AI-Powered Data Analytics
Natural-language analytics — ask questions, get SQL and answers instantly
How Does AI Change Data Analytics in Practice?
Your analytics team is stuck in a reactive loop: Slack requests, Jira tickets, ad-hoc data pulls. They cannot do strategic analysis because the queue never empties. Meanwhile, leadership waits days for numbers that should take minutes.
We deploy AI-powered analytics that lets business users query databases in plain English — with guardrails that prevent wrong answers and expensive queries. But we learned the hard way: connecting AI directly to a database without a semantic layer produces confident nonsense. That is why we build the data foundation first — metric definitions, data quality checks, access controls — then add AI self-service on top. This builds on the same principles as our business intelligence consulting and data analytics consulting practices.
The result: analysts focus on strategic work while routine questions get answered in seconds. Anomaly detection catches issues before they reach dashboards. AI generates narrative summaries so executives get insights, not spreadsheets.
For automated reporting and data pipeline monitoring, see our AI Agents & Automation service. Companies that need a comprehensive AI roadmap before deploying analytics tools start with AI Strategy & Implementation. For enterprise-level data leadership that ties analytics to business outcomes, consider our Fractional CDO engagement.
What AI-Powered Analytics Delivers
Business users query your warehouse in plain English with built-in guardrails
Anomaly detection that explains what changed and why in plain language
Reduce ad-hoc analyst queue by 80% with AI self-service analytics
AI turns data tables into executive-readable narrative summaries automatically
Semantic search finds the right table or metric without exact naming
Data quality monitoring detects schema drift and freshness issues early
Problems & Solutions
"Our analysts are stuck in a reactive ad-hoc queue"
We deploy a natural-language query interface on top of your data warehouse. Business users ask questions and get accurate results in seconds. The AI understands your schema, applies correct joins, and respects access controls. Result: 70-80% of ad-hoc requests resolved without analyst involvement.
"We have dashboards but nobody trusts the numbers"
We build a semantic layer with canonical metric definitions, automated data quality checks, and a metrics catalog. When dashboards disagree, the system flags the discrepancy and explains why. One source of truth, enforced by AI.
"Monthly reporting takes our team 3 days"
AI agents query your data sources on schedule, generate the charts and tables, write narrative commentary highlighting what changed and why, and deliver the finished report to Slack or email. Your analysts review and adjust in 30 minutes instead of building from scratch in 3 days. That's 60% of their reporting time reclaimed for actual analysis.
Built to remove every barrier
Month-to-Month
No long-term contracts. Stay because the results speak for themselves, not because you're locked in. Cancel with 30 days notice.
NDA from Day One
Your data, strategies, and competitive intelligence stay confidential. Mutual NDA signed before any engagement begins.
First 30 Days Guarantee
If we haven't delivered at least 3 actionable data wins in the first 30 days, the first month is on us. No questions asked.
Your IP, Always
All dashboards, pipelines, documentation, and code belong to you. Full knowledge transfer is built into every engagement.
US & EU Time Zones
Core team operates across EST-PST and CET. Async updates daily. Sync meetings on your schedule, not ours.
Transparent Reporting
Weekly progress updates with measurable outcomes. You'll always know exactly what we're working on and why it matters.
Not sure where to start?
Take our 5-minute CDO Healthcheck. Get a personalized scorecard across data ownership, metrics culture, and trust in data, before committing to anything.
Your data stays secure
Mutual NDA
Signed before any data access. Your competitive intelligence stays confidential.
SOC 2 Compatible
Our processes align with SOC 2 Type II controls. We work within your existing compliance framework.
GDPR & CCPA Ready
All data handling follows privacy regulations. We never store client data on personal devices.
Your Infrastructure
We work inside your systems — your cloud, your tools, your access controls. Nothing leaves your perimeter.
