Agentic operating-model experiment
A multi-tenant business workflow prototype
A multi-tenant business-workflow prototype with departmental modules, queued agent execution, retrieval memory, and model routing. The source demonstrates orchestration boundaries, not autonomous business operation.
Business ↔ technology bridge · Agentic operating-model experiment
Explores tenant-scoped agents, organisational memory, queued execution, and human review boundaries.
This is a portfolio and source interpretation—not a customer, deployment, certification, or outcome claim.
Departmental workflows and knowledge are fragmented, while a chat interface can conceal who owns an action, which data it used, and whether work completed.
Scope data by tenant, queue agent work, separate model routing from business modules, and retain human review notices.
Web, API, worker, database, agent, model-routing, retrieval-memory, schema, and selected test packages.
Tool results, queue state, retrieval provenance, and review outcomes return to workflow design, permissions, staffing, and operating controls.
Source evidence
A multi-tenant business-workflow prototype with departmental modules, queued agent execution, retrieval memory, and model routing. The source demonstrates orchestration boundaries, not autonomous business operation.
Repository evidence is not client, deployment, certification, or outcome evidence.
Architecture
Three source boundaries: a business workspace, queued departmental agent workflows, and a retrieval-memory layer scoped by organisation.
Source map only: a layer can represent implemented code, an adapter boundary, or documented design. It does not assert a configured production service.
9 department modules under one Next.js shell: Sales, Marketing, Support, Finance, HR, Knowledge, Analytics, Compliance, Operations. Multi-tenant by design. SOC 2-aligned auth. The single business OS that replaces a dozen point tools.
Modules expose specialised agent definitions for lead scoring, planning, triage, invoice review, recruitment support, and policy review. Provider and locality behaviour depend on configuration; no sovereignty guarantee is asserted.
Document chunking, vector embeddings, and retrieval are represented in source. Grounding quality depends on the ingested corpus, access controls, retrieval evaluation, and human review.
The Problem
Why business operations are broken
Separate CRM, marketing, support, finance, and HR systems that don't talk to each other. Teams waste hours copying data between platforms.
ChatGPT can write emails but doesn't know your sales pipeline, customer history, or company policies. Generic AI adds noise, not value.
Critical institutional knowledge lives in people's heads, scattered docs, and old emails. When someone leaves, the knowledge walks out the door.
GDPR, the AI Act, contracts, and sector rules can create different logging, oversight, security, and transfer obligations. Hosting geography alone neither establishes nor prevents compliance; roles, data flows, subprocessors, controls, and evidence all matter.
Modules
9 modules with 27 specialized AI agents — 3 per department
Source scope only: an entry can describe implemented code, a documented adapter, or a configured workflow. It is not a statement that a production service is active.
01
Lead scoring, deal tracking, and proposal generation. AI agents: LeadQualifier, ProposalWriter, DealCoach — each trained on your pipeline data.
02
Campaign management, content creation, and audience segmentation. AI agents: ContentStrategist, SeoWriter, CampaignManager.
03
Ticket management, AI-assisted resolution, and escalation routing. AI agents: SupportAgent, EscalationRouter, VoiceOfCustomer.
04
Invoice processing, expense tracking, and budget management. AI agents: InvoiceParser, Bookkeeper, FinancialAnalyst.
05
Employee management, recruitment pipeline, and PTO workflows. AI agents: Recruiter, OnboardingGuide, HRPolicy.
06
Document management with semantic search and Company Memory. AI agents: KnowledgeAssistant, MeetingScribe, ContractReviewer.
07
Business reports, trend analysis, and custom dashboards. AI agents: BusinessAnalyst, CompetitiveIntel, TrendSpotter.
08
Audit logs, compliance scanning, and regulatory checks. AI agents: ComplianceMonitor, GDPRAgent (Article-specific violation detection, data inventory, legal basis assessment), LegalReviewer.
09
Workflow automation, task management, and process improvement. AI agents: WorkflowAutomator, OpsAnalyst, ProcurementAgent.
9 departments, 27 lead agents
Each module ships with a small ensemble of specialised agents (3 per department on average). Below: the lead agent in each — the one that's first contact for that department's day-to-day workflow.
Repository surface only: availability, completeness, performance, and operational fitness require separate validation.
SALESScores inbound + outbound leads against your ICP, drafts the next-best-action, and surfaces stalled pipeline items. Reads your CRM directly; doesn't depend on a separate sync.
MARKETINGDrafts campaign briefs, channel options, copy, and KPI hypotheses from supplied context. Brand and performance claims require human review.
SUPPORTClassifies new tickets by severity + topic, suggests a first response from your knowledge base, and routes escalations to the right human reviewer.
FINANCERepresents invoice extraction, purchase-order comparison, exception flags, and close-summary drafts. Financial authority and data locality remain external controls.
HRParses CVs against the JD scorecard, flags interview-worthy candidates, drafts personalised outreach. Tracks the funnel and surfaces drop-off points.
KNOWLEDGERepresents ingestion, chunking, deduplication, vector retrieval, and cited answers for configured documents. Coverage is limited to authorised, successfully indexed material.
ANALYTICSWatches business KPIs for statistical anomalies, runs root-cause hypotheses against the data lake, and drafts the executive narrative for the monthly review.
COMPLIANCERepresents policy checks and draft evidence records for selected workflow outputs. It does not establish legal completeness or compliance.
OPSProfiles repeated multi-step workflows, suggests where AI agents can compress them, and ships the new workflow with rollback. Quantifies time + cost saved per iteration.
Tech Stack
Named technologies identify repository dependencies or adapter surfaces; they do not assert a live integration or deployment.
Source Code
The Athena AI platform source code is available upon request for evaluation and partnership purposes.
To access the source code, please sign our Non-Disclosure Agreement. This is a standard legal formality.
Need help designing AI-powered operations platforms? Our consulting services can accelerate your journey.
Partnership
Whether you need an AI-powered operations platform or want to explore a white-label partnership for your organisation.
Most AI pilots stall before production. Get the playbook for the ones that ship.
Unsubscribe anytime. No spam, ever.