Your AI Department.
Without the Department.
We become your AI delivery team, designing, building, and operating secure AI systems that remove operational bottlenecks, without you hiring specialists or waiting on internal roadmaps.
What this is actually worth to you
These are typical ranges from comparable engagements, not a guarantee, we'll size the specific business case for your workflow before you commit to anything.
faster policy and document retrieval with a knowledge assistant
fewer first-pass contract reviews needing a human read
faster case handling and turnaround
lower cost to run the workload day to day
Representative outcomes from comparable workflow automation and knowledge management engagements. Actual results vary by process maturity and implementation scope.
Compliance isn't an FAQ answer. It's how we build.
For a European buyer, this is usually the question that decides the deal, not a box to tick afterward. It's built into the architecture from the first workshop, not bolted on before launch.
GDPR by design
Data handling mapped to GDPR requirements from the first workshop, not retrofitted before launch.
EU AI Act readiness
Risk classification and documentation aligned to the EU AI Act as it comes into force.
Full audit trails
Every decision, retrieval, and action logged and traceable, not just the final output.
Encryption everywhere
Encrypted at rest and in transit, across every environment we deploy into.
Identity & SSO
Integrated with your existing identity provider, no separate login to govern.
Role-based access
Access scoped to role and need, matching how your organisation already manages permissions.
EU data residency
Data stays within the jurisdiction your compliance team requires, by default.
Human oversight
Clear checkpoints for human review on any decision that warrants it, never a black box.
Cloud, VPC, or on-prem
Deployed wherever your security policy requires, including fully on-premises.
Operational monitoring
Continuous monitoring of latency, usage, cost, model quality, and security events after deployment.
Enterprise engineering, not a black box
This is the same architecture whether you rent, own, or run hybrid, just with a different layer doing the inference. Nothing here is exotic; it's built to be understood and audited.
Your AI roadmap, end to end, not just a pilot.
Most AI initiatives stall between "interesting demo" and "running in production." We own the entire path, assessment, design, build, deployment, and operation, so your initiative doesn't stall at the handoff. That includes what happens after go-live: we stay on as the team running it day to day, the same way you'd rely on a managed service, not a project that ends when the invoice does.
Assess & roadmap
We start with the bottleneck costing your team time, and map the shortest realistic path from where you are to a working solution, no AI expertise required from you or your team.
Design & build
We choose the model, the data pipeline, and the architecture, open-source foundations, fine-tuned on your data, built to run entirely inside your existing environment.
Deploy & approve
Deployed inside your environment and reviewed once by your IT and security teams, a single clean checkpoint, not a recurring approval cycle.
Run & optimise
Every solution runs on a delivery engine we've already hardened, orchestration, guardrails, observability, version control, so nothing lands on your desk afterward.
What happens after you sign
A typical rhythm, not a fixed contract. Scope and integration complexity move the dates, not the sequence.
Everyone's talking about AI.
Your team is still waiting for it.
You don't need another strategy deck on AI, you need the workflow that's eating your team's week fixed, without becoming an AI project manager to get there.
Central AI teams have a backlog, not a slot for you
If your initiative depends on a central IT or AI team's roadmap, it competes with every other department's priorities. You can launch on your own timeline instead.
Rented tools never get cheaper
Generic AI subscriptions and API bills scale with usage, forever. An owned solution gets more cost-efficient the more your team relies on it.
If it touches client data, compliance will ask first
As the business owner of the initiative, you're the one who has to answer for where the data went. We build so that question already has a clean answer.
You know the workflow. You don't need to become an AI team
You can describe exactly what should happen differently in your day-to-day operation. That's the brief we need, the build is on us.
And whatever runs it, we build it.
A rented API, a fully owned model, or a hybrid of both, the right answer depends on the workload, not on ideology. We design around whichever fits.
Rent
Call GPT, Claude, or Bedrock directly. Fast to start, pay per use, best for quick pilots.
Own
Fine-tuned open-source models, run entirely in your environment. Best for core, proprietary workflows.
Hybrid
Rented APIs for general tasks; an owned open-source core for the sensitive, high-value ones. Most teams land here.
Same assistant, same conversation, one decision made automatically underneath it: is this data that can leave the building, or not?
Renting a tool, waiting on IT, or owning it, what each path actually costs you.
