From AI pilot to production
With ContextOne, we help you build, evaluate, deploy and continuously improve AI capabilities on a reusable platform.
It gives your organization one secure, governed and repeatable path from promising pilot to production.
One agent is easy. Running many you can trust is the real work
AI is moving into business-critical work, where reliability, traceability and control matter. Yet many organizations are stuck with isolated pilots, fragmented controls, hard-coded model choices and unclear ownership.
ContextOne turns AI delivery into a repeatable practice.
The value of ContextOne
Faster time-to-value
Less duplicated effort and earlier business impact.
Reuse at scale
Every new AI capability builds on the foundation of the last one.
Higher quality and reliability
Automated evaluation and quality gates before anything reaches production.
Cost visibility and control
Control of AI consumption, with less model lock-in.
Get started with ContextOne
Start with a business area, not with a technology
We start with an area where you want to improve performance, such as customer service, sales, finance, operations, claims or software engineering.
Every stage creates value and ends with a clear decision on whether to continue.
From AI idea to measurable value in three steps
Prove it and build it
(1 day · includes the AI Discovery Workshop)
We prove the approach with your real data and engineer it into a production-ready capability on ContextOne. We choose the right models, tools and integrations, build the first agent or workflow, and test it against representative cases. Evaluation, security and governance are part of the build from the first week.
Are we ready to run it?
Find where AI can make a difference
(4 weeks)
Most organizations do not lack AI ideas. They have pilots that never leave the lab and no clear owner. We bring together business owners, domain experts and technical teams to understand the process, the data, the systems and the outcome you want to reach. We establish a baseline for cost, cycle time or quality.
Is this worth proving?
Run it, improve it and scale it
(Ongoing)
Your AI capability goes live through a controlled release, with documentation and enablement for your team. From there it keeps improving as your business, models and data change. The first capability becomes the foundation for the next one, across more teams and business areas.
Done when people use it and the value is measurable
Four weeks from baseline to production
Week 1
Understand and baseline
Scope, baseline, data and integrations, success criteria and the initial evaluation dataset.
→ Agreed baseline and target
Week 2
Build the AI baseline
The first agent or workflow, connected to knowledge and tools and run against representative cases.
→ Working AI capability
Week 3
Evaluate and harden
Automated evaluation, quality and safety checks, governance, cost and performance analysis.
→ Production candidate
Week 4
Release and enable
Production deployment, live validation, observability, documentation and team enablement.
→ Production-ready AI capability
Model-agnostic by design
Switching models is configuration, not code
Models change fast, and your AI solutions should not have to change with them. All model calls pass through one AI Gateway, which handles authentication, quotas, token control and cost per case. Model routing then decides which model is used for which task, based on capability, cost and latency.
Frontier, open, fine-tuned or self-hosted models can be used or replaced without touching the layers above. We build on Microsoft Foundry, Azure API Management and Microsoft Agent Framework, or with open source components where that is the better choice. The layers stay the same, only the products change.
Costs you can see and control
See what your AI costs, and why
Because every model call passes through the same gateway, every token can be attributed to a business area and a use case.
Whatever cannot be attributed is shown rather than lost.
Governance
Governed, observable and auditable from day one
Production AI needs more than a good demo. ContextOne applies identity, safety and policy, observability, evaluation and cost governance across every layer of the platform, not at a single point.
Five controls, applied across every layer
-
Agents have their own identity and permissions.
-
Prompt injection, personal data, content safety and human approval.
-
Every model call, agent step and tool action can be traced.
-
Agents are defined in code, tested against a labelled dataset and released through quality gates on every change.
-
Every request is tagged by team and use case, so costs can be attributed and controlled.
ContextOne Continuous AI Engineering
Human-led. Agent-operated. Continuously improving.
A production AI capability is not finished when it goes live. Models, data and technology keep evolving, and so does your business.
We analyze usage, traces, failures and evaluations, improve agents and workflows, optimize models, quality and cost, and keep security and governance in place.
Our operating rhythm
-
Analyse usage, traces, failures and evaluations
Optimize models, quality and cost
Improve agents, prompts and workflows
-
Business impact
AI performance and quality
Cost and usage
What to build next
-
New models and agent patterns
New automation opportunities
Agentic coding in your own teams
Add platform capabilities as you grow
AI Engineering
Agentic coding tools, AI-assisted development and AI in CI/CD.
AI Governance
AI inventory, policies, model governance, evaluation and usage.
AI Integration (Nimbus)
API integrations, enterprise connectors and agent actions.
AI Support (Nexus)
Bug investigation, root-cause analysis and AI-assisted fixes.
Bring us a business area where you want to improve performance
We start with a half-day AI Discovery Workshop. You leave with a prioritised shortlist of AI use cases and a clear answer on what is worth proving.
