Discovery
Every AIOS starts with a map of what already exists. The goal of this stage is to investigate the current AI usage inside the business and, more importantly, to identify what tools and platforms the team actually runs on. Which software lives inside the Microsoft 365 organisation. Which lives inside the Google Workspace organisation. Which SaaS subscriptions are floating outside both. And the workflows underneath them, with the pain points the team feels every week.
We run discovery through two assets. The first is the Sentry AI Discovery Tool. A free, interactive audit that turns a company's ecosystem, tools and departments into a draggable knowledge graph in about two minutes, and surfaces where context is siloed, scattered, or ready to connect. The second is our guided discovery workshop, where we sit with the leadership team, interview each department, and map the existing AI usage into a structured snapshot of the business. The output is a single document that everything downstream is built on.
Discovery produces three things. The first is a complete tool and platform inventory: every AI subscription, SaaS tool, internal database, ERP, comms platform and document store, mapped against the team that uses it. The second is an ecosystem audit that treats Microsoft 365 or Google Workspace as the spine of company knowledge, tracing where information lives, who can reach it, and where it leaks. The third is a pain point register: the repetitive, error-prone work the team complains about every Monday, ranked by hours lost and dollars at stake, so the AIOS attacks the right problems first.
“Give me six hours to chop down a tree and I will spend the first four sharpening the axe.”
Enablement
Most teams are already paying for more AI than they use. ChatGPT seats, Claude licences, Copilot rolled across the Microsoft organisation, Gemini bundled into Google Workspace, Notion AI, inline AI in the CRM. The subscriptions are there. The leverage is not. This stage closes that gap.
ChatGPT
Claude
Copilot
GeminiOur role at this stage is to scale the existing AI subscriptions and tools that teams are already using. We teach prompt engineering the way the team actually needs it, we set up architectures so the right tool is used for the right job, and we build the RBAC (role-based access control) and permission systems that let leadership share AI access with confidence instead of locking it down out of fear.
Enablement rests on three pillars. Prompt engineering training comes first, hands-on sessions that get the whole team writing prompts that produce reliable, repeatable output, with templates, patterns and the muscle memory to use them. Next is architecture for the subscriptions already in place, where we stop the sprawl, consolidate the stack, and design how each tool slots into the operating rhythm of the business. Last is RBAC and permission systems, role-based access at every layer so sales sees pipeline context, finance sees the books, ops sees workflows, and the AI inherits exactly the same boundaries. Nobody, human or agent, reaches what they should not.
Development
Once the business knows what it has, the gaps start to show. This is where we build the parts off-the-shelf AI cannot do: the custom applications, the voice agents and the agentic workflows that actually run a business. And we build them fast. We work on the latest tools and open-source repositories the moment they land, so a system goes from idea to production in days rather than quarters, and scales without a rewrite when the load or the requirements grow.
Model-agnostic scaffolding that encodes and protects your IP.
Production voice agents, platforms and dashboards, owned by you.
Modern frameworks and open source, to build, test and scale fast.
Voice agents are a specialism we ship in production at serious scale, with sub-second latency, live function calls and full integration into CRMs and back-office systems, across recruitment, healthcare and finance. When a system needs a face, we build authenticated web platforms and internal dashboards on modern frameworks, deployed and owned by the client, like the MacroActive AIOS.
Speed here is a deliberate advantage. Because we build on open-source repositories and the newest agent tooling rather than a fixed proprietary stack, we can prototype, test and scale in a fraction of the usual time, and adopt a better tool the week it appears instead of being locked to last year's choice.
What we build is not a clever prompt but an AI harness: the model-agnostic scaffolding of skills, tools and guardrails that turns a general model into a system that runs a business. The value lives in the harness, not the model. Encoding a company's proprietary workflows into agentic systems it owns is how AI enhances and protects its IP, rather than leaking it into someone else's platform. And because the harness is model-agnostic, it is future-proof: when a model is deprecated, repriced or banned, the workflow keeps running and we swap the model underneath. In a market this volatile, that is the difference between building on rock and building on sand.
Infrastructure
The future of enterprise AI is custom. The largest organisations are moving off a general model rented through someone else's API and towards running their own, models fine-tuned on their own data, inside their own walls. Owning the model, and the infrastructure it runs on, is what turns AI from a recurring bill into an asset the business controls.
To make that real, we deploy the GPU infrastructure an enterprise needs to run its models on its own data, at a fraction of the price of metered frontier APIs. Open-weight and fine-tuned models (Llama, DeepSeek, Qwen, or a client's own) run on hardware the business owns, in its own environment, so the high-volume and sensitive work never leaves the building and inference that once cost per token now runs on capacity already paid for. For a medical group, a bank, or any organisation bound by data-sovereignty rules, this is the difference between being able to deploy AI at all and not.
We do this hands-on. We provide the hardware, from a single secure server to a full GPU cluster, and we install it on-site, in person, wiring it into the systems and workflows the models need to be useful. The organisation ends up with sovereign, self-hosted AI it owns end to end, without ever having to become an infrastructure company itself.
