Data warehousing for the AI era.
Your data warehouse and data lake (the central stores your reports already run on) hold years of investment. We make them the place everyone gets the same answer: organized, current, governed, and defined in writing.
That same foundation is what AI tools need to give answers you can trust, so the work pays off twice.
- Owner
- CFO
- Fresh as of
- 4 min ago
- Used by
- Board deck, sales dashboard, AI data analyst
The Gap
Most data platforms were built for reports, not for AI
AI tools will answer questions from whatever data you give them. The question is whether anyone can trust the answer.
They do the job they were designed for. AI asks for more:
- Definitions live in spreadsheets and in a few people's heads, not in the platform
- Contracts, tickets, and documents sit outside the warehouse entirely
- Data refreshes overnight, so answers are a day old
- Access rules are too coarse for an AI tool to respect person by person
- Older tools that move data between systems (often called ETL tools) are costly, hard to see inside, and slow to change
What AI-Ready Means
Six things your data needs before AI can use it well
None of them require starting over. Most organizations already have a warehouse or data lake worth building on.
The Context Lake
Your business data and your business knowledge (definitions, documents, the rules behind the numbers) organized in one place, so people and AI tools get the same answers.
Shared definitions
One written definition of revenue, active customer, and every other number that matters, stored where both your team and AI tools read it. No more three versions of the same metric.
Documents alongside tables
Contracts, PDFs, tickets, and shared drives indexed next to your warehouse, so questions can draw on both.
Access an AI can enforce
Permissions set down to individual records (row-level security), so an AI tool answering a question only shows each person what they're allowed to see.
Fresh enough to act on
Data that is minutes old, not a day old, for the parts of the business where timing matters.
Pipelines you can trust
The automated jobs that move and reshape your data (data pipelines) are written as tested code, with quality checks, alerts, and a record of where every number came from, so a wrong number gets caught before anyone acts on it.
The Shift
From a warehouse a few people query to one the whole company can ask
Built for Reports
Nightly batch loads
Answers are always a day behind
Definitions in people's heads
Three departments, three revenue numbers
Documents outside the warehouse
Half the context is unsearchable
Coarse access controls
Fine for dashboards, risky for AI
Drag-and-drop legacy data tools
Informatica, Talend, SSIS: hard to see inside, expensive to license
Failures found downstream
Someone notices the number looks off
Built for AI
Near real-time sync
Data minutes old where timing matters
Shared, written definitions
One number, used by people and AI alike
Documents indexed with tables
Questions draw on both
Access set person by person
AI only shows each person the records they may see
Data jobs written as code
Tracked, tested, and easy to change (we use dbt)
Quality checks and an audit trail
Problems caught at the source, every number traceable
Near Real-Time, Where It Pays
Minutes old, not a day old.
Most decisions don't need data that is seconds old. They need data from this morning, not last night. Under five minutes covers nearly every operational need at a fraction of the cost and complexity of true streaming. We'll tell you which reports need it and which don't.
Overnight Batch
from event to answer
Near Real-Time
from event to answer
How We Get There
Fresh data without the streaming bill
Near real-time rarely needs an expensive, always-on streaming platform. A technique called change data capture copies each update out of your business systems (orders, billing, inventory) the moment it happens, and small, frequent loads keep the warehouse a few minutes behind instead of a day.
That keeps the setup simple enough for your team to own. We save true streaming for the few cases where seconds actually change the outcome.
What we build:
- Live copies of changes from your business databases and cloud apps (change data capture)
- Loads every few minutes instead of once a night
- Your warehouse (Snowflake, Databricks, or Redshift) set up to take data continuously, with cost limits
- Calculations that update only what changed, so frequent refreshes stay cheap (built in dbt)
- Freshness checks and alerts, so you know when data is running late
- True streaming (Kafka or Kinesis) only where seconds really matter
Risk
Anomaly alerts
Unusual transactions or activity flagged within minutes, not discovered the next day.
Commerce
Inventory and pricing
Stock levels, pricing, and demand signals that stay in step across channels through the day.
Operations
Fulfillment visibility
Orders, shipments, and exceptions as they stand now, not as of last night.
Product
Usage and customer health
Adoption and account health that reflect this morning's activity.
Service
Same-day follow-up
Issues surface while there is still time to call the customer today.
Leadership
Today's numbers
Revenue, sales pipeline, and cash that a leader can ask about before the 10 a.m. meeting.
AI-Accelerated Migration
Automated conversion of legacy logic, not manual rewrites.
When a Platform Has to Go
Migrations measured in weeks, not years
Sometimes the legacy platform is the problem. Older tools like Informatica, Talend, and SSIS often hold hundreds or thousands of hand-built transformation rules (called mappings), and untangling them looks like a multi-year project. That fear is why most organizations stay stuck.
Our AI-accelerated migration process does most of the conversion work, so your move lands on a foundation that is ready for AI from day one.
- Reads the old rules and pulls out the business logic, and what depends on what
- Flags jobs nobody relies on anymore, so they're retired instead of moved
- Rewrites the logic as modern, tested code (in dbt)
- Surfaces the risky edge cases for engineers to review by hand
How It Works
From the data you have to answers your team can use, in four phases
Assess and plan
We map your warehouse, data lake, and pipelines against what your team and AI tools need, then sequence the work so each phase delivers something useful.
Typical output: a plain-language readiness report and a phased roadmap your CFO and CTO can both read. It often surfaces savings that pay for the work.
Build and migrate
We modernize pipelines and, where it makes sense, move workloads off legacy platforms with AI-accelerated tooling.
Most legacy logic is converted and test-covered automatically. Engineers spend their time on the edge cases, not the rewrite.
Make it AI-ready
We write down the shared definitions, index the documents, and set the access rules that let people and AI tools answer from the same data.
Your warehouse stops being a place a few analysts query and becomes a source anyone in the company can ask.
Activate and hand off
We put the foundation to work on a first use case, often an AI data analyst for your team, then document, train, and hand over.
You own what we build. No residual dependency on Tiber to keep the lights on.
Once the foundation is in place, the fastest way to put it to work is an AI data analyst that answers your team's questions in Slack, Teams, or email.
Your data already holds the answers.
We make it ready to give them. In 30 minutes we can usually tell you how close your warehouse is to AI-ready, and what the first step should be.