SPIREX®
Agentic orchestration framework

The engineering that puts your AI agents into production

ACL's framework that takes your AI agents from demo to real operation.

Building a prototype with AI is easy; keeping it in production is engineering. SPIREX® orchestrates the layer between your backend and the models —security, stability and performance— so that AI runs in the core of your business, not just in a demo.

Book a POC
WITH SPIREX®

Governed AI, integrated with your backend.

  • AI agents connected to your critical systems
  • Your teams' technical knowledge, centralized
  • Faster, more consistent software development
  • Code quality and security protected
WITHOUT SPIREX®

Disconnected tools, uncontrolled risk.

  • Scattered assistants, each with its own criteria
  • Hallucinations with no auditing or traceability
  • Reliance on consumer-grade interfaces
  • Intellectual property exposed and unintegrated

SPIREX®, the difference between experimenting with AI and governing your infrastructure

What it does

Agentic orchestration

Connects multiple autonomous agents under a single deterministic flow, eliminating information silos and manual dependencies between teams.

What value it delivers

Active oversight

Decisions are made with Human-in-the-Middle (HITM) on critical matters. Every system action is traceable, reversible and auditable.

Key benefit

Uncompromised sovereignty

Your data and models never leave your perimeter. Your operation's intellectual property stays under your jurisdiction, not that of an external provider.

The software lifecycle, governed

AI executes. The human decides.

The five pipeline phases are the software development lifecycle (SDLC) your team already knows: SPIREX® does not propose a new process, it is a faithful reflection of yours, with an accountable agent and a human gate at every stage. And because the pipelines are declarative, the flow is built from the stages, roles and approvals your organization already has.

01
Discover
AI Designer · Figma MCP
Human validation
02
Define
AI PM · Jira MCP
Human validation
03
Build & Test
AI Developer · AI QA · Git MCP
Human validation
04
Deploy
CI/CD
Automatic
05
Operations
Monitoring · auto-heal
Continuous
Principle · Zero Trust

Human-in-the-Middle

Who decides

The human retains absolute authority at every phase transition of the pipeline.
No AI output is assumed valid until someone approves it.
Every approval is recorded with user, timestamp and comment.
Strategy

Governed acceleration

What the team gains

AI cuts the time spent writing, designing and testing — it does not replace judgment.
The final call on design, code and deployment stays with your team.
Speed without losing control: predictable, auditable and scalable to enterprise teams.

The difference between a generic copilot and an agent that knows your business

An enterprise agent is made of two complementary layers: what it knows and what it can do.

Intelligence

Skills

What the agent knows

What they are
Files injected into the model's context before it runs. They define criteria, standards and business context.
Examples
INVEST criteria, architecture patterns, your business glossaries, OWASP, TDD and local directives.
Governance
Versioned as code in Git, peer-reviewed and reproducible per project with skills.lock.
Action

MCP Servers

What the agent can do

What they are
Two-way interfaces invoked in real time during reasoning, against your actual tools.
Examples
Create Jira issues, clone a repository, export from Figma, run analysis in SonarQube.
Governance
Credentials scoped per project, resolved at runtime and never exposed to the model.
Intelligence (Skills) + Action (MCP) × Governance (Registry) = a software development lifecycle (SDLC) with AI that is fit for production

The framework never trusts by default

Every skill runs in isolation, every action is signed and all of your data stays in your tenant.

100%
of client data, inside their own tenant
100%
of AI actions, traceable by execution identifier
0
credentials in code or in prompts
3+
mandatory human validation points per pipeline

Sandbox per skill

Each agent runs in an ephemeral container with restricted network and file system. No persistence between runs and no lateral access to other clients.

Secrets outside the model

Jira, Figma, GitHub and database tokens are resolved at runtime from a secrets manager: they never appear in the model's context.

Data residency

Prompts, contexts and outputs are processed in your contractual region, on private models with zero retention and no training on your data.

Signed audit trail

Every run records the skill, input and output hashes, human reviewer, timestamp and signature. Any decision can be reconstructed end to end.

