AI is Only as Reliable as the Data Underneath It.
Most AI pilots break on the data layer, not the model. CreateOS forward-deployed engineers build the data foundation that makes AI reliable in production: clean data, fast retrieval, custom models your team owns, and automated model operations. Everything runs on the unified AI execution layer, with lineage tracked from systems of record into every decision.
- ISO 27001 and SOC 2 Type II certified
- Lineage on every decision
- Custom models you own outright
- Governed and auditable by default
The Gap is the Data Layer, Not the Model
AI pilots break on dirty data, missing lineage, and pipelines that were never built for production. CreateOS builds the data foundation underneath, so the models, agents, and features built on top of it actually hold.
of enterprise AI pilots never reach production.
MIT NANDA, 2025
of enterprise teams admit their AI governance is deficient, even as 87% feel ready to deploy.
Delinea, 2026
average breach cost in financial services, where ungoverned data pipelines are one of the leading exposure points.
IBM, 2025
What We Deliver
A governed data and ML stack built for production from the first call, not a proof-of-concept that stalls at scale.
Ingestion, cleaning, and labeling pipelines
We design and ship data pipelines that pull from your systems of record, clean and validate at ingestion, and produce labeled, audit-ready datasets. Lineage is tracked from source to output.
Vector databases and embedding infrastructure
We select, deploy, and tune vector databases for your retrieval workloads, including embedding generation, indexing strategy, and access controls so search is accurate and governed.
Custom model training and fine-tuning
We train or fine-tune models on your data for your specific task. You own the weights outright. Training runs are tracked, versioned, and reproducible.
MLOps and model lifecycle management
We build the infrastructure to monitor, version, retrain, and redeploy your models without downtime. Drift detection and performance alerts fire before quality degrades.
Data lineage and provenance
Every inference traces back through the pipeline to its source data. Lineage is stored, queryable, and ready to hand to a regulator or auditor on request.
Governance and audit infrastructure
Access controls, data masking, retention policies, and audit logs are built into the stack from day one. Compliance is a property of the pipeline, not a check at the end.
How an Engagement Works: The Production Path
A staged path from concept to governed production, with a Data Trust foundation that tracks lineage from systems of record into every decision. Value lands early and governance holds at every step.
- 01
Discover
We audit your current data landscape: sources, quality, gaps, and the AI use case at the top of the list. We produce a build spec, a data quality baseline, and a production roadmap. Fixed pricing agreed in writing.
- 02
Prove
We build a scoped pilot on the execution layer with real data, lineage tracked from the first record. The goal is a working data or ML pipeline against your own systems, not a demo on sample data.
- 03
Productionize
Forward-deployed engineers harden the stack: pipeline monitoring, model validation, drift detection, access controls, and a full audit trail that holds up under security review.
- 04
Scale
It goes live, then extends. Model lifecycle management, retraining schedules, and governance tooling stay on the layer you keep. The data foundation the rest of your AI depends on.
What We Put into Production
Common data and ML workloads we take from pilot to production, each with lineage tracked and governance built in.
Fraud-alert investigation pipelines
Data pipelines designed to gather transactions, account history, and prior cases behind each fraud alert, scored against policy and handed to an analyst as a decision-ready file. Designed to cut the manual assembly that slows every alert review.
Demand forecasting models
Custom demand forecasting trained on your own sales and seasonal history, with visible assumptions so planners can adjust. Designed to reduce the over-production and stockouts that come from spreadsheet-based forecasting.
Predictive maintenance from sensor data
Models trained on your equipment sensor history to flag anomalies before they become failures. Designed to surface early warning signals so maintenance can act before unplanned downtime occurs.
Production yield analysis
Pipelines that correlate production and quality data to locate where yield drops and which factors drive it. Designed to surface root causes that typically take weeks to track down manually.
Risk scoring and credit model infrastructure
Custom risk and credit models trained on your own data, with full lineage tracked from input features to score output. Models you own outright, with explainability output that maps to your regulatory reporting requirements.
Embedding and vector infrastructure for RAG
Full retrieval infrastructure: document chunking, embedding generation, vector indexing, and access-controlled search. Designed so retrieval-augmented generation applications return accurate, auditable answers from your own knowledge bases.
Data quality and validation pipelines
Automated checks that run at ingestion: schema validation, anomaly detection, completeness checks, and flagging for human review. Data quality is enforced before it reaches a model or a report.
Model monitoring and drift detection
Continuous monitoring infrastructure that tracks model performance in production and fires alerts when output distribution or accuracy degrades. Retraining triggers are explicit and logged.
Regulatory and audit data lineage
Lineage tracked from raw data sources through transformations to every model output or decision. Queryable lineage records designed to satisfy regulator and auditor requests without manual reconstruction.
Why CreateOS for Data and ML
Lineage and provenance on every decision
Every output traces back through the pipeline to its source data. Lineage is stored, queryable, and ready to hand to a regulator, auditor, or your own engineering team.
Custom models you own outright
All model weights, training runs, pipelines, and IP are yours outright at the end of the engagement. We document everything and train your team to run what has been built.
The data foundation the rest of AI depends on
Agents, RAG systems, and AI features break when the data underneath them is dirty or untracked. We build that foundation first, so everything built on top of it holds.
Governed from day one
Access controls, data masking, retention policies, and audit logs are in the stack from the first pipeline, not added before the compliance review.
Common Questions
Do you build custom models or just data pipelines?
Both, depending on what the use case requires. Some engagements are primarily data pipeline work: ingestion, cleaning, lineage, and vector infrastructure. Others include custom model training or fine-tuning on top of that foundation. We scope what is needed and agree it in writing before any build begins.
How do you handle data quality and lineage?
Data quality checks run at ingestion: schema validation, completeness, and anomaly detection. Lineage is tracked from source systems through every transformation to the model output or decision. The lineage record is stored and queryable, so any output can be traced back to its source data without manual reconstruction.
Which vector databases do you work with?
We work with the vector database that fits your retrieval workload, latency requirements, and existing infrastructure. We do not recommend a single vendor across every engagement. Selection is driven by your data volume, query patterns, and the access controls your security team requires.
How do you handle model monitoring and retraining?
We build continuous monitoring infrastructure into the stack: performance tracking, distribution shift detection, and explicit retraining triggers that fire before quality degrades. Retraining decisions are logged. Deployments run with versioning so a previous model can be restored if a retrained version underperforms.
What does a data and ML engagement cost?
Engagements run on fixed-scope pricing, not open-ended retainers. A discovery sprint and scoped build spec is agreed in writing before any pipeline or model work begins. Cost depends on data volume, the number of sources, whether custom model training is included, and deployment mode.
How long does it take to reach production?
A scoped data pipeline pilot can reach production in weeks. Engagements that include custom model training, evaluation, and MLOps infrastructure typically run 12 to 16 weeks across phased milestones. Timeline depends on data availability and integration depth.
Who owns the IP, models, and data after the engagement?
All code, model weights, training pipelines, data schemas, and IP are yours outright. We document everything and train your team to manage and extend what has been built. We do not retain any access to your data or models after the engagement closes.
Where do you want to start?
Bring one stuck data or ML pilot. We will take it to governed production on the execution layer.