Data & AI Consulting Services
Our data and AI consulting services help you collect reliable data, report on it consistently, and use machine learning or generative AI where it saves time or money.
Capabilities
- Data source audits and KPI definitions agreed across departments
- Tested pipelines with dbt, Airflow, Azure Data Factory or Fivetran
- Snowflake, Databricks, BigQuery or Microsoft Fabric platforms
- Power BI, Tableau and Looker dashboards on a shared semantic layer
- Machine learning and generative AI proofs of concept with go or no-go criteria
Outcome: One trusted set of numbers, and AI use cases measured against a clear baseline.
Who our AI consulting services are for
Most clients come to us with one of two problems. Either their reports disagree, because each department pulls numbers from different systems and spreadsheets, or leadership wants to use AI but does not know where it would pay off. Often both are true. Data may be spread across a CRM, an ERP, marketing tools and files on shared drives, with nobody responsible for its quality. Before any model can help, the business needs data it trusts and clear definitions of its key numbers.
From raw data to working AI: our process
We first list the decisions the business wants to improve and the data each one needs. Then we audit data sources for quality, ownership and access. Next we build pipelines that load data into a central warehouse or lakehouse, with tests that catch errors early. On top of that we create a semantic layer and dashboards with agreed definitions. Only then do we choose AI use cases, prove each one on a small scale, and move the ones that work into production with monitoring.
Data and AI technologies we use
Data platforms are usually built on Snowflake, Databricks, Google BigQuery, or Azure Synapse and Microsoft Fabric. Pipelines use dbt for transformation, with Airflow, Azure Data Factory or Fivetran to move and schedule data. Reporting is built in Power BI, Tableau or Looker. For machine learning we use Python, scikit-learn and MLflow. For generative AI we work with Azure OpenAI, Amazon Bedrock and open models, using retrieval over your own documents. The cloud setup underneath is handled with our cloud consulting services.
Engagement models and timelines
A data and AI assessment typically takes four to six weeks and ends with a ranked list of use cases and a platform plan. A first set of governed dashboards on a new warehouse often takes two to four months. An AI proof of concept usually runs four to eight weeks, ending in a go or no-go decision. We work as a fixed-scope project, an ongoing analytics team, or by adding data engineers and analysts to your staff through our talent solutions.
Measuring value and avoiding data and AI mistakes
For data work we measure report adoption, data freshness, failed pipeline runs and the time analysts spend preparing data. For AI we measure the business result each use case targets, such as hours saved per week, forecast error or response time, against a baseline. Common mistakes include building dashboards before agreeing on definitions, starting AI projects without enough clean data, and putting models live with no plan to monitor them. Regulated industries we work with, such as healthcare and finance, need extra care with personal data.
Frequently asked questions
Do you implement generative AI for businesses?
Yes, when there is a clear use case and the data is ready. Typical examples are searching internal documents, drafting replies for support teams and summarizing long reports. We test accuracy on real examples, add human review where errors are costly, and limit what data the model can see.
Can you work with our existing BI tools?
Yes. If you already use Power BI, Tableau or Looker, we usually keep the tool and fix the data underneath. Most reporting problems come from inconsistent definitions and poor source data, not the tool. We suggest switching only when licensing or features block you.
How do you keep our data private when using AI?
We prefer AI services that run inside your own cloud account and do not use your data to train public models. Access follows your existing permissions, sensitive fields can be masked, and we log what the system retrieves. We review these controls with your security team before launch.
How do we know if an AI idea is worth doing?
We score each idea on business value, data readiness, risk and effort. A good candidate has a measurable outcome, enough historical data and a team that will actually use it. We then run a short proof of concept with a success threshold agreed in advance.
Do we need a data warehouse before starting with AI?
Not always, but most AI use cases need clean, connected data. A narrow use case, such as searching a document library, can start without one. Forecasting, customer scoring and similar models usually need a warehouse or lakehouse with reliable history first.
Related services
Explore complementary capabilities that often sit alongside this engagement.
Software Development
As a custom software development company, we design, build and maintain web applications, APIs and integrations for businesses whose needs are not met by off-the-shelf products.
Learn more →Cloud & DevOps
Our cloud consulting services help teams move workloads to AWS, Azure or Google Cloud safely, automate how software is released, and keep monthly cloud bills under control.
Learn more →Digital Transformation
Our digital transformation consulting helps you replace paper, spreadsheets and outdated systems with better processes and tools, delivered in small steps that each show a measurable result.
Learn more →Looking for the full catalogue? View all technology services.
Ready to talk about this service?
Tell us about your goals. We will recommend a practical next step — consulting, delivery, or talent.