One control plane for the whole AI lifecycle
Register the data, pick the compute, train the model, assure the artifact, serve the endpoint, and account for every request - with the same identities and the same audit trail from start to finish.
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
Data & Compute Plane
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.
Model Factory
From dataset to served endpoint without leaving the platform
The task types research actually needs, tracked experiments, reproducible packaging, and one step from a finished result to something you can call.
Training across the range
Imaging, tabular, sequence, time series, generative, and language tasks, including fine-tuning of existing models.
Experiment history
Runs, parameters, metrics, and artifacts captured as you work, on storage you control.
Reproducible packaging
Versioned, reviewed build definitions, so a result can be rebuilt long after the person who produced it moved on.
Bulk and interactive serving
Large offline runs on your scheduler, low-latency endpoints for interactive use, from the same registry.
Assisted troubleshooting
Help interpreting a failed run, so a researcher is not blocked waiting on a support ticket.
AI Gateway & Governance
One governed door to every model you allow
Point applications at a single entry point. XAIOps decides where the request goes, applies safety policy, accounts for the usage, and keeps a protected record of what happened.
One entry point
Applications integrate once and reach every approved model, without ever holding provider credentials themselves.
Access and metering
Issue and revoke access per person or team, with consumption attributed for quota and chargeback conversations.
Safety screening
Automated checks on what goes into a model and what comes back, before either reaches a person.
Q&A over your documents
Turn the documents a team depends on into a repository they can question, with the source behind every answer and access you control.
More than one opinion
Put a question to several models, compare the answers side by side, and score them before you standardize on one.
Reviewed automation
Assistants and agents pass review before anyone in the organization can use them.
Model Security & Assurance
Treat model files like untrusted code, because they are
Every model entering your environment is inspected in isolation first. Only what passes becomes available to use.
Review before promotion
Every artifact is inspected for serialization attacks, malicious opcodes, and embedded secrets before it is allowed into your environment, whatever its source.
Multi-layer inspection
Analysis runs sealed off from your network using twelve independent scanners, including static AST analysis and malware signature detection.
A gate, not a report
Failing means it does not ship. A model that does not pass never reaches the place your teams pull from.
Evidence you can hand over
A comprehensive PDF verdict and technical report retained with the artifact, in records protected against later alteration.
Encrypted throughout
Credentials, conversations, and sensitive content protected at rest across the platform.
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.
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.
Answers that cite their source
Each answer includes specific source citations, down to the document and page, allowing claims to be verified rather than merely accepted.
Measure the answer
Put a question to more than one model, compare side by side, score against a known-good answer, and rate what comes back.
Nothing is lost
Every exchange is kept and searchable, starred when it matters, with the cost and the quality of each one visible.
Share and export
Hand a conversation to a colleague, in one go or in bulk, and export it when a finding has to leave the platform.
A repository is not a side door. Questions asked of your documents run through the same access rules, safety screening, and audit trail as everything else on the platform.
The detail comes in the demo
We describe what the platform does for you here, and keep the how out of public material. On a call we go into architecture, data flows, security design, and integration specifics as far as your team needs - under NDA if you prefer.
Fits the stack, does not replace it
XAIOps sits above your existing schedulers, registries, and storage. Nothing has to be rebuilt to adopt it.
Compute is registered centrally, with credentials held by the platform rather than by end users. A researcher picks a target from a list; everything that differs between environments is handled underneath. We go through the integration detail for your specific estate on a call.
Your infrastructure, your rules
XAIOps is delivered as signed, vendor-built software. There is no build step on your side and no requirement for outbound internet access.
On-premises
A reference topology we install alongside your team, on standard Linux hosts with your own storage - or bring your own container platform.
- Installed with your infrastructure team
- Your storage, your network, your hosts
- Or run it on your existing container platform
- No external service dependencies at runtime
Air-gapped
Offline delivery, licensing that does not phone home, and local model availability keep the platform fully operational with no egress at all.
- Offline delivery bundle
- Licensed without an outbound connection
- Models available locally
- Signed release manifests
Private cloud & hybrid
Run the control plane where you prefer and attach remote compute - a cloud cluster, a GPU server, or an on-premises queue - as registered capacity.
- Cloud and on-premises capacity in one place
- Credentials held centrally, encrypted at rest
- Each environment verified before use
- S3-compatible storage
Not a dashboard bolted onto someone else’s stack
Genuinely multi-platform
Training, bulk inference, live serving, and specialist model workloads all reach whichever compute you own, from one interface. Most platforms support one and call it a strategy.
Security in the critical path
Assurance, safety screening, and approvals are gates rather than reports. An artifact that fails is never made available to your teams.
Audit designed for regulators
Records that cannot be quietly edited, retained for the long term, and verifiable on demand - so provenance is demonstrable, not asserted.
Science, not just MLOps
Medical imaging, genomics, and a curated catalog of biology models ship in the box, because research teams do not only train classifiers.
Walk the whole lifecycle with us
We will run a dataset through registration, assurance, training, and serving on a live environment so you can see exactly what your team would touch - and go as deep on the internals as you want.