Your Snowflake is full of answers.
No one can reach them.
Employees search five tools and find nothing. Analysts queue for SQL reports that take three days. Executives make decisions without the data that already exists in your warehouse. We build two intelligence layers on top of the Snowflake you already run, and change all of that.
Your data in.
Intelligence out.
Data sources
- Confluence / Notion
- Jira / Linear
- PDFs & Documents
- CRM (Salesforce / HubSpot)
- Email threads
- SQL Databases
Your data warehouse
Snowflake
Cortex Search, Cortex Analyst, governance. Already in place.
Intelligence layers
Document Intelligence
Semantic search and AI Q&A across all of your knowledge sources.
NL Analytics
English questions become SQL, then answers with written explanations.
Who gets access
- Operations teams
- Business analysts
- Executives
- New employees
- Product managers
Data exists.
Access doesn't.
The problem is not a shortage of data. Most enterprise Snowflake environments hold years of structured and unstructured information. The bottleneck is human. Finding the data, querying it, and trusting the result takes more time than it should.
2 to 4 hours
Daily time lost to information retrieval
Employees search Confluence, ask colleagues, and re-read old emails. There is no single place to get a reliable answer from company knowledge.
3 to 5 days
Average wait for an analyst-built report
Business users with a simple question still queue for data team bandwidth, while decisions wait and opportunities close.
Around 60%
Of Snowflake data never queried by business users
The data sits. The value does not move. The reason is not bad data. The access layer was never built.
Two products. One Snowflake.
Both systems run inside your existing Snowflake environment. No new data warehouse. No migration. No data leaving your perimeter. No new vendor for your security team to approve.
Document Intelligence & Knowledge Base
Every document your company has ever produced gets ingested into Snowflake and made intelligently searchable. Confluence, Jira, PDFs, CRM notes, email threads, and internal databases. Employees ask questions in plain language and get precise, cited answers. Not a list of links. The actual answer, with the source document linked. This is your institutional memory, finally retrievable.
Example question
“What are our service-level commitments to enterprise customers for P1 incidents?”
Example question
“What did the product team decide about mobile architecture in the Q3 review?”
Natural Language Analytics
Business users ask questions in plain English. The AI writes schema-aware SQL, executes it against your Snowflake tables, and returns results with a plain-language explanation. Not just a table of numbers. For complex questions, the system chains multiple queries, identifies patterns across dimensions, and generates a written report. No analyst required. No ticket. No wait.
Example question
“Which customer segments had the highest churn rate last quarter?”
Example question
“Why did conversion drop in the Northeast in October? Show me the breakdown.”
What this looks like
in your industry.
Every industry has the same underlying problem of fragmented knowledge and inaccessible data. But the specific questions, the stakes, and the payoffs are different. Select yours below.
Key pain · Compliance burden and analyst bottlenecks on risk data
Compliance teams manually hunt for policy documents and audit evidence across six systems. Risk analysts queue for SQL reports on portfolio exposure. Relationship managers cannot self-serve client history without a data engineer. Your Snowflake already holds all of it. It just is not accessible.
Document Intelligence
User asks
What are our KYC requirements for corporate account onboarding in the UAE?
AI responds
Retrieves from 3 compliance documents and 1 regulatory update. Summarises the specific requirements, flags the 2024 CBUAE amendment, and cites each source with a direct link.
User asks
Find all audit evidence related to our Basel III Tier 1 capital calculations.
AI responds
Surfaces 12 documents across your drive, CRM notes, and PDF archive. Groups by evidence type, dates each one, and flags gaps against the audit checklist.
Outcome
Compliance teams reduce audit preparation from days to hours.
Natural Language Analytics
User asks
Which loan categories had default rates above 3% last two quarters, broken down by geography?
AI responds
Queries your loan performance tables. Returns a ranked breakdown with trend comparison. Flags that SME loans in 3 regions are approaching your internal risk threshold.
User asks
Why is our net interest margin declining this quarter?
AI responds
Runs 4 chained queries across funding cost, loan yield, and product mix tables. Identifies term deposit repricing as the primary driver. Generates a 3-paragraph written summary with the supporting numbers.
Outcome
Risk analysts self-serve in minutes instead of queuing 3 days for reports.
Once the foundation is built,
the compound begins.
Document Intelligence and NL Analytics are the first layer. Teams that build correctly on Snowflake unlock a set of higher-order capabilities that were not possible before, because the data is now structured, accessible, and governed in one place.
Proactive Anomaly Intelligence
Instead of waiting for someone to ask, the system monitors your Snowflake data continuously and surfaces anomalies before your team would think to look. Revenue spikes. Defect trends. Unusual customer behaviour patterns.
Expansion · Phase 2
Revenue Intelligence & Forecasting
Predictive models for churn probability, expansion likelihood, and pipeline conversion. Models build directly on your CRM, product usage, and transactional data in Snowflake. Revenue teams get a forward view, not just a rearview mirror.
Expansion · Phase 2
Automated Compliance Reporting
For regulated industries, the AI reads your operational data, maps it to the relevant framework (SOC 2, HIPAA, Basel III, GDPR), and generates a draft evidence pack or regulator-ready report. Compliance becomes continuous, not a quarterly sprint.
Expansion · Regulated verticals
Customer 360 Agent
A unified customer profile assembled from CRM, support tickets, transactional history, and product usage. Any team can interrogate it in plain English. CS, sales, and product finally see the same complete picture without a data team in the loop.
Expansion · Phase 2
Competitive Signal Monitor
External data (earnings calls, job postings, pricing pages, news) flows into Snowflake and gets synthesised by AI into a weekly competitive intelligence brief. The first team to act on a signal wins; this closes the lag.
Expansion · Strategic layer
Employee Onboarding Accelerator
New hires ask the knowledge base anything (process history, product decisions, client context, team norms). The system answers from your institutional memory. Teams that used to take 90 days to ramp now take 30.
Expansion · HR & People
Why build on Snowflake,
not a new system?
Your data never leaves your perimeter
Snowflake Cortex handles vector search and LLM inference natively. Sensitive data stays inside your environment, behind your existing auth and governance. One compliance conversation, already had.
No migration. No new vendor.
No separate vector database, no new ETL pipeline, no new platform your security team has to approve. Everything runs on infrastructure you already operate and pay for.
Your governance layer keeps working
Role-based access, data masking, audit logs. The Snowflake governance you have already invested in applies automatically to everything we build on top of it. Nothing leaks across boundaries.
One platform for structured and unstructured data
Snowflake now handles vectors, documents, and structured data in a single query layer. Building separately for each data type creates fragmentation. Building on Snowflake compounds the value over time.
You own the system, the models, and the pipelines. We hand over full source code at completion. The intelligence we build becomes your competitive moat, not ours.