Data analytics and AI implementation

Working data systems for teams that have no patience for slideware.

Zetta Clarity helps senior leaders modernize analytics, strengthen data foundations, and ship AI and Agentic AI use cases that survive real data, real users, and real operating constraints.

Talk Through a Data Problem Review an AI Workflow

Business-first scoping We start with the decision, workflow, or operating question before recommending a platform, dashboard, model, or agent.

Senior technical execution Strategy only matters if it can be built. We connect roadmap work to pipelines, governance, BI, AI, and adoption.

Fluent in the modern data stack We work inside the tools your team already uses: Databricks, Snowflake, ClickHouse, AWS, Azure, Power BI, Tableau, dbt, Atlan, and modern AI stacks.

Problems worth solving

The work usually starts where teams are already frustrated.

Senior buyers do not need another AI pitch. They need a clear path from business problem to reliable data product, dashboard, model, or automated workflow.

Dashboards no one trusts

Different teams use different metric definitions, data refreshes fail quietly, and executive reviews turn into debates about numbers.

Manual reporting drag

Analysts spend too much time moving data, cleaning extracts, and rebuilding recurring reports instead of answering better questions.

AI ideas without a business case

Use cases sound plausible, but the data, workflow, owner, risk, and expected return have not been made concrete.

Systems struggling to scale

Current platforms and data workflows exist, but speed, reliability, cost, or performance cannot keep up with business expectations.

What we do

Services built around execution.

Data Strategy and Roadmaps

Prioritize the decisions, workflows, metrics, and platform work that will actually change performance.

Analytics Modernization

Adopt modern BI practices, cloud-native tooling, reusable patterns, and scalable delivery models so analytics can evolve faster.

Data Engineering and Integration

Design pipelines, models, quality checks, orchestration, and integrations across operational and cloud systems.

Predictive Analytics and Machine Learning

Build forecasting, classification, optimization, anomaly detection, and decision models tied to real operational choices.

Generative AI, Agents, and RAG

Design grounded assistants, retrieval systems, workflow agents, and automation patterns with clear evaluation and controls.

Metric Governance and Semantic Layer

Define trusted business metrics, ownership, semantic models, and controls so dashboards answer the same question the same way.

Platforms we support

We build on the stack your teams already use.

Databricks

Snowflake

ClickHouse

AWS

Azure

Google Cloud

Power BI

Tableau

dbt

Fivetran

Confluent

Atlan

AI and agentic automation

AI built around real work, not isolated experiments.

AI is not only about prompts or prototypes. Useful systems connect to the work, data, tools, review paths, and operating rhythms that help them survive daily use and produce trusted results.

Review an AI Use Case

Agentic AI workflows

Design agents that can retrieve context, call tools, route tasks, escalate exceptions, and operate inside clear business boundaries.

Knowledge and retrieval systems

Build trusted search and retrieval over documents, dashboards, policies, tickets, CRM records, and operational data.

Predictive decision support

Use forecasting, scoring, anomaly detection, and classification where prediction improves a specific decision or workflow.

Production-ready AI systems

Track quality, cost, adoption, exceptions, and user feedback so AI systems stay reliable after launch.

Outcomes

Every engagement should change how work gets done.

Fewer manual reporting hours

Recurring reporting gets automated, documented, and monitored.

Trusted operating metrics

Metric definitions are agreed, visible, and consistently produced.

More reliable pipelines

Failures are caught early and data quality issues stop surprising executives.

AI and agent workflows with owners

Each candidate has a workflow, data source, risk profile, review path, and success measure.

Case notes

Representative work, stated plainly.

These are the types of problems we are brought in to solve: visible enough for leadership, technical enough to require experienced builders, and operational enough that the solution has to hold up after launch.

Customer analytics

Behavioral segments made growth decisions more concrete.

Problem: rich activity, value, and promotion data existed, but teams lacked usable behavioral segments.

Work: engineered 90-day features, reduced them into interpretable drivers, and clustered users into profiles.

Result: leaders could see where value concentrated and which segments needed different growth, retention, or product actions.

Read the case note Funnel analytics

Activation analysis turned conversion leaks into acquisition decisions.

Problem: the team knew registration and first transaction were connected, but not where users were dropping.

Work: mapped the funnel through identity verification, quantified drop-offs, and tested timing, market, and age-band signals.

Result: leaders could recommend better activation windows and consider incentives for affiliates bringing higher-fit traffic.

Read the case note AI discovery agent

An AI agent accelerated the first touch of strategy discovery.

Problem: strategy intake was spending too much time on baseline data landscape questions.

Work: built an agent that guided stakeholders, reviewed source documents, and asked targeted follow-ups.

Result: consultants could begin with a structured landscape brief and spend live time on priorities.

Read the case note

How we engage

Outcome-led scopes. Useful artifacts. No theater.

We scope around outcomes, not activity.

Before work expands, we agree on the operating outcome, the artifact that proves progress, and who will own it after launch.

2-4 weeks

Assessment

Review data maturity, platform health, reporting trust, AI candidates, cost, performance, and adoption blockers.

Roadmap

Prioritization

Rank work by business value, feasibility, data readiness, risk, dependency, and time-to-impact.

Design

Define the target architecture, data products, workflows, owners, success measures, and delivery sequence.

Build

Implementation

Ship dashboards, pipelines, platform assets, AI workflows, predictive models, and operating documentation teams can use.

We will not tell you every problem needs AI or an agent. Sometimes the right answer is a cleaner data model, a better metric definition, a reliable pipeline, or a dashboard people can finally trust.

Start with the specific problem

Bring us into the working details.

Tell us what is breaking, slow, distrusted, expensive, or underused. We will help decide whether the answer is analytics, engineering, governance, Agentic AI, or a simpler fix.

Email Zetta Clarity Call +1 (571) 577-9702