Copilot (Microsoft 365 Copilot, etc.)
DescriptionAI assistant integrated in productivity tools
Limit or scopeFollows the human step by step, does not execute autonomously.
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AI agent swarms designed, secured, and operated by Access to run your business value chains — under selective human supervision, in open MCP architecture, with no vendor lock-in.
The era of AI that responds to a prompt is behind us. The question for an executive in 2026 is no longer 'which chatbot do I install?' but 'which business processes do I entrust to an agent swarm that scans, distills, proposes, and executes continuously, under the control of a human orchestrator?'.
Access does not sell a closed product or a generic copilot. Access designs, secures, and operates custom agent swarms for your priority business processes.
DescriptionAI assistant integrated in productivity tools
Limit or scopeFollows the human step by step, does not execute autonomously.
DescriptionText or voice interface with contextual reply
Limit or scopeReactive to human prompt, not proactive.
DescriptionVisual automation of interface actions
Limit or scopeRigid, breaks on UI change, no judgment.
DescriptionMultiple specialized AI agents in continuous orchestration with selective supervision
Limit or scopeBounded by business and security guardrails, never without human on critical decisions.
MCP standard for connectors, substitutable LLM (Claude, Mistral, OpenAI, open-weight models), no lock-in. Radical agentic security by default.
ZSP · JIT access · TEE · multi-tenancy · GDPR / PDPL / NIS2 audit
Channels · supervision dashboard · FinOps · runbook
Triage · distillation · proposal · validation
Standard + custom MCP connectors · interface contracts
Sovereign or hybrid cloud · optional sovereign LLMs
Our ATLAS-Agentic methodology transposes the ATLAS Legacy framework (code modernization) to the agent swarm context: kill/go gates, parity proven by statistical double-run, observed-behavior register.
Scoping note, quantified success criteria.
Process mapping, source inventory, baseline KPI measurement.
Architecture diagram, connector list.
Agent specs, LLM choices, guardrails, selective supervision.
Swarm code, tests, runtime documentation.
Double-run swarm / human, agreement rate, offensive audit.
Progressive go-live, training, continuous observability.
Four Access pillars to bound the perimeter of a swarm that touches your production: prompt injection, data exfiltration, silent modification, tampered audit logs — new classes of risk that require a dedicated framework.
No agent has permanent rights. Permissions are granted per session.
Access keys live minutes to hours, then self-destruct.
Attacking agents continuously probe the perimeter of defensive agents.
Zero context mixing across clients or entities.
A coordinated set of specialized AI agents under shared orchestration.
A new role that supervises swarms without doing the work in their place.
A model where the human validates only critical decisions; routine ones execute autonomously.
Open interoperability standard between AI agents and enterprise systems.
Continuous loop scan → distillation → proposal → validation → execution → traceability.
Consolidated agentic dashboard. Distillation of operational signals into 3-5 strategic decisions per day. Human orchestrator freed from reporting.
Continuous anomaly detection, automated monthly closing, proactive multi-subsidiary alerts, cash forecasts and counterparty risk scoring.
Continuous sourcing, multi-platform screening, personalized onboarding journey, performance tracking, HR document management.
Lead qualification, proposal generation, opportunity scoring, campaign personalization, market and competitor intelligence.
Ticket triage, autonomous L1-L2 resolution, contextualized escalation, satisfaction tracking, self-enriching knowledge base.
Spend analysis, supplier management, procurement compliance, logistics forecasting, disruption anticipation.
Continuous regulatory watch, continuous transaction control, auditor agents (reverse offensive audit), anomaly register.
Augmented ITSM + ITOM, predictive alerting, 24/7 operations, automated low-risk change management.
A 4-agent swarm for the controlling function of a 12-subsidiary group: gap detection, reporting automation, proactive alerts, continuous audit. The CFO moves from 200 manual validations per month to 3 strategic decisions per day.
An AI orchestration platform across CRM, booking engine, email, and analytics. Per-traveler individualized decisions, replacing static rules. Originally designed for a Gulf airline.
Our products are organized by 6 business use cases. Each is modular, integrates into your ecosystem, and adapts to your industry. They fit into the agentic architecture presented above.
Connect your systems and keep humans in the loop.
View dedicated pageCollect, structure, leverage your data and market data.
View dedicated pageMeasure digital presence, compliance, AI positioning.
View dedicated pageIndustrialize video, audio and visual production.
View dedicated pageReach, qualify, convert on preferred channels.
View dedicated pageFeed your sales pipeline and talent pool.
View dedicated pageA copilot follows the human step by step inside their tool. An agent swarm continuously scans your environment, distills the important signals, and proposes or executes actions under selective supervision. The copilot completes a human task; the swarm runs a full business process in a continuous loop.
It depends on the chosen architecture. For ultra-sensitive data (health, defence, regulated finance), Access deploys in a sovereign environment — Vivantro France, European sovereign cloud, or on-premise — with sovereign LLMs (Mistral, open-weight models deployed locally). Doctrine: data stays client-side.
The ATLAS-Agentic methodology delivers a swarm in production in 4 to 7 months depending on complexity. Agentic Intake: 2-4 weeks. Build: 6-16 weeks. Supervised validation: 4-8 weeks. Go-live: 2-4 weeks.
No. Access is vendor-neutral by doctrine. The LLM is a substitutable component of the MCP architecture. You can start on Claude for reasoning quality, switch later to Mistral for sovereignty, or combine both by case criticality.
ROI is measured on three axes: human orchestrator time saved (typically −60 to −90 % on routine tasks), quality gain (reduced judgment variance, increased traceability), and scale capacity (the swarm absorbs peaks without hiring). The E1 scoping quantifies these axes for your case.
Any critical decision goes through human validation (selective supervision). Business guardrails bound the allowed actions. The observed-behavior register tracks every swarm / human divergence for tuning. Degraded mode is pre-wired to fall back to humans-only if the swarm becomes unavailable.
4 weeks of ATLAS-Agentic scoping to identify the first process to entrust to a swarm, measure expected ROI, and price the program.