For Analytics leads whose stakeholders still export everything to a spreadsheet
AI/BI & Genie dashboards
TechFabric builds AI/BI and Genie experiences on Databricks: the semantic layer and metric definitions underneath them, benchmark question suites that measure answer accuracy over time, dashboards reading those same definitions, and Unity Catalog permissions so a Genie answer respects who asked it. The two to four week Genie Accuracy engagement is the named way to start.
Genie answers a business question in English, and the answer holds up when somebody checks it against finance.
4 common questions, answered below ↓Genie will answer any question you ask it. Making the answers right often enough that a VP stops checking them is a data modelling problem well before it is a prompt problem. Genie reads your tables, your column names and whatever instructions it was given, so an ambiguous grain or two columns both called revenue produce a confident wrong number and no warning. We build the layer underneath: metric definitions agreed with the people who own the number, joins and grain written down rather than inferred, and a set of benchmark questions with known answers that Genie has to keep passing before anyone trusts it in a board pack. Dashboards read the same definitions, so the figure on the slide and the figure Genie gives are the same figure.
- Metric definitions agreed with whoever owns the number, written once and read by both Genie and the dashboards
- Benchmark questions with known answers, run as a suite, so accuracy is measured rather than felt
- Grain and joins written down, instead of Genie inferring them from column names
- Row and column filters inherited from Unity Catalog, so an answer respects who is asking
How an engagement works
01
Talk to an engineer
A real conversation about your initiative with a senior engineer who has built this before. Not a sales call. What you are trying to build, what has been tried, and what is realistic.
02
Discovery and scoping
Two to three weeks to clarify requirements, evaluate where AI fits, and define realistic scope. On AI work this is also where success gets defined precisely enough to score, because a goal nobody can measure cannot be hillclimbed. You get a plan you can act on before committing to a larger engagement.
03
The right team, daily demos
We put the team the work actually needs on it and show you running software every day. Built with the same rigor as any enterprise system: tested, monitored, documented.
04
Production and beyond
Deployed and running under real load, handling real business processes. Ongoing support and team continuity for whatever comes next.
Migrations to Databricks
Off Snowflake, Synapse, Teradata and SQL Server, onto Lakehouse and Lakebase, with a cutover you can reverse.
Data engineering
Pipelines that hold, tables people trust, and a bill that stops surprising you.
Data science & AI
Context stores, memory, retrieval and governed agents that survive production.
Machine learning
Models that reach an endpoint, get retrained on a schedule, and can be rolled back by somebody who was not there.
Data & AI governance
Unity Catalog designed so grants hold, lineage survives a refactor, and an agent inherits permissions instead of routing around them.
Forward-deployed teams
Product, design and engineering people who sit inside your business, find the real problem, and ship it.
Product development
Full product delivery: multi-tenant architecture, operator consoles and the data layer under them. Shipped as Databricks Apps when the product belongs next to the lakehouse.
APIs & durable systems
Long-running operations that survive restarts and partial failure. Temporal under the lakehouse jobs, agent runs and approvals that must not half-complete.
FAQ
AI/BI & Genie dashboards, answered
Why does Genie give confident answers that are wrong?
Usually because the model underneath is ambiguous rather than because the language model is bad. Two tables both hold something called revenue, the grain changes halfway through a table nobody documented, or an instruction says one thing and a column name implies another. Genie resolves that ambiguity silently and moves on. The fix is upstream of the prompt every time.
How do you measure whether Genie is accurate?
With a suite of benchmark questions that have known answers, run repeatedly rather than once at launch. That gives you a number that moves when someone renames a column, which is the only way to know accuracy is holding. The Genie Accuracy engagement at /databricks/genie-accuracy exists to build that suite and the definitions under it.
Can Genie show someone data they should not see?
Not if the permissions are done properly, and this is the question worth asking early. Genie queries under the identity of the person asking, so row and column filters in Unity Catalog apply to the answer the same way they apply to a query. Where that boundary has not been drawn yet, it is governance work before it is Genie work, and it is at /services/data-ai-governance.
If we have Genie, do we still need dashboards?
Yes, and they should read the same definitions. A dashboard answers the question everybody asks every Monday, and it should look identical every Monday. Genie answers the question somebody thought of in the meeting. Building them on separate definitions is how you end up with two numbers and an argument.