04
Set up isolated environments for agent work
Branched environments on your stack
You cannot let an AI coding agent iterate against production for 30 minutes and hope nothing gets dropped. We set up zero-copy clones in Snowflake or Databricks, each with its own Git branch and compute. Your engineers run three or four agent sessions in parallel, and none touches the launch report until the work is promoted.
What you get
Branched environments wired into your CI/CD, concurrent agent sessions, and a bounded blast radius.
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05
Refactor pipelines to FITT
Functional, idempotent, tested, two-stage
A pipeline a person can reason about is safe for an agent to change. We refactor your most error-prone stages into FITT data architecture: same input, same output, every run safe to repeat, every transform tested, two stages instead of a chain of layers. When a specialty pharmacy resends Tuesday's file on Thursday, you reload it and move on.
What you get
A FITT pattern your team applies to every new feed, idempotent SQL, and no 2am debugging across five intermediate layers.
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06
Wire tests into the agent loop
AI agent working patterns
An LLM can write and run code. It cannot judge whether the output is correct. Tests do that. We wire your test suite into the agent loop so the agent knows when it is done and when it broke something upstream, then set up the parallel workflow: memory files the agent reads at the start of every session, several sessions trying different approaches, and an engineer picking the winning diff.
What you get
AI coding agents adopted across the team, parallel agent patterns, and context that survives a session restart.
How it runs
We do the work alongside your team. You own it when we leave.
Your data stays in your cloud account and every line of code lives in your repository from the first sprint, so the handover is a normal day.
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01
Assess
Two weeks on one of your data sources. You get a gap report on data trust, data experience, and context, an AI-readiness scorecard, and a sized proposal.
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02
Build
Tests, observability, isolated environments, the FITT refactor, and the context layer, built in your cloud account and your repository.
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03
Operate
One to two quarters running it with your team on production workloads. Real agent sessions, with analyst corrections fed back into the context layer.
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04
Transfer
Your team owns it, or whoever you name. Open source tooling, documented patterns, no exit fee. We leave when you run it without us.
The tools
Your stack, your AI tools, and two open source tools.
Nothing proprietary to leave behind. The platform page sets out what runs in your cloud and what runs in ours.
Your LLMs and AI agents
Whichever your company approved: ChatGPT, Claude, Gemini, GitHub Copilot, Cursor. Coding agents work in branched environments and are graded by tests.
Snowflake and Cortex Analyst
Zero-copy clones for agent work. Cortex Analyst reads the context layer.
Databricks and Genie
Zero-copy clones for agent work. Genie reads the context layer.
DataOps TestGen
Apache 2.0 data quality tests that run as SQL inside your warehouse.
DataOps Process Observability
Freshness, volume, schema drift, and quality scores across every pipeline.
Your Git repository
Every transform, test, context file, and agent memory file lives there from the first sprint.
Read the method
What the engagement is built on.
Or have us build it
No team to enable yet? Our engineers build and run it, then hand it over.
The same practice, with our engineers doing the work: AI drafts the pipelines and tests, our engineers review and own what ships, and your team takes over on a schedule you set. Replacing a data vendor? We run the old and new side by side until the numbers agree, then cut over. How a vendor switch runs.
Questions
About AI enablement.
What is AI enablement for a commercial pharma data team?
Why do AI coding tools underdeliver on commercial data work?
Can our analysts trust what an AI tool says about our commercial data?
Will this make Snowflake Cortex Analyst or Databricks Genie work?
How do you stop an AI agent from breaking production data?
Do we own the platform and the code?
How does an AI enablement engagement start?
See it on one of your data sources.
Sixty minutes, the feed your brand team asks about most, end to end on your screen. What the tests would catch, what the context layer would hold, and what your AI tool answers when it reads the right shape of data.