AI Strategy & Implementation
Identify where AI moves the needle and build the implementation plan
What Does AI Strategy Consulting Actually Look Like?
Most AI initiatives fail because companies start with tools instead of problems. They buy licenses, run demos, and six months later nobody uses it. We take the opposite approach: start with your data, your processes, and your actual bottlenecks.
We see this pattern constantly: a startup connects AI to their database, gives teams natural-language access — and the AI hallucinates. Without data quality, semantic layer, and clear metric definitions, AI tools generate confident-sounding wrong answers. We fix the foundation first, then layer intelligence on top. This is where our data strategy consulting and data governance expertise becomes critical — the same frameworks we use across all engagements.
We help you navigate the real decisions: Claude vs GPT vs open-source, build vs buy, cloud vs self-hosted. We model costs honestly — including compute, maintenance, and the change management that most vendors leave out. The result is a concrete plan with clear ROI, not a slide deck that sits on a shelf.
Once your strategy is set, we help execute: from building AI agents that automate workflows, to deploying AI-powered analytics that make your data accessible to every team, to rolling out AI developer tools that accelerate your engineering team. Many clients start with AI strategy as part of a broader Fractional CDO engagement.
Why Companies Work With Us on AI Strategy
Platform-agnostic — we test Claude, GPT, Gemini against your actual use cases
Honest ROI modeling with real costs — compute, integration, change management
AI governance with prompt security and data privacy built in from day one
Quick wins in 4-6 weeks while building long-term strategic capabilities
Cost control via model routing, caching, and per-team token budgets
Change management that drives 60%+ sustained adoption, not forgotten training
Problems & Solutions
"We connected AI to our database but it keeps hallucinating"
We audit your data foundation first — metric definitions, schema documentation, access patterns. Then we build a semantic layer that gives AI the context it needs to produce accurate answers. Our clients go from unreliable AI outputs to trusted self-service analytics.
"We don't know which AI platform to bet on"
We benchmark models against your actual data and use cases — not generic benchmarks. Our evaluation covers accuracy, latency, cost per query, data residency, and vendor lock-in risk. Most clients end up using 2-3 models for different tasks, optimizing both quality and cost.
"Our AI costs are unpredictable"
We implement model routing — expensive models for complex tasks, cheap ones for simple queries. Add response caching, token budgets per team, and spending dashboards. Typical result: 40-60% reduction in API costs with no quality degradation.
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 long does an AI readiness assessment take?
Typically 2-4 weeks. We interview key stakeholders, audit your data infrastructure, evaluate current tool usage, and identify high-impact use cases. The output is a prioritized roadmap — not a 100-page report nobody reads.
Should we use Claude, GPT, or open-source models?
It depends on your use cases, data sensitivity, and budget. Claude excels at long-document analysis and careful reasoning. GPT-4o is strong for multimodal tasks and has the broadest ecosystem. Open-source models like Llama make sense when you need full data control or have high-volume, lower-complexity tasks. Most organizations benefit from a multi-model approach.
What does AI governance actually involve?
Three layers: data governance (what data can AI access, where is it processed, retention policies), access governance (who can use which models, approval workflows for production deployment), and output governance (quality monitoring, hallucination detection, human-in-the-loop requirements). We build the policies and the technical controls to enforce them.
How do you measure AI ROI?
We track three categories: time savings (hours reclaimed per week per team), quality improvements (error rates, consistency scores), and revenue impact (faster time-to-market, new capabilities). Every use case gets baseline metrics before deployment and ongoing measurement after. If the numbers don’t justify the cost, we say so.
Can you work with our existing data team?
That’s our preferred model. We work alongside your analysts, engineers, and product managers — not as a replacement. The goal is knowledge transfer: your team should be able to maintain and extend everything we build. We typically engage for 3-6 months and leave behind documentation, runbooks, and trained internal champions.
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
Related Services
Cloud Data Services and Cloud Migration Solutions
Migrate to cloud, cut infrastructure costs, and unlock elastic scale
→Data Analytics Engineering Services
Clean pipelines, tested transforms, and analytics-ready data — every morning
→Data Visualization Consulting Services
Clear, interactive visualizations that turn complex data into action
→