Agentic Analytics Implementation
Deploy a reliable analytics agent your business team actually uses, in 4 to 8 weeks
Why Production Analytics Agents Fail Without Context Engineering
Most analytics agents fail because teams plug a model into the warehouse and hope for the best. Out-of-the-box tools work for demos but fall over on real questions, because the bottleneck is ambiguity, not the model. A semantic layer alone does not save you, since most ambiguity lives in how metrics are defined, not how tables are joined.
We deploy production-grade analytics agents that business teams actually use. We pick the right stack for your data and team, engineer the context (not just a semantic layer), measure reliability with text-to-SQL unit tests, and roll out one domain at a time with adoption KPIs. Our own benchmark: 17% reliability baseline, 86% after context engineering, with no semantic layer required.
This builds on the foundation laid by our AI-Powered Data Analytics and AI Agents & Automation services, and pairs naturally with Fractional CDO engagements where reliable self-serve analytics is a leadership priority. Companies still mapping their AI roadmap typically start with AI Strategy & Implementation first.
Not sure where you stand? The free Agentic Analytics Readiness assessment takes 5 minutes and gives you a score across data foundation, tooling, metric clarity, user demand, and governance, plus a recommended next step before you commit to a POC.
What Production Agentic Analytics Delivers
Reliability you can measure with text-to-SQL unit tests, not vibes
Tool-agnostic stack selection across Claude with MCP, Hex, Omni, Lightdash, Cortex, and Genie
Context engineering that fixes data-model ambiguity, not just adds a semantic layer
Domain-by-domain rollout that builds trust before scaling across the company
Adoption KPIs tracked monthly, with active users and resolved requests
Recurring context maintenance that keeps the agent reliable as your schema evolves
Problems & Solutions
"Our team is overwhelmed by tool choices and vendor pitches"
We evaluate Claude with MCP, Hex, Omni, Lightdash, Snowflake Cortex, Databricks Genie, and custom builds against your actual data, BI tools, and target users. The recommendation comes with a baseline reliability number on roughly 20 unit tests, not a vendor pitch. By the end of the POC you know which tool fits, what the gap to production looks like, and whether the project is worth funding.
"Our last agent hallucinated and we lost trust"
We treat reliability the way engineers treat code. Unit tests on every model change, regression checks before rollout, and a measured number you can take to leadership. Most of the lift comes from removing ambiguity in the data model and metric definitions, not from prompt tuning. Typical path: 17% baseline to 80% or higher after context engineering, with the eval suite living in CI so regressions surface before users see them.
"We are not sure our data is clean enough for an agent"
We expect to find ambiguity. We fix what blocks reliability, document the rest, and hand you a runbook your team can extend. The agent becomes the forcing function for the data hygiene you already needed, scoped to one domain so the cleanup is bounded and visible.
Three sequential phases, not three options.
You move through the phases in order. POC qualifies the project and removes buying friction. Implementation builds the production agent. Maintenance keeps it reliable as your data evolves.
2 weeks · Tool selected, baseline measured
- Stakeholder discovery and use-case scoping
- Tool-fit assessment across Claude, Hex, Omni, Lightdash, Cortex, Genie
- Baseline reliability eval (~20 text-to-SQL unit tests)
- Live demo to 5 to 10 internal champions
- Reliability report and project plan for next phase
4 to 8 weeks · 1 domain, ~10 tables, ~50 unit tests
- Context engineering loop and rules and skills
- Data-model fixes for ambiguity and metric clarity
- Reliability target 80%+ on agreed eval suite
- Rollout to 1 domain with 5 to 15 active users
- Eval CI on every dbt model or context change
- Runbook plus knowledge transfer to your data team
Month-to-month · Cancel with 30 days notice
- Weekly review of agent conversations and failures
- Schema-change detection and context refresh
- Scope expansion: new domains, tables, question patterns
- Adoption KPIs reported monthly
- LLM cost monitoring and optimization
All engagements include: Mutual NDA from Day 1 · Self-host option · BYO LLM keys · Full IP ownership
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 the 5-minute Agentic Analytics Readiness assessment. Get a personalized scorecard across data foundation, tooling, metric clarity, user demand, and governance, with a recommended next step before you commit to a POC.
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 do you measure reliability?
We run a text-to-SQL eval suite of 20 to 50 questions tied to real business decisions. Each question has a known answer or an accepted SQL pattern. The agent passes when its output matches within tolerance. We baseline before context engineering, then iterate until we hit 80% or higher. The eval suite stays in CI: every dbt model change or context update reruns the tests, so regressions surface before users see them.
Do we need to migrate our BI tool?
No. We layer the agent on top of your existing warehouse and BI tool. Snowflake, BigQuery, Databricks, Redshift, and Postgres are all supported. Tableau, Looker, Power BI, and Metabase users keep their dashboards. We only recommend changes when your current tool is clearly blocking adoption, and we always show the cost-benefit before suggesting it.
What if our data model is messy?
That is the typical starting point, not a blocker. Implementation includes data-model fixes for whatever blocks reliability: ambiguous metric definitions, overloaded columns, dbt models that disagree, missing documentation. We do not rebuild your warehouse. We fix what the agent stumbles on, document the rest, and hand you a runbook so the cleanup keeps going after we leave.
Can we use our own LLM keys and self-host?
Yes. By default we use enterprise API agreements with zero data retention. For sensitive environments we deploy the agent inside your VPC with self-hosted models. You bring your own LLM keys, you control the data flow, and the context lives in your infrastructure. SOC 2 vendors are recommended where third-party processing is acceptable.
How fast can we see value?
POC results land in 2 weeks: tool selection, baseline reliability, demo to 5 to 10 champions. Full implementation runs 4 to 8 weeks depending on data-model maturity. Typical first measurable outcome is a 60% drop in routine ad-hoc analytics requests for the rolled-out domain within 30 days of go-live.
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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