What our clients say
Valiotti Data did a fantastic job helping us design our Tableau dashboards. They quickly understood what we needed, were easy to communicate with, and delivered high-quality, polished work that really impressed us. We’d happily work again and highly recommend them to others looking for Tableau expertise.
"We wanted to focus on business KPIs and get dashboards to transform the data into revenue. We chose Nikolay and his team because of the communication, responsiveness, and the ability to give their honest feedback."
The team performed their duties extremely professionally and efficiently. It's worth highlighting their flexible approach to resource allocation. They are ready to quickly allocate additional capacity when workloads increase. With their help, Refocus addressed all of its analytics needs across key departments—from HR to the product department. Thanks to the collaboration with Nick's team, Refocus was able to implement a data-driven approach, which significantly enhanced the efficiency of work and decision-making.
Results we've delivered
A growth-stage D2C brand with $20M in revenue was overspending on bottom-funnel channels due to last-click attribution. We implemented multi-touch attribution, built a marketing data warehouse, and delivered a unified dashboard — reducing CAC by 35% and improving ROAS to 2.1x
A $25M ARR B2B SaaS with 200 employees suffered from data silos, no single source of truth, and rising churn. We implemented a modern data stack, self-serve analytics, and a churn prediction model — improving net revenue retention from 95% to 108% in one quarter
A $12M ARR SaaS platform had zero product analytics and no A/B testing capability. We built their experimentation infrastructure from scratch — going from 0 to 15 experiments per month, improving activation rate by 23%, and cutting feature validation time by 40%
Frequently asked questions
How accurate is text-to-SQL? Can we trust the results?
With proper schema documentation and guardrails, modern text-to-SQL achieves 85-95% accuracy on typical business queries. We add multiple safety layers: the generated SQL is shown to the user before execution, results include confidence scores, and complex queries are flagged for analyst review. We also implement query cost limits and read-only access to prevent any data modification. The goal isn’t to replace analysts — it’s to handle the routine 80% so analysts focus on the complex 20%.
Does this work with our existing data stack?
Yes. We integrate with the major data warehouses (Snowflake, BigQuery, Redshift, PostgreSQL, Databricks), BI tools (Looker, Tableau, Power BI), transformation layers (dbt), and orchestrators (Airflow, Dagster). We add the AI layer on top of your existing infrastructure — no migration required.
What about data security? Does our data go to OpenAI/Anthropic?
You control the data flow. For most clients, we use enterprise API agreements with zero data retention — your queries are processed and immediately discarded by the model provider. For sensitive environments, we deploy self-hosted models that run entirely within your infrastructure. Either way, we implement column-level access controls so the AI can only query data the user is authorized to see.
How long does implementation take?
A basic text-to-SQL interface on a well-documented data warehouse takes 3-4 weeks. Adding automated reporting, anomaly detection, and a semantic catalog is typically a 2-3 month engagement. We start with a focused pilot on one domain (e.g., marketing analytics or financial reporting) and expand from there.
What ROI should we expect?
The math is straightforward. If your analytics team spends 40% of their time on ad-hoc requests and you reduce that to 10%, you’ve freed up 30% of your team’s capacity for strategic work — without hiring. Add automated reporting (saving 5-10 hours/week per analyst) and faster decision-making from instant data access. Most clients see positive ROI within 2-3 months of deployment. We set baseline metrics before we start so the impact is measurable, not theoretical.
From our blog
How to set up dbt Labs' official MCP server with Claude Code, including a real legacy-project audit story, the read-only pattern, Local vs Remote tradeoffs, and the gotchas dbt Labs has not fixed yet
How to set up Fivetran's official MCP server with Claude Code, with a real client case study (HelpScout to BigQuery), the read-only safety pattern, and the gotchas worth knowing
Practical guide to wiring Google's MCP Toolbox for Databases into Claude Code for BigQuery work. Setup commands, the .mcp.json block, all nine prebuilt BigQuery tools, custom tools.yaml, and how to lock it down for production
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