Infrastructure is also how we reach the systems cloud AI cannot touch on its own: on-premise ERPs, ageing databases and line-of-business apps with no public API, behind a corporate firewall. For these we provision dedicated virtual servers (VPS) inside the company's own network that terminate the VPN, vault credentials and expose a narrow, audited surface, plus the custom MCP servers and orchestration layer that route context between agents and every database, service and API. This is the connective architecture the rest of the operating system runs through, and we build it alongside the in-house IT team, inside their change-control and security standards, not around them.
Management
Most enterprises are already past deployment. Their agents are scattered across the business: a Claude agent here, an AWS AgentCore agent on Bedrock there, a self-hosted open-weight model, an embedded Salesforce agent, each in its own console, with no single view of what they cost, what they are doing, or whether they are safe. The industry has now named the category that fixes this: the Agent Management Platform. Most entrants are platform control towers: their openness is strategic and partial, deepest inside their own estate.
The Sentry Agent Management Platform is the neutral version: one console to monitor, manage and optimise every agent the business has, whichever harness it runs in. It is a control plane, not another agent: it does not build, host or replace anything. It registers each agent where it already runs, including the agents you bought, not just the ones you built, and the team never has to leave the harness their agents already live in.
How deeply the platform sees an agent depends on how that agent lets it connect, across four integration tiers. Tier 1, inline: agents you build or self-host route through Sentry's own gateway, giving full transcripts, per-request cost and live policy enforcement. Tier 2, native telemetry: cloud runtimes such as Azure AI Foundry and AWS AgentCore stream their telemetry straight to Sentry, near-full fidelity with no rebuild. Tier 3, admin APIs: closed platforms like Salesforce or Microsoft are polled for usage, cost and audit records through the vendor's own APIs. Tier 4, registered: true black boxes are still registered, given an owner and a boundary, and governed by policy. Nothing in the estate is left ungoverned. This is also how the platform is productised: connecting an agent is a guided step in the console, pick the harness, it is classified into its tier, and the tier tells you exactly what Sentry will see and govern. In week one we classify the whole estate this way, agent by agent.
Most importantly, optimisation requires data. The platform tracks and analyses every AI conversation the business runs, from input and output tokens to which tools were called and how long each step took, then recommends what to change. With that dataset we apply semantic model routing, sending each request to the model that handles it best for the lowest cost instead of paying frontier prices on every turn. Because every agent is measured the same way in one place, the same work runs at a fraction of the spend, and a cost or quality regression is caught the moment it appears.
Management is more than watching, it is control. Nothing an agent does that changes the business, a payment, an outbound email, a record update, happens without a human approving it first. Because everything flows through one plane, every agent can be governed: sensitive actions are held in a human approval queue before they run, boundaries and permissions are enforced at the tool call, an audit trail records who did what, and a single control can pause or stop any agent at its connection point. This human-gated approval is near-absent in the platform towers, and it is exactly what a regulated business needs. Observation becomes control.
Optimisation pulls on five levers. Context re-orchestration restructures retrieval and prompts so each call carries only what it needs. API orchestration tuning parallelises tool calls, often taking a workflow from twenty seconds to two. Knowledge base restructuring fixes layout, chunking and embeddings so retrieval returns the answer in one hop. Conversation-level token tracking records tokens, tool calls and latency on every interaction. And semantic model routing sends each request to the model that handles it best, cheap for simple turns, frontier for hard reasoning.
Just as important is how it arrives. The platform is delivered as a managed service, not shelfware: live in weeks rather than quarters, at a fraction of the cost of the enterprise suites, with a named Sentry team who monitor the estate, curate the optimisations and meet you on a weekly cadence. The enterprise vendors sell you a console and leave your team to staff it; we run the operation with you.
Measured on one set of axes, the whole fleet becomes something a business can reason about. That is what lets us make clear, evidence-based decisions about where to optimise, which model to route to and which agents to fine-tune, and act on them without disruption.
Custom Intelligence
The final stage is what turns an AI deployment into an AI operating system: intelligence the business owns. We take the organisation's own data (documents, conversations, decisions, records) and vectorise and embed it into a living knowledge graph the whole system can draw on, so any agent fetches the right context in one hop instead of re-deriving it. On top of that substrate we build custom and fine-tuned models trained on how the business actually works: proprietary intelligence that answers better, cheaper and faster than a general model could, and that the company owns rather than rents.
Around that graph we build feedback loops and CI/CD pipelines so workflows constantly improve and learn from themselves. Prompts are versioned. Agent changes are reviewed. Retrieval quality is scored. What works gets promoted. What does not gets retired. The AIOS does not stand still after launch. It compounds.
Three mechanisms keep it compounding. A living knowledge graph absorbs every conversation, decision, document and outcome, becoming the institutional memory of the business and queryable by every agent in the system. Feedback loops score, edit, accept or reject outputs, and that signal feeds back into prompts, retrieval and routing so the system gets quietly smarter without anyone retraining a model. And CI/CD pipelines for AI push prompt changes, agent changes and knowledge changes through review, test and deploy, so the AIOS evolves with the same discipline as production code rather than by drift.