Technical architecture
Advanced Workflow Optimization
Proprietary stack, multi-agent orchestration, and a centralized knowledge layer for high-complexity enterprise automation.
Structural limitations of the dominant approach
Most AI automation platforms currently available on the market share the same basic architecture: prompt chaining on general-purpose LLMs consumed via API, with channel-specific agents built and maintained separately.
This approach introduces structural limitations that become particularly evident in production:
- Non-deterministic latency on multi-step tasks, incompatible with real-time use cases such as voice interaction
- No shared state between agents operating across different channels — each agent reasons in isolation
- Knowledge fragmentation: updating a business procedure requires changes to each agent separately, creating a high risk of behavioral inconsistency
- Insufficient audit trails for regulated environments, as general-purpose models do not expose structured logging at the process-step level
- Single-vendor dependency for the LLM model, where changes in pricing, availability, or model behavior directly impact production.
The architecture that enables automation
Shared Process Knowledge Layer
The central component of the architecture. It is a structured process knowledge layer, independent of the channel, that powers all agents operating within the system. It is not a shared prompt, but a knowledge base organized around entities, procedures, business rules, and operational contexts.
Every agent, regardless of the channel it operates on, queries this layer to determine the correct behavior. Updating a procedure means modifying it in the knowledge layer: changes are propagated immediately and consistently across email, chat, voice, and APIs without requiring any changes to individual agents.
This eliminates the problem of knowledge fragmentation at its root and dramatically reduces the cost of maintaining production systems.
Multi-Agent Orchestrator
The orchestration engine manages coordinated agent pipelines with:
- Explicit state management — each workflow maintains context across subsequent steps, including non-contiguous sessions.
- Conditional routing — the pipeline dynamically branches based on the output of each step, with support for complex conditions and lookups against external systems.
- Configurable fallback and retry — degraded behavior is explicitly defined for each failure point, preventing silent error propagation.
- Human escalation — configurable triggers for handoff to a human operator, with the complete session context transferred.
- Structured logging — every step of every pipeline generates a structured event containing timestamp, input, output, agent involved, and system called: a complete and queryable audit trail.
Fine-Tuned LLMs for Vertical Domains
We do not use general-purpose LLMs as-is. Our stack is built on base models fine-tuned for specific domains, with explicit optimization of the latency/accuracy trade-off required by the operational context.
For real-time use cases, particularly voice interaction, latency optimization is a primary design requirement, not a secondary parameter. The Voice AI component operates with target end-to-end latencies below 200ms, including speech recognition, LLM inference, and speech synthesis.
Vertical fine-tuning also delivers direct production benefits:
- Higher accuracy within the specific domain
- Lower hallucination rates on business entities
- More stable and predictable behavior compared to general-purpose models
Connectors and Integration Layer
An API-first integration layer with native support for:
- REST and GraphQL for modern systems with well-documented APIs.
- Webhooks for push events from external systems into the knowledge layer.
- Adapter pattern for legacy systems that do not expose native REST APIs — the connector implements the transformation logic without modifying the source system.
- Event streaming for high-volume data pipelines with low-latency requirements.
- Stateless and idempotent connectors by design, compatible with multi-instance environments and container-based orchestrators.
Agent state and memory management
A critical aspect of complex agentic pipelines is managing memory across steps and sessions. Our architecture explicitly distinguishes three levels of memory:
- Working memory — context from the current session, maintained in-process to minimize latency
- Session memory — state persisted across subsequent interactions of the same user or process, retrievable across different channels
- Organizational memory — structured knowledge within the knowledge layer, shared across all agents and centrally updateable
- This separation enables the creation of agents that maintain consistency over time without requiring extremely large context windows — with a direct impact on inference cost and latency.
Observability and production control
Every system component exposes metrics and structured logs compatible with standard observability stacks:
- Logging: structured JSON events for every pipeline step, ingestible by Elasticsearch, Datadog, Splunk, or custom systems
- Metrics: latency per step, throughput, fallback rate, human escalation rate — exportable via Prometheus or custom endpoints
- Distributed tracing: correlation of events across agents and channels through a trace ID propagated throughout the entire pipeline
- Operational dashboard: real-time visibility into the status of active pipelines, with configurable alerting for critical thresholds
Security and data governance
- Inter-service authentication via mTLS and API keys with granular scopes
- No persistence of personal data outside the customer's systems — the knowledge layer operates on anonymized or pseudonymized entities by design
- Encryption in transit and at rest across all components
- Infrastructure deployable on European cloud providers with guaranteed data residency — compatible with GDPR, DORA, and regulated-sector requirements
- Role-based access to the logging system and audit trail