Train. Infer. Govern. Everywhere.
XAIOps centralizes the entire MLOps lifecycle, from data ingestion through model training, deployment, and monitoring. It integrates your data, models, and compute infrastructure with built-in governance and a comprehensive audit trail, ensuring compliance is part of the workflow rather than an afterthought.
- One platform from data ingestion through model deployment and monitoring
- Manage workloads across heterogeneous computes from a single interface
- Platform-agnostic model training and inference
- Built for medical imaging, sequence, and genomic data management
- Analyze data with AI in natural language
- Scan models/codes before it enters your environment
- Deploy LLMs on Laptop, workstation, or cluster and create endpoints with a single click
- Every action governed and recorded, as evidence you can hand to a reviewer
- Expedite scientific discovery and publish your work faster than ever before
- Runs on your infrastructure: on-premises, private cloud, hybrid, or air-gapped
Compute backends
- hpc-prod128 GPU
- hpc-research64 GPU
- containers24 nodes
- workstation2 GPU
Active workflow
- Dataset registeredimaging-cohort-04
- Assurance passed0 findings
- Training on hpc-researchepoch 34 / 50
- Endpoint queuedinference
1,284
requests / hr
0
policy bypasses
100%
audit integrity
One
Control plane
Data, models, and compute under a single permission and audit model
Any
Compute you own
HPC schedulers (Slurm, IBM Spectrum LSF, etc.), Kubernetes, workstation/VM, Laptop
Zero
Data egress required
Operates entirely inside your network, air-gapped sites included
Full
Audit coverage
Every dataset access and model action recorded as evidence
Where data, model, and platform finally meet
The three things every AI project needs usually live in three different worlds: a storage team, somebody's notebook, and a scheduler queue. XAIOps puts them on one plane under a single permission model, so the handoffs between them stop being manual, undocumented, and slow.
Data
Cataloged, permissioned, understood
- One catalog for every dataset, whatever shape it takes
- Medical imaging, sequence, and genomic data as first-class citizens
- Access scoped to the people and teams who should have it
- A complete record of who used what, and when
Model
Trained, assured, versioned, served
- Training across the task types research actually needs
- Experiment history captured without extra bookkeeping
- Reproducible packaging, so a result can be rebuilt later
- The same model served offline in bulk or live to an application
Platform
Whatever compute you own
- HPC schedulers, Kubernetes, GPU servers, and workstations
- One submission flow regardless of where the work lands
- Capacity chosen at submit time, not baked into a script
- Infrastructure credentials held centrally, never by end users
The control plane
Where the three axes converge: identity, permissions, review, safety policy, usage accounting, and audit - defined once and applied to everything above.
Four verbs, one platform
XAIOps is a single, unified platform—not a collection of disconnected products with separate logins and fragmented permissions.
Train
On whichever cluster has capacity
Imaging, tabular, sequence, time series, generative, and language tasks - including fine-tuning - submitted to the compute you own without rewriting anything for each environment.
- No-code/low-code/full-code model training on any compute
- Experiment history captured as you work
- Custom architectures where the standard ones fall short
- A finished result promoted to a serving artifact in one step
- Prepare materials for peer review and publishing in a single click
Infer
In bulk, live, and behind one door
Run large offline jobs on your scheduler, publish low-latency endpoints for interactive work, and put a single governed entry point in front of everything you expose.
- Deploy models/LLMs for batch or real-time use with a single click
- Run inference directly from the study/dataset you are looking at
- Inference runs on the cluster/workstation/laptop you already operate
- Interactive endpoints with health tracking
Govern
Enforcement, not documentation
Review, assurance, safety screening, usage accounting, and audit are positioned on the request and promotion path. A control that fails has consequences instead of producing a warning nobody reads.
- Nothing reaches production without passing review
- Model supply chain security: Scan every model for hidden code, backdoors, and malware before it enters your environment
- Automated safety screening on model input and output
- Tamper-evident records, retained and verifiable
Everywhere
Your infrastructure, including no internet
The same platform on premises, in a private cloud, across a hybrid estate, or fully air-gapped with no outbound network access required at any point in its operation.
- Installs and runs with no external service dependencies
- Cloud and on-premises capacity side by side
- Metrics exported to the monitoring you already run
From data ingestion to deployment and monitoring
Most teams stitch this lifecycle together from five tools with four handoffs, and the handoffs are where provenance disappears. In XAIOps each stage lives on the same platform, so a model in production traces back to the data it learned from.
Ingest
Bring data in from upload, existing storage, or a clinical archive - including imaging studies and genomic files that arrive ready to work with.
Register & permission
Catalogue it once, with ownership and access scoped to the right people and a record opened from the very first touch.
Explore
Understand the data before modeling it, using viewers built for the modality rather than a generic table preview.
Train & track
Train on whichever compute has capacity, with experiment history, parameters, and artifacts captured as you go.
Package & assure
Turn a result into a reproducible artifact and put it through security review before it can go anywhere near production.
Deploy & serve
Publish for bulk or interactive use behind a single governed entry point that applications integrate with once.
Monitor & audit
Track health and consumption, attribute usage back to teams, and keep an evidence trail that survives inspection.
One platform, no handoffs
Every stage shares the same identities, permissions, and audit trail, so a served endpoint traces back to the dataset it learned from.
Explore the modulesRuns on the infrastructure you already own
Every module shares one permission model
XAIOps is not a collection of scripts behind a dashboard. Data, training, serving, gateway traffic, and model assurance run on the same identities, permissions, and audit trail.
Every dataset governed, every cluster one click away
Catalogue data once with the right people able to reach it, then send work to whichever compute has capacity. The same interface reaches a shared HPC queue and a container platform.
