Platforms

For IT & Infrastructure (CIO, CTO)

Accion Labs makes enterprise AI economical and safe to run, and puts 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 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 well-controlled.

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 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 safe 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 access control 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

The foundation uses one operating model across the platforms you already own. Each platform contributes its native agent, data, security, and operating services; the shared architecture keeps grounding, policy, evidence, and cost control consistent across them.

One operating model across the platforms you run

The architecture connects four layers: the interfaces where people work, the agents and skills that act, the data and intelligence that ground decisions, and the models and infrastructure underneath. A control spine applies access policy, audit, evaluation, observability, and FinOps across every layer. Microsoft Azure, AWS, Databricks, and Snowflake provide the cloud and data foundations. Anthropic provides a reasoning layer across those choices. ServiceNow and Salesforce put agents into the operational systems where work already happens.

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, including ServiceNow, Salesforce, 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, agents do real work in production. Around all of it runs 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.

Under that boundary the runtime is composable and cloud-agnostic, so agents stay portable across the clouds you run rather than locked to one. An agent mesh handles service discovery, routing, health checks, rate limits, and a kill-switch, with a registry of every agent in production. Orchestration runs on Kubernetes with workflow tooling and canary releases. A model router and gateway front the model services on each cloud, Bedrock, Azure OpenAI, and Cortex, alongside a vector store, an MCP server and tool registry, and prompt management. Observability runs on OpenTelemetry and Grafana with drift detection, and policy-as-code applies across every layer. Security and identity wrap the whole runtime with OAuth and OIDC, an mTLS service mesh, managed secrets, zero-trust networking, and encryption in transit and at rest.

Agentify what you already run

The primary opportunity is to put 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 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.

ServiceNow: remove release handoffs and ground service agents

AccionStream moves update-set approval, retrieval, preview, commit, and collision feedback into ServiceNow. Reviewers approve or reject the DEV update set in the platform; approved sets load and preview in TEST; collision and preview errors return to the developer and reviewer without log exports and email chains.

For development work, a controlled Claude and MCP pattern drafts objects, fields, validations, Flow Designer logic, workflows, scripts, and tests. Scoped permissions, prompt validation, human review, approval gates, and audit records keep generated changes inside the same release controls as human-authored work.

An SKG records instance topology, configuration relationships, integrations, data rules, recurring failures, runbooks, and prior resolutions. That context grounds role-based agents for triage, development, workflow policy, data quality, integration recovery, knowledge curation, and release review.

Salesforce: connect Agentforce to governed enterprise context

Agentforce acts over the customer record, Data Cloud provides the grounding layer, and MCP connects the agent to data and tools outside Salesforce. Shared evaluation and policy checks apply across Service Cloud, Sales Cloud, Marketing Cloud, and industry products, so service, sales, and marketing agents can cite the data behind a recommendation and record each action.

Anthropic: carry the reasoning layer across cloud choices

Claude runs through Foundry, Bedrock, Mosaic AI Gateway, and Cortex, so the reasoning model can move without replacing the customer’s data, identity, policy, or audit services. Claude Code handles specification-led engineering, Agent SDK moves agents into production, and Skills plus MCP package operating instructions and tool access under the same control spine.

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.

For a sense of scale, the patterns above are grounded in active platform work: more than 20 agents in production across five industries, AI and cloud spend in the millions brought under FinOps control, and major-incident resolution moving from tens of minutes toward minutes. These are directional, and we set targets against your baseline before we start.

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, well-controlled, 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.