Platforms

For the CIO and CTO

Accion Labs makes enterprise AI economical and safe to run, and puts governed agents to work inside the platforms your business already operates. Most enterprises run on the hyperscalers and on enterprise platforms like ServiceNow, Salesforce, and Microsoft, and each one is a place agents can take real work off people’s hands. We agentify those platforms and give every AI initiative one governed, cost-controlled runtime to build, deploy, and run agents, with AI FinOps to keep spend in check and the option to run inside your own perimeter for data that has to stay there.

What we deliver

The platform foundation has three jobs: agentify the platforms you run, give agents a home to run in, and keep all of it economical and governed.

Agentify the platforms you run

Your business already runs on platforms where agents could take real work off people’s plates: the service desk, the CRM, the ERP, the productivity suite, and the hyperscaler services behind them. We put governed agents to work inside those systems, through connectors that respect their data and permissions, so the value lands where your teams already work rather than in a separate tool. This is the largest and fastest area of opportunity, because it builds on what you already own.

A home for agents

Agents need somewhere governed and economical to run. We stand up one runtime that spans your clouds and your own data center, where agents are orchestrated, connected to the tools and platforms they act on, grounded in your knowledge, and observed in production. New initiatives ship on a foundation that already exists, instead of each one inventing its own.

Economical and governed

AI spend climbs quickly, and governance and access cannot be an afterthought. We bring AI FinOps to the foundation, so the cost of every agent and model is visible and steerable, and we build role-based access, audit, and security into the runtime. Where data has to stay inside, we deploy GenAI-in-a-box, the same foundation stood up on-premises or inside your own cloud account, so modern AI runs without your data ever leaving your control.

How we do it

Two things make the foundation work: one stack that runs across the platforms you already own, and the discipline that keeps it governed and economical. Then an engagement that fits where you are.

The foundation: one stack, across the platforms you run

The foundation is a single stack with a portable boundary. At the base are the platforms you run on, the enterprise platforms like ServiceNow, Salesforce, and Microsoft, and the hyperscalers behind them. On top sits the agent runtime, where agents are orchestrated, connected to tools, grounded in your knowledge, and observed. Above that, governed agents do real work in production. Around all of it runs governance and control, access and audit, security and compliance, FinOps, and observability. The same foundation deploys as a managed service, inside your own cloud account, on-premises, or in a hybrid of these, so the choice of where AI runs stays yours.

Agentify what you already run

The primary opportunity is to put governed agents inside the platforms your business already operates. Most often that starts in the service-management platform, where agents triage alerts and find root cause, run major-incident response, fulfil access and provisioning requests, and resolve routine tickets through a conversational assistant, with auto-remediation that restarts, clears, scales, or fails over before a person is paged. The same approach extends across the CRM, the identity platform, and your own applications: copilots that draft and summarize, identity-lifecycle agents that onboard and offboard with approval and an audit trail, and risk agents that replace email and spreadsheets. Every one runs through governed connectors that honor each platform’s data and permissions, and stays inside your perimeter. You get agents doing real work where your teams already are, without a rip-and-replace and without your data leaving your control.

The deeper governance and assurance story, access, evidence, and compliance, is covered in Governance and Assurance, and the methodology behind all of it is on our Semantic Engineering practice.

The engagement: assess, agentify, operate

We start by assessing the platforms you run, where your spend is going, and which workloads have to stay inside, then pick a platform where agents can earn their keep quickly. We agentify it, prove the value, and expand across the rest of your estate, and we can operate and optimize the foundation as a managed service or hand it to your own platform team. How we enter varies: a platform-agentification engagement on the system that matters most, a FinOps engagement to get spend under control, or a GenAI-in-a-box deployment for data that must stay inside.

What to expect

Results vary by environment, and we set targets against your baseline before we start. The patterns we work toward:

• Agents doing real work inside the platforms your teams already use, reached without a rip-and-replace.

• Routine tickets, change plans, and provisioning handled in minutes rather than days, with auto-remediation acting before a person is paged.

• A unified view of your estate, with assets discovered automatically across on-prem and cloud.

• AI and cloud spend brought under control, with idle and oversized resources cut substantially.

• Availability and reliability targets met in production, with most issues caught before they reach users.

• Sensitive workloads run inside your own perimeter, governed and audit-ready by default.

The differentiator is that the foundation meets your business where it already runs. Agents land inside the platforms you own, on a foundation that is economical, governed, and yours to place across any cloud or your own walls.

Who it is for

The strongest fit is the CIO and CTO operationalizing AI across the enterprise: organizations running major enterprise platforms and several clouds, carrying rising AI and cloud spend, and holding regulated or sovereignty-sensitive data that has to stay inside. It is the foundation that makes enterprise AI economical and safe to run. See how we engage for the commercial models.