AI Agents & Automation
Autonomous AI workflows that connect your CRM, databases, and internal tools
What Are AI Agents and Why Do They Matter Now?
AI agents go beyond chatbots. They connect to your actual systems — CRM, databases, project management, communication tools — and execute multi-step workflows autonomously. Not just answering questions, but gathering data, making decisions, taking action, and reporting results.
We build on the same architecture we use ourselves: OpenClaw, an open-source AI operating system running 60+ automations — from morning briefs and meeting prep to lead monitoring and automated reporting. It is built on Claude Code with MCP integrations, runs on a simple VPS, and saves 10+ hours per week.
Every agent we build includes human-in-the-loop controls. Agents escalate to people for high-stakes decisions, ambiguous situations, or when confidence is low. Production-grade means error handling, retry logic, audit trails — not a Jupyter notebook demo.
AI agents work best when built on a solid foundation. That is why we often pair this with AI strategy consulting to identify the right use cases, and AI-powered data analytics to feed agents with reliable data. For companies that need broader data leadership, our Fractional CDO service covers the full scope — from strategy through execution. Need your engineering team to build and maintain agents internally? See our AI Developer Tools service for team enablement.
What AI Agents Deliver
Agents connected to your actual stack via MCP — 50+ system integrations
Autonomous end-to-end workflows: gather, analyze, decide, act, and report
Human-in-the-loop controls for high-stakes decisions and edge cases
Production-grade reliability with error handling, monitoring, and audit trails
Smart model routing keeps API costs predictable without sacrificing quality
Start with one workflow, prove value, expand — no big-bang required
Problems & Solutions
"Our team spends hours on repetitive reporting"
We build agents that query your databases and APIs on schedule, generate formatted reports with narrative summaries, and deliver them to Slack, email, or dashboards. Same analyst quality, zero manual effort.
"Customer support is drowning in tier-1 tickets"
An AI agent reads incoming tickets, checks your knowledge base, drafts responses for routine issues (human-approved before sending), and routes complex cases to the right specialist. Handles 40-60% of tier-1 volume.
"Nobody notices data quality issues until it's too late"
AI agents continuously monitor your data pipelines for anomalies: unexpected nulls, volume changes, schema drift, freshness violations. When something breaks, the agent diagnoses the likely cause, creates a Jira ticket with context, and alerts the right engineer — not just a generic PagerDuty alarm.
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
What is MCP and why does it matter?
MCP (Model Context Protocol) is an open standard created by Anthropic that lets AI models securely connect to external tools and data sources. Think of it as a universal adapter: instead of building custom integrations for every AI-to-tool connection, MCP provides a standardized way for agents to read databases, call APIs, manage files, and interact with SaaS platforms. It’s what makes modern AI agents practical rather than theoretical.
How do you prevent AI agents from making mistakes?
Three layers of protection. Confidence thresholds: agents escalate to humans when they’re uncertain. Action constraints: agents can read freely but need approval for write operations above certain thresholds (e.g., sending emails to customers, modifying production data). Audit trails: every decision and action is logged, so you can review what the agent did and why. We tune these controls per use case during deployment.
How long does it take to build a custom AI agent?
A single-workflow agent (e.g., automated reporting from one data source to Slack) typically takes 2-3 weeks. Multi-step agents with several integrations take 4-8 weeks. Complex systems with multiple agents coordinating across processes can take 2-3 months. We always start with the simplest version that delivers value, then iterate.
What's the difference between AI agents and traditional automation (like Zapier)?
Traditional automation follows rigid if-then rules. AI agents can handle ambiguity, interpret unstructured data (emails, documents, images), make judgment calls, and adapt to situations they haven’t seen before. A Zapier workflow breaks when the input format changes slightly. An AI agent reads the new format, understands the intent, and continues working. That said, we use traditional automation where it’s sufficient — AI agents are for the messy, judgment-heavy tasks.
What does ongoing maintenance look like?
AI agents need monitoring, not babysitting. We set up dashboards tracking success rates, error patterns, cost per execution, and drift detection. Monthly reviews cover performance metrics and optimization opportunities. Most agents need minor prompt adjustments every 2-3 months as your business processes evolve. We offer maintenance retainers or train your team to handle it internally.
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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