AI shouldn't stop at a chat box.
It should land in your workflow.
Three product lines: autonomous coding, data intelligence and IP matrix. What they share — you get delivered outcomes, not a half-finished kit you have to wire up yourself.
One foundation, three applications.
Writing software, reading data, producing content — the same ability to wire AI into real processes.
Autonomous coding
From requirement to go-live without writing a line of code. Five AI specialists — requirements, architecture, coding, testing, operations — deliver together.
Data intelligence
Ingestion, AI cleansing, sub-second warehouse loading and the GBI decision layer. Turn scattered data into usable fuel.
IP matrix
An early-stage productised capability for IP operations — content production, multi-platform distribution, data-driven operation.
Not autocomplete — end-to-end delivery.
You describe business intent; the engine coordinates specialists to design, build, test, deploy and operate.
Requirements specialist
Parses business intent into verifiable technical requirements and acceptance criteria.
Architecture specialist
Module boundaries, interface design and technology choices, automated.
Coding specialist
Generates maintainable, reviewable code against established practice.
Testing specialist
Generates test cases, runs regression, finds boundary failures.
Operations specialist
Deployment, monitoring, alerting and autoscaling to keep the system up.
How it differs
Most tools stop at function-level completion or drag-and-drop low-code. This delivers complete engineering — auditable architecture, reversible code.
Generated work must be reviewable and reversible.
Verifiable requirements
Every requirement carries acceptance criteria, checked before and after generation.
Auditable design
Architecture decisions and code changes fully traceable, reviewable at any time.
Reversible code
Version-controlled; any change can be safely rolled back.
Observable runtime
End-to-end monitoring and alerting, anomalies caught immediately.
From ingestion to decision, without the T+1 wait.
A real-time engine built for trillion-row scale, with second-level latency from ingestion to insight.
One-click ingestion
Relational, distributed, warehouse, NoSQL, message queues, files and object storage.
AI cleansing
Generates continuous Flink SQL jobs automatically; humans assist conversationally.
Real-time loading
Billion-row ingestion in seconds; warehouse APIs for high-concurrency queries.
GBI decision layer
Q&A, table, dashboard, insight and profiling specialists, plus numeric alerting.
Security & sovereignty
Private deployment; data stays in your environment; field-level permissions and full audit logs.
Cost structure
AI replaces labour-heavy development and tuning; hardware scales with load.
Productising "building an IP".
Positioned by the vendor as an early-stage capability with commercial partnerships open.
Content production
AI-assisted articles, video and courses — solving cost and throughput.
Multi-platform distribution
Produce once, adapt to each channel, avoid repetitive manual work.
Data-driven operation
Track influence, audience profile and conversion paths in one place.
First question: does it solve the thing in front of you?
Tell us where you're stuck — we'll say which line fits, or that none does.