AI & Advanced Analytics
Predictive models, real-time pipelines and trusted data, built with Kafka, Spark and Flink.
MBE certified (Minority Business Enterprise)
Overview
About AI & Advanced Analytics
Most organizations collect far more data than they use. Desmata builds the pipelines, models and platforms that turn it into forecasts, alerts and decisions. We start with the question the business needs answered, then work back to the data.
For data that cannot wait for a nightly job, we design real-time stream processing on Kafka, Spark and Flink. For everything else we build governed lakes and warehouses with tested transformations in dbt, so people trust the numbers they see.
Machine learning and NLP are delivered as working services, with monitoring in place from the first release. We can embed AI directly in your processes, for example to route documents, flag exceptions or support a decision.
Start with a conversation
Tell us what you are working on. A member of the Desmata team will reply by email or phone.
Call us on +1 (214) 407-8089Outcomes
What you get
Answers while they still matter
Streaming pipelines surface events in seconds, so teams can act on a stockout, a fraud signal or a failing device, not read about it tomorrow.
Forecasts you can test
Predictive models ship with documented features, validation results and a baseline to beat.
Data people trust
Quality checks, lineage and agreed definitions mean two departments no longer bring two different numbers to the same meeting.
Less manual processing
Embedded AI handles routine classification and extraction, and sends the hard cases to a person.
Capabilities
What we do, in detail
Predictive modeling and machine learning
Models built around a decision, not a technique.
- Demand, churn, risk and propensity models
- Feature engineering and model validation
- Deployment as APIs or batch jobs with MLOps pipelines
- Drift monitoring and retraining schedules
Natural language processing
Text and documents turned into structured data.
- Document classification and entity extraction
- Summarization and semantic search
- Retrieval-augmented generation over internal content
- Evaluation sets that measure quality before launch
Real-time stream processing
Event data processed as it arrives.
- Event pipelines on Apache Kafka
- Stateful stream processing with Apache Flink
- Spark Structured Streaming for unified batch and stream jobs
- Alerting and live views fed from the stream
Data engineering and platforms
Secure, governed storage that scales with volume.
- Lake, lakehouse and warehouse design
- ELT with dbt and orchestration with Airflow
- Snowflake, Databricks, BigQuery and Redshift
- Data catalog, lineage and access control
Embedded AI and decision intelligence
Models placed where work happens.
- Intelligent document processing
- Rules plus model scoring for approvals and triage
- Human-in-the-loop review queues
- Decision logs for audit and review
Approach
How an engagement runs
Step 1: Frame the question
We agree the decision to improve, the metric that shows it improved and the data it needs.
Step 2: Assess the data
We profile sources for completeness, freshness and bias, and decide what must be fixed before modeling starts.
Step 3: Build the pipeline and the model
Pipelines, transformations and models are built together, with tests and versioned code.
Step 4: Put it into use
Results go to the dashboard, application or alert where the decision is made, with a clear owner for each.
Step 5: Monitor and improve
We track data quality and model performance after launch and tune both as your business changes.
Technologies and standards
What we work with
Tools, platforms, frameworks and standards our teams work with and design for. Standards are context for the work, not certifications held by Desmata.
Streaming
- Apache Kafka
- Apache Spark
- Apache Flink
- Kafka Connect
- Amazon Kinesis
Warehouse and lakehouse
- Snowflake
- Databricks
- Google BigQuery
- Amazon Redshift
- Azure Synapse
Transformation and orchestration
- dbt
- Apache Airflow
- Dagster
- Great Expectations
Machine learning and NLP
- Python
- scikit-learn
- PyTorch
- Hugging Face
- MLflow
- spaCy
Reporting
- Power BI
- Tableau
- Looker
- Apache Superset
Engagement models
Ways to work with us
Analytics project
A defined use case, from data assessment to a working pipeline or model in production.
Data platform build
A governed lake or warehouse designed, built and handed over, with documentation.
Embedded data team
Data engineers and scientists work as part of your team on a continuing roadmap.
Staff augmentation
Individual specialists added to an analytics team you already run.
Technology and talent
Technology and talent together
Data roles are among the hardest to hire. Our recruiters place data and AI specialists on contract or permanently, and our engineers can pair with them on the platform design.
Roles we can also staff
- Data engineer
- Streaming engineer (Kafka, Flink, Spark)
- Machine learning engineer
- Data scientist
- NLP engineer
- Analytics engineer (dbt)
- MLOps engineer
- Data architect
- Data analyst
Need the people as well as the plan?
The engineers who design the work and the specialists who join your team come from one firm.
Why Desmata
Why Desmata
Questions first, tools second
We pick Snowflake or BigQuery, Spark or Flink, based on your workload and team, not on a standard package.
Engineers and recruiters under one roof
When the roadmap needs two more data engineers, we can place them and keep delivery continuous.
Outcome-driven
Each use case has a business metric attached, such as hours saved, forecast error or decisions made faster, and we report on it.
Capacity where you need it
Offshore, nearshore and onshore delivery models let us extend a data team without a long hiring cycle.
Proof
Our work in practice
RetailGeo Insights: machine learning on location data
RetailGeo Insights, Desmata's own platform in development, applies geospatial analytics and machine-learning trend predictions to retail decisions. Demographic heatmapping and competitive proximity analysis run on scalable cloud infrastructure. It is the same data engineering and modeling practice we bring to client projects.
Questions
Frequently asked questions
Do we need a data warehouse before we can use machine learning?
Not always. Some models can run on data from a single system, but most deliver more once data from several sources is cleaned and joined. We assess this first and recommend the smallest platform that supports your use case.
When is real-time analytics worth the added complexity?
When the value of a decision falls quickly with time, such as fraud checks, inventory alerts or operational monitoring. For weekly planning or monthly reporting, batch pipelines are simpler and cheaper, and we will say so.
Can you work with the cloud platform we already use?
Yes. We build on AWS, Azure and Google Cloud, and with warehouses such as Snowflake, Databricks and BigQuery. We follow your security and naming standards and document everything we build.
How do you keep AI outputs reliable?
We test models against labeled evaluation sets before launch, route low-confidence results to a person, and monitor accuracy and drift after release. Each decision can be logged so that you can review it later.
Put your data to work
Tell us the decision you want to improve and where the data lives. A Desmata data engineer will reply with a proposed approach and the skills needed.
MBE certified (Minority Business Enterprise)