"Wait for the central AI team" usually means a multi-quarter queue behind other departments. Xenium gives your initiative a dedicated path to production.
| Renting AI (generic tools) | Waiting on your internal AI/IT roadmap | Owning AI with Xenium | |
|---|---|---|---|
| Time to launch | Days, good for generic productivity | Depends on internal priorities | Weeks, dedicated to your outcome |
| Who it's built for | Every customer of the vendor | Whichever team the central roadmap favors | Your specific workflow, your data |
| Data boundary | Leaves your environment on every call | Inside, once eventually approved | Always inside your environment |
| Cost trajectory | Scales with usage, forever | Upfront platform investment, benefits arrive later | Bends down as your team adopts it |
| Who owns the result | No one, it's a shared tool | The central team's priorities, not yours | You, the outcome is built to your brief |
| Approval & compliance | Inherited from vendor terms | You wait for the platform team's review cycle | Audit-ready by design, approved once |
| Headcount needed | None, but limited differentiation | You compete for budget and hires on the central plan | Zero hires, we design, build, and run it |
The kind of thing your team could be using next month
A knowledge assistant that answers from your own documents, and an agentic workflow that carries out a task end to end. Try both below, illustrative demos, running on sample data.
Knowledge Assistant
Private AIAgentic Workflow: Insurance Claim
These are sample versions running in your browser.
See the RAG chatbot and agentic AI application actually deployed in a private AI environment, your data, your infrastructure, in action.
The same rigor. Sector-specific stakes.
Every business unit below already knows the workflow that's costing them time, and carries the risk of getting AI wrong with client data. We design around both.
Financial Services & Insurance
Heads of Claims, Operations & Client Services- Policy and claims Q&A assistants over internal documentation
- Contract and disclosure review agents
- Full audit trail, encrypted at rest and in transit
Legal & Professional Services
Practice Leads & Operations Directors- Contract and due-diligence review agents
- Clause and precedent search across your own matter files
- Client data never touches a shared inference layer
Healthcare
Heads of Operations & Patient Services- Clinical and administrative knowledge assistants
- Patient data never leaves your environment
- Governed, logged, and traceable by design
Business & Professional Services
COOs, VPs of Operations & Customer Experience- Internal knowledge assistants across policies, wikis, and manuals
- Workflow agents for reporting, approvals, and client onboarding
- Fine-tuned to your terminology and brand voice
Not why AI matters. Why us, specifically.
What launching your own AI initiative actually looks like
lower AI & GPU cost vs. unmanaged rented inference
AI, DevOps, or IT hires required on your side
from your first conversation to a production-ready assistant
of your data stays inside your own environment
Hi, I'm Shubhangi.
Over the past 25 years, I've helped organisations modernise software, transform to the cloud, and build enterprise platforms. Throughout that journey, I kept seeing the same pattern: building an AI pilot is relatively easy. Running AI securely, reliably, and cost-effectively in production is where organisations struggle.
That's why I founded Xenium and architected XePlatform, our own AI operations platform. Deployed directly into your cloud account, it provides the infrastructure, security, orchestration, observability, and release engineering needed to operate AI at scale, without requiring you to build a dedicated platform engineering team.
Whether the right fit is renting, owning, or a hybrid of both, our goal is simple: help you move from AI pilot to production while keeping complete ownership of your data, infrastructure, and AI strategy. Your cloud. Your data. Your models. Your choice.
If you're ready to turn AI into a reliable operational capability, not just another proof of concept, I'd be delighted to discuss how we can help.
Before you talk to us
Yes, that's who we're built for. You bring the business problem and the outcome you want; we handle the model, the data pipeline, and the infrastructure. Your IT and security teams stay involved at the approval stage, not the build stage.
You'll want to loop them in before go-live, but discovery and design can start with just you and your team. We prepare exactly what your IT and security stakeholders need to review, so that approval is a single clean checkpoint rather than a months-long back-and-forth.
Renting means calling someone else's model over an API, you pay per use, forever, and your data typically leaves your environment to get an answer. Owning means a model fine-tuned on your own data, deployed inside your environment, that your team controls. The data and the fine-tuning are what make it yours.
You don't need one to start. Most of our engagements begin with a single business unit's workflow, not a company-wide strategy. A well-run first initiative is often what turns into the wider strategy.
Data residency, audit trails, and governance are built into how we deploy, not bolted on afterward. Your compliance team retains ownership of accreditation; we give them an environment that's structurally easier to sign off on.
Most initiatives move from a defined outcome to a working, production-ready assistant or workflow in a matter of weeks, not the multi-quarter timeline typical of a central AI/IT roadmap.
Azure, AWS, GCP, OpenShift, and VMware environments. We deploy into whichever platform you already run, rather than asking you to move to ours.
Entra ID, Okta, Ping, and ADFS, among others. Access follows your existing identity and permissions model, not a separate login we introduce.
Pinecone, Chroma, Milvus, pgvector on Postgres, and Azure AI Search on the retrieval side; OpenAI, Claude, Gemini, Llama, Mistral, and DeepSeek on the model side. We choose based on your workload, not a fixed stack.
We've connected into SAP, Dynamics, SharePoint, Teams, Salesforce, and ServiceNow on prior engagements. If your data lives in it, we've likely already built the connector pattern for it.
Tell us the bottleneck. We'll show you what owning the fix looks like.
No demo scripts, no platform tour. Just a conversation about what you're trying to achieve, and how fast we can get you there.
Start Your AI Initiative
