01AI-native consultancy
We design and deliver personalized AI products — strategy through implementation, in one engagement, on infrastructure you own. Custom systems for your stack, your servers, your weights.
The differentiator
Every other AI vendor sells you access. We deliver systems you keep when we leave.
Every system we build slots into your existing tech: your warehouse, your auth, your observability, your ticketing.
We deploy on your AWS, Azure, GCP, or on-premise infrastructure. Prompts and data never leave your perimeter.
Code, model weights, training pipelines, and runbooks are yours forever. There is no platform, no SaaS, no lock-in.
We don't sell a product — we design and deliver yours. Every engagement is shaped to your business, not a configured template.
Built by
Founder & CEO
Founder & CEO of Ubuntu Online. Leads strategy and delivery for AI consultancy work — building custom AI systems that customers actually own — code, weights, training pipelines, and runbooks — on infrastructure they control.
Head of Engineering
Leads engineering for Ubuntu Online. Builds production-grade enterprise AI and automation workflows, scaling private LLM platforms from POC to production. Full-stack background spanning fintech, insurance, and AI infrastructure.
Strategic AI Head
Heads strategic AI for Ubuntu Online. Builds enterprise operating systems powered by AI and owns the architecture, model selection, and engagement model that turn AI initiatives into production systems leadership actually trusts.
AI Researcher
AI researcher focused on model evaluation, fine-tuning, and the eval pipelines that keep private LLM deployments accurate as base models evolve. Works across model selection, retrieval, and post-training methods.
Strategy + Implementation · One engagement · No handoffs
Most consultancies hand strategy to one team and implementation to another, and the client pays for the seam. We deliver both — same engineers, same engagement, four phases.
What we build
Every engagement is custom-built for your stack and your data. These three shapes recur most often.
Also delivered — six more capabilities
Systems that learn your business processes and run them. No brittle scripts.
Live agent assist on calls. Next-best-action, knowledge retrieval, compliance prompts.
A coding copilot trained on your codebase. Stays inside your firewall.
One pane of glass for every AI agent in your org. Audit trails, on-prem.
Fine-tune or train models from scratch on your data. You own the weights.
Small language models trained on your data, deployed at the edge.
What we ship
We deliver under NDA. Below is the shape of a private LLM deployment, drawn the way we actually build it. The highlighted path traces inference data: request, model, observability, sink.
What this shipped
Industries served
We deliver into six industries across five regulatory regimes. Frameworks adapt. Method stays the same.
KYC, fraud, statement analysis, and underwriting AI for retail and corporate banking.
CBK · Data Protection Act 2019 · OSFI
Contact-center AI, fraud on signalling, churn prediction, and network-ops automation.
CA Kenya · Data Protection Act · CRTC
Document intelligence for waybills and customs. Route, warehouse, and damage-detection AI.
WCO data standards
Citizen-services automation, document digitization, air-gapped on-premise AI for sensitive workloads.
Data Protection Act · Privacy Act · ITSG-33
Clinical document intelligence, prior-auth automation, and patient-interaction AI on customer infrastructure.
HIPAA · PIPEDA · PHIPA
Conversational BI, demand forecasting, loss prevention, and customer-service AI.
PCI DSS where applicable
Compliance frameworks we work to
SOC 2 · HIPAA · PIPEDA / PHIPA · DPDP Act 2023 · OSFI · GLBA · RBI · CBK / Data Protection Act · NDPR
The obvious question
You can. Here is what changes when you don't.
9–18 months
MLOps stack, eval suite, governance layer — built from scratch by engineers also doing their day jobs.
6–12 weeks
Open-source models, vLLM, and modern eval tooling — assembled, integrated, deployed, and operated on your infra.
Hallucinations in prod
Most teams rush to a model and ship without an eval suite. Production hallucinations kill the project.
Eval pipeline first
We build the eval pipeline before the first model goes live, and the governance layer before the second model arrives.
Heroic engineer
When the original team rolls off, the system depends on one or two people who remember how it works.
Your whole team
We hand over with documentation, runbooks, training, and an optional support engagement. Your team runs it.
Common questions
The closer
Thirty minutes to talk through your stack, your data, and the AI opportunity you care about most. No pitch deck. No sales theatre.