One data catalog
Every dataset in the organization in one place, with ownership, access control, and search that works on meaning rather than filenames.
Clinical imaging
Imaging studies organized as clinicians expect, reviewable in the browser, with inference launched from the study itself.
Sequence & genomics
Large genomic data made analysis-ready on arrival, so researchers explore it in place instead of transferring it.
Any compute you own
HPC schedulers, container platforms, GPU servers, and workstations behind a single submission flow.
Connected storage
Work with the object storage you already run, with access governed centrally rather than by shared credentials.
Every data task, with the audit attached
Data management and auditing are the same feature here. There is no path to read, move, share, or remove a dataset that leaves no record - which is what turns a data platform into one a privacy office will sign off on.
Manage
- One catalog for every dataset in the organization
- Ownership and access scoped per dataset
- Search that finds data by what it is, not what it is called
- Connect the storage you already use instead of copying data twice
- Perform EDA and visualizations with a natural language interface
Audit
- Every access recorded with who, what, and when
- Records held in a form that cannot be quietly altered
- Retention aligned to clinical and research obligations
- Verification on demand, so the trail is evidence rather than a claim
Protect
- Sensitive material encrypted wherever it is stored
- Separation of duties between administrators, researchers, and reviewers
- Controlled sharing within and between teams
- Review gates on anything leaving the platform
Designed around medical imaging, sequence, and genomic data
Generic MLOps tools treat data as rows in a bucket. Clinical and genomic data are neither: an imaging study is a structured hierarchy carrying identifiers, and a genome is too large to move casually. XAIOps was built for both from the start.
Put your own documents to work, with the source behind every answer
Every team possesses critical documentation—protocols, standards, literature, and internal reports—that often goes unread. XAIOps transforms these documents into a repository your team can query in plain language, providing precise answers with source citations for immediate verification.
- Turn the documents a team depends on into something they can simply ask
- Answers cite the passages behind them, down to the document and page
- Private, shared with named colleagues, or open to the organization
- Conversations kept, searchable, shareable, and exportable
Build a repository
Add the documents that matter - reports, PDFs, spreadsheets, notes - and indexing runs in the background while you watch it progress.
Decide who sees it
Keep a repository to yourself, share it with named colleagues, or open it to the organization. Access comes back as easily as it was given.
Protected where it sits
Stored content and saved conversations can be encrypted at rest, so a repository built from sensitive material stays defensible.
Ask in plain language
Answers are drawn from your own material - across the whole repository, or one document at a time when the document is the unit that matters.
Governance in the path, not in a spreadsheet
Most AI governance is a document written after the fact. In XAIOps it is enforcement: the platform will not promote an unassured model, will not serve unapproved automation, and cannot quietly lose the record of what happened.
Approve before it ships
Anything that reaches users - packaged models, assistants, externally sourced artifacts - passes a review that records who approved it, for what purpose, and for how long.
Screen every request
Automated safety checks run on model input and output, applied separately to application traffic and internal automation, with findings kept alongside the request.
Prove what happened
Access records are held so they cannot be quietly edited, retained for the long term, and verifiable on demand - which is the difference between evidence and an assertion.
Meter and attribute
Access is issued per person and per team with consumption attributed back, so you can answer who spent what across which models before anyone has to ask.
A drug discovery bench that runs on your cluster
A curated catalog of GPU-accelerated biology models, deployable to whichever compute you own, with an assistant that assembles them into multi-step workflows and explains the choices it made along the way.
- Deployed to whichever compute you own, with lifecycle controls for status and teardown
- Job history and results stored alongside the rest of your experiments, not in a separate silo
- The assistant proposes a plan, cites its reference material, and refuses tools it has not been given
Deployable capabilities
Structure prediction
Predict how a protein folds from its sequence
Complex prediction
Model how proteins and partners fit together
Molecular docking
Pose candidate molecules against a target
Molecule generation
Generate and refine candidate chemistry
Protein design
Design new backbones and the sequences for them
Genomics
Model DNA at genome scale
What teams ask us first
Deliberately. We keep implementation specifics out of public material, and go as deep as you like in a demo - architecture, data flows, security design, and integration detail, under NDA if you prefer. If you are evaluating seriously, that conversation is more useful than a feature list anyway.
Yes - that is what it was designed around. Imaging studies are organized the way clinicians expect and reviewable in the browser, and large sequence data is made analysis-ready on ingest so researchers can explore it without transferring it. Both live in the same catalog as the rest of your data.
The full lifecycle: ingestion, cataloguing and permissioning, exploration, training with experiment tracking, packaging, security assurance, bulk and interactive deployment, and monitoring with usage accounting and audit. Each stage lives on the same platform rather than in a separate tool with a manual handoff.
Yes. The platform is delivered as signed, vendor-built software, licensed without an outbound connection, and no component requires internet access to operate. Air-gapped installation is a first-class path, not a workaround.
No. XAIOps works with your existing compute, storage, and container infrastructure rather than replacing it. It is the layer that ties them together with one permission model and one audit trail.
Data stays in your storage, inside your network. Access is permissioned per dataset and recorded in a form that cannot be quietly altered, retained long term, and verifiable on demand. We will walk your privacy office through the specifics on a call.
It depends on which workloads you intend to run - some are comfortable on a single modern data-center GPU, while large structure prediction and genome-scale models want considerably more memory. We size this with you during scoping rather than guessing in a brochure.
Most organizations get one real workflow running end to end within a few weeks of install. The scoping call sets the target so the pilot proves something you actually care about.
See it running on your own workflow
Bring a use case to the call. We will show the modules that matter, on a live environment, and tell you honestly whether XAIOps fits.