ML tools for
developers who build,
not just researchers who experiment.
We are building a set of tools that make it practical for development teams to train, evaluate, fine-tune, and deploy machine learning models — without needing a dedicated ML infrastructure team.
This is early. We are sharing what we are working toward so you know where Cortiqa is headed. Nothing on this page is available yet.
Join the waitlistWhere we stand
Right now, Cortiqa's focus is on Cordenex and Corde. We do not have our own models yet. We are working on that.
As we build our own model infrastructure, we are learning what it takes to train, evaluate, and deploy ML systems. The tooling gap in this space is real — most ML tools are built for researchers at large companies, not for developers at startups and mid-size teams who want to use ML in their products.
We plan to take what we learn from building our own ML pipeline and turn it into tools that other teams can use. That is the long-term vision for this product line.
The problem we see
If you are a developer and you want to add ML to your product today, you have two options. Use a third-party API and accept the limitations. Or build your own ML pipeline, which means learning a completely different stack, managing GPU infrastructure, dealing with training pipelines, and figuring out deployment — all before you write a single line of product code.
The second option is what ML engineers at large companies do. They have dedicated teams, custom infrastructure, and years of experience. Most product teams do not have any of that.
We want to make the second option accessible to teams that do not have ML engineers on staff.
Roadmap
Building our own ML infrastructure
We are developing the training pipeline, evaluation framework, and deployment systems needed for our own models. This is internal work that directly informs what the external tools will look like.
Internal tooling matures
As our internal tools stabilize, we will begin abstracting them into reusable components. The goal is a set of tools that are opinionated enough to be useful but flexible enough to work with different model architectures and data types.
Private beta for ML tools
Early access for teams that want to train, fine-tune, and deploy custom models. CLI-first workflow that integrates with Cordenex. Managed compute and self-hosted options.
Public launch
General availability with documentation, managed infrastructure, pricing tiers, and enterprise support. A complete ML development workflow accessible to any engineering team.
Planned tools
These are planned capabilities. Nothing listed here is available yet. Specifics will change as development progresses.
Model training
Train custom models from your own data. Upload datasets, configure training parameters, and start runs from the CLI or dashboard. Managed GPU compute so you do not need to provision infrastructure.
Fine-tuning
Take existing open-source models and fine-tune them on your specific data. Useful for teams that need domain-specific performance without training from scratch. LoRA and full fine-tuning support planned.
Evaluation and benchmarking
Run your models against standard and custom benchmarks. Compare performance across versions. Track metrics over time. Automated evaluation pipelines that run on every training completion.
Dataset management
Upload, version, and manage training datasets. Built-in tools for data cleaning, deduplication, and quality filtering. Dataset versioning tied to model versions for full reproducibility.
Model deployment
Deploy trained models as API endpoints with a single command. Auto-scaling, load balancing, and version management included. Deploy to our infrastructure or export to your own.
Experiment tracking
Log training runs, hyperparameters, metrics, and artifacts automatically. Compare experiments side by side. Share results with your team. No manual tracking spreadsheets.
What it will look like
CLI-first workflow, consistent with how Cordenex works. Manage your entire ML pipeline from the terminal.
How it connects
ML Tools will integrate with the rest of the Cortiqa ecosystem. Each product addresses a different part of the development workflow.
Cordenex
Use Cordenex to write the data processing scripts, evaluation code, and integration logic for your ML pipeline. Cordenex understands ML frameworks and can generate training configurations.
Corde
Ask Corde to summarize training run results, compare model versions, draft documentation for your model, or search your experiment history. Corde connects to ML Tools data.
Cortiqa API
Models trained and deployed through ML Tools will be accessible through the same Cortiqa API. One authentication system, one billing system, one set of SDKs.
Analytics Dashboard
Training costs, compute usage, model performance metrics, and deployment statistics will appear in the existing dashboard. One place to track everything.
Who this is for
This is being built for
- Product teams that want to add ML features without hiring ML engineers
- Developers who understand code but are new to model training
- Startups that need custom models but cannot afford dedicated ML infrastructure
- Teams that want to fine-tune open-source models on their own data
- Companies that need to keep training data and models on their own infrastructure
This is not being built for
- ML researchers who need maximum flexibility and custom training loops
- Teams that already have mature ML infrastructure and dedicated MLOps engineers
- Use cases that require training models with billions of parameters from scratch
- Academic research that needs fine-grained control over every aspect of training
Planned technical details
Compute and infrastructure
- Managed GPU compute — no infrastructure to provision
- A100 and H100 instances for training
- Auto-scaling inference endpoints for deployment
- Self-hosted option for enterprise customers
- Pay-per-use pricing for compute
- Training job queuing with priority tiers
Supported model types
- Transformer-based language models
- Code generation and understanding models
- Embedding models for search and similarity
- Classification and regression models
- Open-source base models (Llama, Mistral, etc.)
- Custom architectures via configuration
Planned framework support
Data and privacy
ML training involves sensitive data. Our planned approach to data handling for ML Tools follows the same principles as the rest of Cortiqa.
Planned commitments
- Your training data is yours — we will not use it to train our own models
- Your trained models are yours — full ownership and export rights
- Datasets encrypted at rest and in transit
- Self-hosted option for organizations that cannot send data externally
- Dataset deletion on request with cryptographic verification
- No data sharing between customers under any circumstances
- Compliance with GDPR, HIPAA, and other frameworks for training data handling
How it will compare
Planned positioning relative to existing tools. Subject to change.
| Cortiqa ML Tools | DIY (PyTorch + cloud) | Enterprise ML platforms | |
|---|---|---|---|
| Target user | Developers | ML engineers | ML teams + data scientists |
| Infrastructure management | Managed | You manage everything | Managed |
| Setup time | Minutes | Days to weeks | Weeks to months |
| CLI-first workflow | Yes | Varies | Usually GUI-first |
| Integrates with coding tools | Yes (Cordenex, Corde) | No | No |
| Self-hosted option | Planned | By definition | Some |
| Pricing model | Pay-per-use compute | Cloud compute costs | Platform license + compute |
| Minimum team size | 1 developer | 1 ML engineer | Typically 5+ |
Common questions
When will ML Tools be available?
We do not have a timeline. Our own model development is the prerequisite, and that work is ongoing. We will announce a private beta when we are ready. Join the waitlist to be notified.
Do I need ML experience to use these tools?
That is the goal. If you can write code and understand basic concepts like training data, model accuracy, and API endpoints, you should be able to use ML Tools without a PhD in machine learning.
Will I be able to train large language models from scratch?
Initially, no. Training LLMs from scratch requires enormous compute resources and data. We are focusing on fine-tuning existing open-source models and training smaller, task-specific models. These cover the majority of practical use cases.
Can I use my own training data?
Yes. Your data is the entire point. Upload your datasets, fine-tune models on them, and deploy models that understand your specific domain. Your data stays yours and is never used for anything else.
Will there be a free tier?
We plan to offer a free tier for experimentation. Compute costs for training will be usage-based. The exact pricing model has not been finalized.
How does this relate to the Cortiqa API?
Models you train and deploy through ML Tools will be accessible through the Cortiqa API. You can use the same API to access both Cortiqa's models and your own custom models. One interface for everything.
ML should be a tool
in every developer's toolkit.
Not a separate discipline.
We are working toward a future where any developer can train, evaluate, and deploy custom models as naturally as they deploy a web application. It will take time. We are building it right.