SOC 2 readyISO 27001GDPR-safeNo training on client data
Two fits, not one

There are two ways SPIREX® enters your operation

Depending on the problem you are solving, the framework comes in as the way you execute or as an accelerator for development already under way.

Fit 01 · Governance

“I need to take AI agents to production without losing traceability or control over who approved what.”

SPIREX® is the way you execute: an end-to-end agentic pipeline, human gates at every transition and auditable evidence for every decision. This is the conversation in regulated sectors, where speed alone is not enough to reach production.

Talk about governance
Fit 02 · Acceleration

“I have a development project with a committed date and I want to deliver sooner, without lowering the quality bar.”

SPIREX® comes in as an accelerator on a conventional project: your team keeps its process and its stack, and the framework adds speed, consistency and quality control over the code being produced.

Talk about timelines

SPIREX® in the field: validated results

Active deployments in industries where mistakes are not an option.

3-5×
faster from Epic to stories ready for development
−40%
less rework from defects found after merge
4 weeks
from POC to measurable results in real operation
Time from epic to stories ready for development
Before
5-8 days · manual analyst
→
With SPIREX®
4-6 hours · agent + human validation
Post-merge defects returned by QA
Before
~12 per sprint
→
With SPIREX®
~7 per sprint
New code that inherits the client's standards
Before
~15%
→
With SPIREX®
70%+
Time-to-first-commit for a new joiner
Before
10-15 days
→
With SPIREX®
2-3 days

Measured with the same formulas your team already uses: a baseline in the sprint before the pilot, one concrete pain point and one agent, reviewed at the close of every sprint.

Three ways to bring it into your operation

From a contained diagnosis to a closed-scope delivery, depending on how much you want to commit up front.

Entry pointLimited scope

Discovery

Before accelerating anything, SPIREX® maps the system you already have: living documentation and a dependency graph of your code, generated by the agents and validated by your team.

Ideal for
Legacy systems nobody documented, or sizing a project before committing budget.
What you get
The map of the system —modules, dependencies and risks— and a recommendation on where to start.
Format 01Time & materials

Outsourcing AI

Our professionals join your team with SPIREX® already running on their workstation. They pick up tickets from your Jira, work to your standards and your sprint cadence, and report to your tech lead.

Ideal for
Growing the team without being slowed down by hiring, while keeping operational control.
What you get
Full stack, migration and testing profiles, with the shared skills registry and a weekly throughput and quality report.
Format 02Outcome-based · fixed scope

Software Factory

You define the outcome — a migration, a new module, a certification — and the ACL cell delivers it end to end with SPIREX®. Your team reviews at every gate; it does not execute.

Ideal for
Delegating contained deliveries with demanding deadlines: legacy migrations, closing regulatory gaps or a module with a firm date.
What you get
The outcome in production and certified, with test coverage ≥ 90%, a signed audit trail and closed scope.

Frequently asked questions

Does SPIREX® replace Claude, ChatGPT or other language models?

No. SPIREX® is the infrastructure that governs them and securely connects them to your transactional systems. The gateway is multi-vendor —Anthropic, OpenAI, Gemini, DeepSeek, Mistral (EU), Groq/Llama and Copilot— with a failover chain between them. You arrive with your own models and contracts, and SPIREX® plugs in without asking you to migrate.

How long does it take to show the first results?

We design a Proof of Concept (POC) tailored to your technology stack. The cycle takes up to 4 weeks from kick-off to having agents running in production with measurable metrics.

Does my data leave the company during implementation?

No. SPIREX® is deployed as a container inside your own VPC: neither the code nor the data leaves your tenant. Private models (Vertex, Bedrock) run in your contractual region, with zero retention and no training on your data.

Do I have to change the way my team works?

No. The pipelines are declarative: you describe the flow you already have —your stages, your roles and your approval points— and SPIREX® runs it as is, without touching code. That is why it is a faithful reflection of your development lifecycle, not someone else's process.

Does SPIREX® replace the copilot my developers already use?

No, and they are best used together. A copilot helps one person inside their editor; SPIREX® governs the whole cycle —from requirement to deployment— and leaves auditable evidence of every step.

Ready to see SPIREX® in your operation?

We'll schedule a free technical session to map your specific case.

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