Xect AI combines industry data models, event processing, workflow orchestration, and human review. These capabilities provide a common foundation for deploying and managing automation across connected enterprise systems.
You describe what you need. Xect AI selects the relevant schema, maps it to your enterprise data, configures rules, validates against representative scenarios, and routes the result for approval before deployment.
"Build an agent that handles all attachments"
Schema, terminology, regulatory reference structures and vendor context are added automatically from the configured Knowledge Graph.
Domain-aware and pre-loaded with schema, guardrails and industry logic, ready for review before deployment.
Industry-native agents start from an existing schema and context instead of a blank prompt.
Industry schemas, terminology, and configured business rules provide a starting point for solution design. Teams adapt this context to their data, policies, and operating requirements before deployment.
Network event taxonomy, service order lifecycle, and NOC escalation paths, provided as a reference schema you configure for your environment.
Reference data types aligned to HL7/FHIR terminology and prior-authorization rules, configured and validated by your compliance team before use.
FNOL schema, policy data models, and configurable state compliance flags, adapted to your lines of business during setup.
Reservation lifecycle, housekeeping workflows and guest loyalty attributes, mapped.
Sample lifecycle, LIMS structures, and configurable GxP reference rules, validated by your quality team before use.
Xect's team encodes your operations into a graph built for you alone.
Talk to usThe graphs are built by practitioners, not scraped from training data. Field experience is encoded as structured schemas and ontologies that agents reference directly.
When you ask Xect AI to build an agent, it starts from the schema and terminology already configured for your industry, not from an empty prompt.
Agents handle the routine. People decide the exceptions. The workflow pauses at uncertainty and resumes the moment they act.
AI handles most operational work. When an agent hits ambiguity, a policy conflict, missing information, or a high-impact call, it routes the exception to an operator. The operator sees the underlying records and a recommended response, then approves, rejects, or amends the action before the workflow resumes.
The handoff is built in. No disconnected ticketing systems. No starting over.
The agent stops at the exact point of uncertainty and waits for a decision.
Full event history, the agent's reasoning and the recommended next step. All in one view.
Review, approve, modify or escalate. Everything they need to decide, in one place.
Automation resumes from exactly where it paused. The decision is captured and logged for later review.
Route operational messages using configured policies and recipient preferences. Track delivery status, apply supported fallback channels, and return responses to the relevant workflow. For full application connectivity beyond notifications, see AI-Native Integration.
Register a connector once — Twilio for SMS and WhatsApp, SMTP or SendGrid for email, a webhook for anything else. The Notification Router Agent picks the channel from the policy attached to the event, not from a hardcoded rule per flow.
Twilio-backed delivery for time-critical alerts. Two-way replies route straight back into the flow that sent them.
Transactional email through SMTP or SendGrid, plus mobile push for app-based teams. Templates stay versioned per flow.
Post directly into the channel your team already watches, or hand off to any system over a signed webhook.
The channel is a policy decision, not a hardcoded integration. Change the routing rule, not the flow.
An agent flags a condition — an outage, a billing correction, an SLA breach — and attaches the notification policy for it.
The Notification Router Agent reads the policy and the recipient's preference, then selects SMS, email, push or chat.
Each connector reports delivery status back. A bounce or timeout triggers the fallback channel automatically.
A reply to an SMS or a click in an email resumes the flow that sent it, with the response attached.
Define and manage business events from connected systems, such as linking a payment failure to the affected account. Maintain versioned data structures and make event data available to operational workflows and performance dashboards.
Paste a sample payload and Xect AI proposes the schema you register and control. Producers and consumers stay decoupled — evolve a schema without breaking the flows downstream of it.
Drop in a JSON sample and Xect AI maps it to an Avro schema — register your own or refine the suggestion.
Schema Registry tracks every version so producers and consumers can evolve independently and stay compatible.
Events stream over Kafka with per-org topic isolation — one tenant never sees another’s data.
Define an event, map it to a use case, and map your fields — the platform provisions the pipeline for you.
Register any business event by dropping in a single sample JSON payload.
Pick a pre-defined use case so your data arrives structured and analytics-ready.
Visually map each field to the chosen use case, so every value lands correctly.
One click provisions a dedicated Kafka topic and a live EPM analytics table.
Every registered event provisions its own analytics table. XECT AI EPM turns that live stream into validated dashboards — real-time over Socket.io, no SQL, no ETL. Drill from high-level groups down to individual activities, and compare “This Month” against any range to catch a shift the moment it happens.
Purpose-built for operations, product and business teams who need to act on data, with less dependence on data engineering for routine reporting.
Unify event data from connected systems into one accessible analytics layer, built once.
Go from a raw event to a rich, working dashboard in minutes, not weeks or months of backlog.
No custom pipelines, ETL code or database wrangling. The platform provisions and runs all of it.
Analytics land in the hands of the people who need them, without writing SQL or custom queries.
Trends, anomalies and opportunities get flagged early, so teams act before issues escalate.
Reconstruct recorded process activity from event data to compare execution paths, identify bottlenecks, and quantify rework. Reduce manual mapping by using available event histories, with business validation of the resulting process model. Use the findings to prioritize improvements with process owners.
Every registered event carries a case ID, an activity and a timestamp. The Process Miner replays that trail across every case and reconstructs the process graph as it actually ran, with every path, loop and rework it finds. Deviations from the intended flow show up as a variant, not a guess.
Every case ID and activity in the event stream becomes a node on the graph. No manual process mapping.
Ranks every step by wait time and rework frequency, so the slowest handoff is never buried in a report.
Compares every real path against the intended one and shows which cases deviated, and by how much.
The same events already flowing through the platform. No separate logging, no export to a mining tool.
Every registered event is tagged with the case, activity and timestamp it belongs to, as it's ingested.
Every case's activity sequence is reconstructed and merged into a single process graph.
Loops, delays and off-path cases are ranked by how often they occur and how much time they cost.
New events redraw the graph automatically, so the map reflects this week, not last quarter's audit.
Book a 30-minute demo and see the Knowledge Graph, Human in the Loop, Business Operators Hub and the rest of Xect AI working together in your environment.