Four orchestration products delivered in production
Each is industrialized. Two already run at our clients (think tank MENA agentic workflows, French telecom HITL Framework). The other two are ready for first sector deployment (tourism, banking).
10
In progress / availableAI Tourism Orchestration — Dynamic workflows on client log analysis
The right message, to the right visitor, at the right time. Dynamic workflows triggered by deep client log analysis.
AI orchestration layer for national or regional tourism ecosystems. Connects destination CRMs, hotel Property Management Systems, airline reservation systems, and analytics. Goes beyond communication orchestration: deep client log analysis (past stays, preferences, behaviors, signals) and dynamic workflow triggering (complementary booking, upsell, room transfer, local recommendation). Delivers the right contextual action at each step of the journey.
Key features
- Integration layer between destination CRMs, hotel PMSs, airline GDSs, analytics
- Deep client log analysis (stay history, preferences, behaviors, intent signals)
- Dynamic workflows triggered by contextual analysis (not just static rules)
- AI orchestration for personalization and smart upsell
- Real-time silo connection (booking, loyalty, preferences, cultural intentions)
Technologies
MCP (Model Context Protocol), LLM, log analysis and contextual scoring engine, CRM, PMS, GDS, data warehouse integrations.
HITL Framework — Human validation loop for AI
Keep humans in the decision on critical cases, at scale.
Industrial Human-in-the-Loop framework: human validation interface on AI outputs, case queue to arbitrate, confidence scoring, human/AI agreement metrics, continuous learning from corrections.
Key features
- Validation interface designed for production rhythm
- Case queue prioritized by criticality and AI uncertainty
- Confidence scoring and configurable thresholds
- Continuous learning from human corrections
- Human/AI agreement metrics and quality reporting
Technologies
React frontend, queue management, LLM integration, real-time metrics, EU AI Act compliance.
18
In progress / availableAI Banking Orchestration — Dynamic workflows on client log
Deep banking client log analysis, contextual workflow triggering: product reco, fraud alert, credit opportunity, complaint management.
AI orchestration layer for banking players: deep client log analysis (transactions, interactions, life events, risk signals) and dynamic contextualized workflow triggering. Product recommendation (savings, credit, insurance) at the right time, fraud alert based on behavior deviation, contextual complaint management, upsell or cross-sell opportunity at the opportune moment. Designed for strict regulatory compliance (KYC, GDPR, banking secrecy, AML).
Key features
- Deep client log analysis (transactions, interactions, behaviors, signals)
- Dynamic workflows triggered by contextual analysis (not just static rules)
- Contextual financial product recommendations (savings, credit, insurance, investment)
- Real-time anomaly and fraud signal detection
- Contextual complaint management with full client history access
Technologies
MCP (Model Context Protocol), LLM, log analysis and contextual scoring engine, core banking, CRM, fraud detection, data warehouse integrations.
Agentic workflows — Multi-agent AI orchestration in production
Run multiple specialized AI agents together, on real business task chains — without breakage and without rogue agents.
Design and operation of agentic workflow chains: several specialized AI agents (research, writing, validation, publishing) coordinated around explicit orchestration (n8n, Airflow, custom). The promise is not multi-agent magic: it is the **discipline of a chain that runs in production every day**, with human checkpoints, decision logging, source allowlists, and inference-cost governance. Validated in production on a real Middle-East economic think-tank case since late 2025.
Key features
- Explicit agentic chain design (no opaque auto-orchestration): every step declared, every LLM call traced, every transition documented
- n8n stack (recommended), Airflow, or custom orchestration on Cloudflare Workers / Node depending on client constraints
- Task-specialized agents (sourced web research, long-form writing, factual validation, CMS publishing, Gamma slide generation) rather than autonomous generalist agent
- Configurable human checkpoints (HITL) on risky steps: final publish, spend > threshold, source allowlist exit
- Source allowlist and complete decision logging: we know why an agent chose this source, this phrasing, this action
Technologies
n8n self-hosted or cloud (default recommendation), alternatively Airflow or custom orchestration. Mixed LLM per step: Claude (long-form writing, reasoning), OpenAI GPT-4o (multi-modality, speed), open-source models via Groq for non-critical steps. WordPress, Gamma, Notion, Google Workspace connectors, business APIs.
