u/myth-buster9999

Scottish Water had plenty of capital investment data. The problem was getting the right answer to the right person quickly.

Project teams often had to search through a large collection of reports—or rely on analysts and data specialists to extract information from underlying tables. That created duplicated reporting work, slowed decision-making, and made valuable project data harder for non-technical users to access.

Their answer was SPARK, an internal natural-language interface built with Databricks Genie and embedded in Microsoft Teams through Copilot.

Instead of asking, “Which report has this information?”, teams can ask questions such as:

• Which open project risks are expiring this month, and who owns them?
• What is the current live risk score for a project?
• Which risk has the highest exposure?
• Who is the future contractor for a project?

Genie translates those questions into queries against governed data in Unity Catalog, using curated gold-layer data and metric views to keep business definitions consistent.

The governance and delivery details are especially interesting:

• Business rules, fiscal-period conventions, project IDs, and milestone logic were explicitly configured.
• Worked examples and benchmark queries were used to improve and repeatedly test answer quality.
• Adoption, conversation patterns, query performance, and cost per user are monitored in production.
• Databricks Asset Bundles and Azure DevOps support repeatable deployment across development, test, and production environments.

The reported efficiency opportunity is meaningful: if 100 users ask three questions per week, saving just two to five minutes per request could recover roughly 10–25 hours every week.

The broader lesson is that conversational analytics is not just about adding a chatbot on top of raw tables. The quality of the experience depends on the foundations underneath it: curated data, shared semantics, governance, testing, monitoring, and integration into the tools people already use.

It’s a useful example of how organizations can make governed data more accessible without giving up trust or control.

What has your experience been with natural-language analytics? Does embedding it in an existing collaboration tool make adoption more likely, or do users still prefer traditional dashboards?

I feel really positive about all the recent progress of the Genie One which is backed up by all the business contexts, data, metrics views, pages and driven by Genie Ontology. It can get us answers cost effectively compared to external models connected to Databricks. Earlier it didn't have much unstructured data support, but it is now allowing connection to UC Volume as well as attaching the file option to a chat. There'll soon be image attachment options too where we can add screenshots as an additional context and ask questions like we do generally with ChatGPT or Gemini in our personal life.

Great days for businesses ahead 😀

I just want to share my experience of using Omnigent for the last month or so and to be honest I am really enjoying it. I love using Polly agent to run my development use cases where one harness/model do the coding and other harnesses/model do the critical review. The Supervisor Polly then hands over the critical review fixes to the original agent and then cross-review again. It saves so much of my time and still the end product remains quite high quality.

I also use Debby for multi-agent brainstorming. Where Claude and Codex debate on a topic and comes back with where they agree and where not. This really helps me to understand wider or different perspectives on a topic which using one harness never helps. One harness (e.g. just Claude) may blindside our thoughts even with it's hallucinations.

The team collaboration is another feature I really love where I can share the whole session to another of my colleagues who can also provide his/her input and review output codes/files/prompts.I don't think there's any other meta-harness in the market that provides this capability.

It also comes with Intelligent Model routing feature that can route your requests to different capabilities and cost model to get better outcomes and cost efficiencies. However, if I am being honest, the effect of this is not so visible to me as a user.

Although the product is still evolving and just like any other products there are some gaps and bugs. But overall it's a great experience so far and I would really recommend everyone to try this out.

I just want to share my experience of using Omnigent for the last month or so and to be honest I am really enjoying it. I love using Polly agent to run my development use cases where one harness/model do the coding and other harnesses/model do the critical review. The Supervisor Polly then hands over the critical review fixes to the original agent and then cross-review again. It saves so much of my time and still the end product remains quite high quality.

I also use Debby for multi-agent brainstorming. Where Claude and Codex debate on a topic and comes back with where they agree and where not. This really helps me to understand wider or different perspectives on a topic which using one harness never helps. One harness (e.g. just Claude) may blindside our thoughts even with it's hallucinations.

The team collaboration is another feature I really love where I can share the whole session to another of my colleagues who can also provide his/her input and review output codes/files/prompts.I don't think there's any other meta-harness in the market that provides this capability.

It also comes with Intelligent Model routing feature that can route your requests to different capabilities and cost model to get better outcomes and cost efficiencies. However, if I am being honest, the effect of this is not so visible to me as a user.

Although the product is still evolving and just like any other products there are some gaps and bugs. But overall it's a great experience so far and I would really recommend everyone to try this out.