Bring AI into your company.
Keep control.
We help companies use AI with clarity, control and confidence — from internal assistants to governed agents connected to your tools.
We design the infrastructure, permissions and security so it works in production — even if you are not an AI expert.
What changes when you do it right
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AI that your teams can actually use
Assistants on your documents, policies and internal knowledge — with clear limits.
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Connected to your tools — safely
SharePoint, email, calendars, APIs — with permissions and human approval where it matters.
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Someone who guides you end to end
From diagnosis to implementation. No need to become an AI company overnight.
Three things every company needs
Who we help
Companies starting with AI the right way
You do not need an expert team. We help you define the right foundation from the start — in plain language.
Teams moving from pilots to production
If you have already tried ChatGPT, RAG or agents, we help you move to a governed, secure environment.
Organisations that need control and traceability
When AI touches data, tools or processes, permissions, approvals and supervision must be defined.
How we work
Architecture Review
Independent 90-minute assessment across infrastructure, governance and security — with a written report and prioritised roadmap.
Platform Blueprint
Target architecture, identity model, security boundaries, operating model and phased implementation plan.
Engineering
We build the platform layer: runtimes, connectors, policy engine, observability and operational controls.
Operate & Evolve
Handover, runbooks, cost governance and continuous improvement — so the platform survives day two.
Not sure where to start?
Tell us what you want to achieve with AI. We will assess risks, access and the right path — in plain language.
Request an Architecture ReviewTechnical radiography · INNOQUO
The control layer for enterprise AI agents
Configure → Connect to knowledge → Connect tools → Govern → Observe → Evolve
INNOQUO designs and builds the infrastructure, controls and operating model that let AI agents run connected to enterprise data, tools and processes.
The shift
The model is no longer the system.
A model can generate a response. An enterprise AI system must also manage identity, knowledge, tools, permissions, security and operations.
When AI gains access and the ability to act, the platform around it becomes the critical part.
For engineering leaders
A shared foundation for taking AI to production
One standard for running AI
Avoid every team building its own stack, duplicating connectors and defining incompatible controls. We design a shared foundation for models, agents, knowledge, tools and operations.
Pilot to production without rebuilding everything
We define trust boundaries, isolation, observability and an evolution path from the start. The goal is not to complicate the pilot — it is to stop it becoming an architecture you cannot operate.
Controls that can be understood and demonstrated
Identity, permissions, approvals, audit and action limits built into the platform. Every agent should have an owner, a defined scope and a verifiable record of its actions.
Visible costs and capacity
Model consumption, executions, storage, infrastructure and tools under observation. The platform should attribute costs, set limits and detect drift before it becomes an operational problem.
Independent assessment before you build
An architecture review covers infrastructure, governance and security before you commit to a larger implementation. You get a written report with risks, open decisions and a prioritised roadmap.
Verifiable engineering
We publish principles, architectures and technical learnings so our decisions can be inspected. We do not defend a technology by default — we choose architecture proportional to risk, load and operational capacity.
Agent lifecycle
From the first agent to a governed AI operation
01
Configure
Define the agent's purpose, role and boundaries.
Roles · Instructions · Models · Versions · Deployment state
02
Connect to knowledge
Define what information it can query, from which sources and under which identity.
RAG · Retrieval policies · Source scoping · Memory · Traceability
03
Connect tools
Give controlled access to enterprise systems and action capabilities.
MCP · APIs · SharePoint · Databases · Email · Calendars
04
Govern
Define who can use the agent, what it can do and when approval is required.
Identity · RBAC · Policies · Tool scopes · Approvals · Ownership
05
Observe
Understand what it did, why, how much it cost and where it failed.
Traces · Evals · Cost attribution · Decision logs · Audit · Incidents
06
Evolve
Change the agent without losing control over versions, permissions and behaviour.
Versioning · Rollouts · Rollback · Suspension · Change control
Platform architecture
The layers behind an agent system
Experience
Channels through which users and systems interact with AI.
Chat · API · Email · Applications · Internal portals
Agents
Logic that interprets goals, maintains context and coordinates actions.
Agents · Workflows · Memory · Models · Instructions
Access
Enterprise resources the agent can query or use.
Knowledge · Connectors · APIs · Tools · Data
Control
Rules that define who can access, what is allowed and what needs approval.
Identity · Permissions · Policies · Approvals · Audit
Runtime
Infrastructure that executes models, agents and tools.
Models · Workers · Queues · Sandboxes · Isolation · Gateways
Operations
Layer needed to keep the system available, secure and cost-controlled.
Traces · Evaluations · Costs · Alerts · Incidents · Versions
Three engineering axes
AI Infrastructure
Where and how agents execute
We design the foundation needed to run models, agents and tools with reliability, isolation and observability.
Agent runtime · AI gateway · Tool runtime · Knowledge layer · Control plane · Operations layer
Explore →AI Governance
What agents can access and what they are allowed to do
We define identities, permissions, connectors, tools, approvals and ownership.
Identity · Knowledge access · Connectors · Tool permissions · Approvals · Ownership · Audit
Explore →AI Security
How agents and organisations stay protected
We reduce attack surface and limit the impact of incorrect behaviour or unauthorised actions.
Prompt injection controls · Secrets · Isolation · Validation · Least privilege · Operational limits · Incident response
Explore →Delivery model
From assessment to an operable platform
Architecture Review
Independent assessment of infrastructure, governance and security.
Includes documentation review, technical session, written report and prioritised roadmap.
Platform Blueprint
Target-state design before a major implementation.
Architecture · Identity · Connectors · Security boundaries · Observability · Operating model · Phased plan
Engineering
Build the components needed to put the platform into operation.
Runtimes · Integrations · Policies · Automation · Observability · Operational controls
Handover and evolution
Documentation, runbooks, team transfer and a clear model to maintain and extend the platform.
The goal is for the system to survive day two without depending on informal knowledge or manual processes.
Our platform
Engineering principles, implemented
agents.innoquo.com is INNOQUO's platform to configure agents, connect knowledge and publish AI agents.
Available today
- Configurable agents
- Managed knowledge and RAG
- Publication via dedicated URL
Platform direction
- Governed connectors
- Tool identity and permissions
- Approvals and audit
- Observability and enterprise operations
Evidence
We publish architecture and technical knowledge to make the decisions, limits and trade-offs behind our systems visible.
Reference architectures
Complete designs with components, flows, identity, trust boundaries, security and operations.
Explore architectures →Applied research
Technical documents on problems that appear when taking models and agents to production.
Explore research →Engineering labs
Reproducible implementations to validate patterns, tools and controls.
Explore labs →Open source
Our reference implementation programme is in development. We publish code when it includes documentation, tests and a reproducible operating model.
View roadmap →Start here
Review the platform before granting AI access
An Architecture Review identifies risks and open decisions before connecting agents to enterprise data, tools and processes.
Infrastructure · Governance · Security
- Documentation review
- 90-minute technical session
- Written assessment
- Prioritised roadmap
- Follow-up session