CHALLENGES
Most organizations have invested heavily in data, documents, and operational systems. Access to all of it is bottlenecked by who knows how to query it, navigate it, or ask the right person.
Questions stack up waiting for analysts, agents, or subject-matter experts to respond.
Institutional expertise lives in dashboards, PDFs, ticket systems, and people's heads, with no single interface a user can query directly.
Every question that requires SQL, navigating a portal, or knowing who to ask becomes a ticket somebody has to clear.
Questions that never get asked because asking is too hard.
Approach
We build Conversational AI systems, combining semantic modeling, retrieval-augmented generation, and natural language interfaces to put answers in front of users in the tools they already use.
01
Use case definition and scoping
02
Semantic modeling for structured data and indexing for unstructured content
03
Interface design and platform integration
04
Governance framework for accurate, reliable, appropriately-scoped responses
The output is a production system your users rely on every day, along with the semantic models, retrieval patterns, and governance practices that make every subsequent build faster to ship.
Applications
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Built for
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Built for
Sales, marketing, and customer success teams who need instant access to customer data, pipeline insights, and performance metrics
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Built for
Customer-facing assistants, support agents, and concierge experiences across travel, retail, financial services, and beyond
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Built for
Higher Education institutions giving their community instant access to institutional resources, policies, and support
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Built for
Healthcare organizations reducing administrative burden and improving access to clinical information and care guidance
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Built for
Manufacturing and operations teams getting fast answers from maintenance records, operational data, and institutional knowledge
Accelerator
For organizations whose dashboards leave the ad-hoc data questions in the analytics backlog, we deploy the Conversational AI Accelerator, a Snowflake-native query layer built on Cortex Analyst, Cortex Search, and Snowflake Intelligence.
What's included
Conversion workflows for existing Tableau and Power BI semantic models into Snowflake's semantic layer, plus a tuned Cortex Analyst deployment that translates natural language into precise SQL.
A configured Snowflake Intelligence deployment connected to Cortex Analyst for structured data and Cortex Search for documents and other unstructured content.
Role-based access inherited from your existing Snowflake configuration. Configurable guardrails for Cortex credit consumption and query cost limits.
Multiple semantic models and data domains supported. Start with one team and expand without rearchitecting.
Process
01
Use case scoping and semantic model audit
02
Cortex Analyst tuning, semantic model conversion, and Cortex Search setup
03
Query accuracy testing and governance verification
04
Snowflake Intelligence rollout and user enablement
FAQs
It looks like an interface where users ask in plain language and get answers drawn directly from your data, documents, or operational systems. A sales leader asks "what's our pipeline coverage for Q3 by region" and gets an answer in seconds. A patient asks "when is my next appointment and what should I bring" and gets accurate guidance. A maintenance technician asks "what was wrong with this machine last quarter" and pulls the relevant records. Same architecture, different use case.
Three layers. First, semantic models define the relationships and metrics for structured data, so the natural language layer translates against a governed vocabulary rather than guessing at table schemas. For unstructured content, retrieval is grounded in the source documents the system has been given. Second, verified query patterns and validated examples train the system on the questions your users actually ask. Third, ambiguity guardrails ask the user to clarify rather than producing a confident wrong answer.
Yes. Interfaces run inside Slack, Teams, web applications, mobile, your existing BI layer, or as embedded widgets in customer-facing products. Existing Tableau and Power BI semantic models can be converted into Snowflake's semantic layer rather than rebuilt from scratch.
Off-the-shelf chatbots work when the question set is narrow, the data is static, and the answers can be pre-scripted. Custom Conversational AI is the right choice when answers need to come from live data, governed semantic models, or proprietary documents, when the question set is open-ended, or when responses need to respect user permissions and data boundaries. Most enterprise use cases fall into the second category.
For structured data, every response shows the SQL generated, the semantic model fields referenced, and the source data drawn from. For unstructured retrieval, the system cites the specific documents or passages the answer came from. Users can verify each answer against the underlying source.
