Built around your team
Who ContextFlow is for.
For AI/ML teams, data scientists and enterprise platform teams building repeatable paths from data to evaluated models and AI workflows.
ContextFlow
Enterprise intelligence. From data to delivery.
Bring data preparation, model development and AI workflows into one workspace. Explore multi cloud AutoML, LLM fine tuning and governed AI assets at enterprise scale.
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Design and review an experiment
The existing multiclass experiment shows its configuration, evaluation and model candidates. Original product capture; workspace identity relabelled for DEOS.
- Experiment: dataset, target, task and budget
- Best accuracy, candidates trained, elapsed time and rows
- Dataset Copilot proposes validated, allow listed transformations
- Leaderboard trained to optimise accuracy
- Ranked candidate models from this experiment
- Compare accuracy, train time and latency side by side
- Experiment lifecycle with version and status
- Best model and its accuracy
- Open as pipeline or download the best model
- Start a new tuning job
- Tuning jobs, models serving, GPU hours and examples trained
- Compute targets: on prem GPU, AWS, Google Cloud or Azure
- Register a governed asset once
- Search and filter assets by type and environment
- Data and model assets with versions, owners and analyses
Explore the capabilities
What ContextFlow brings to the work.
ContextFlow spans the enterprise AI/ML lifecycle: governed data assets, AutoML experiments, model comparison, LLM fine tuning, knowledge bases and AI workflows, and the operational surfaces around them.
Availability. Execution targets, cloud adapters and workflow options are enabled per deployment. DEOS confirms which are configured for your environment before you rely on them.
Multi cloud AutoML
Configure classification, regression, forecasting, clustering and drift workflows. On premises AutoGluon execution is paired with AWS, GCP and Azure adapters; the providers enabled for a given deployment are confirmed during setup.
Model comparison and leaderboards
Inspect candidate models side by side with leaderboards and feature importance, then support champion approval, endpoints, batch workflows and model cards for the selected model.
Dataset Copilot and versioned datasets
Apply validated transformations and keep immutable dataset versions, so every experiment can be traced back to the exact data it used.
Asset discovery and profiling
Register AI assets and inspect data quality, PII indicators, lineage and versions. Prepare the data foundation before running an experiment.
LLM fine tuning
Prepare and track fine tuning jobs, including LoRA and QLoRA workflows, with on premises or cloud execution depending on the compute targets configured for your environment.
Knowledge bases, workflows and tools
Build retrieval and knowledge base (RAG) workflows, orchestrate AI flows, connect integrations and tools including MCP, and run evaluations. Provider availability should be confirmed for the target environment.
Governed handoffs and operations
Use model evidence, approval workflows and metadata to support selection and review, including a handoff path to RAIA for AI assurance. Operational surfaces cover deployments, runs, approvals, activity, observability and analytics/ROI.
A practical place to start
An example starting workflow.
Register a dataset, define the modeling task, compare candidates and review the evidence before selecting the next deployment or assessment step.
See the five step processYour first digital employee starts with a job to be done
What role would change your business?
Bring us the workflow, capacity gap, risk, or business outcome you need to address. DEOS will help define the role and accountability structure.