Four disciplines. One operating model: governed autonomy.
Each discipline is engineered to solve a specific enterprise friction point — or combine into a complete, governed AI capability connecting your front-line portals to core back-office systems.
Agentic Workflow Automation
Multi-agent systems that coordinate complex, multi-step operations — document verification, registry triage, and cross-system reconciliations. Agents reason through edge cases rather than breaking on unexpected inputs.
Core capabilities
- Unstructured document triage & data extraction
- Human-in-the-loop approval workflows
- Dynamic policy checking against academic regulations
- Immutable audit logging for regulatory appeals
Secure Localised LLMs
Deploy open-weight and enterprise language models inside your own cloud tenant or on-premise hardware. Sensitive student data, research IP, and commercial contracts never leave your agreed security perimeter.
Core capabilities
- Tenant-isolated Azure OpenAI and open-weight models
- Zero-data-retention model configurations
- RAG pipelines grounded in institutional policy documents
- Custom Model Context Protocol (MCP) tool integration
AI Infrastructure & Integration
Resilient integration layers connecting CRMs, student records, and virtual learning environments into coherent data pipelines. Built on enterprise messaging brokers like Azure Service Bus.
Core capabilities
- Salesforce to SITS event-driven synchronisation
- Dead-letter queue monitoring & automated recovery
- Near-real-time telemetry and operational dashboards
- Idempotent message handling with zero lost transactions
Legacy System Modernisation
A disciplined path from fragile legacy estates to AI-ready infrastructure. We wrap older databases and monolithic systems in clean API contracts, modernising user experience while safeguarding core records.
Core capabilities
- Legacy student record database migration & refactoring
- Relational schema optimisation and deadlock reduction
- Clean REST & GraphQL abstraction wrappers
- Data warehouse feeding real-time Power BI reporting
Systems & Architecture
Platforms and enterprise estates we integrate
We don't ask you to replace your operational infrastructure. Our engineers build robust, compliant middleware connecting the platforms you already run into a unified event-driven pipeline.
Common Enquiries
Frequently asked questions about our services
Can these integration and AI services be deployed incrementally?
Yes. We explicitly avoid high-risk 'rip-and-replace' programmes. Our delivery model starts with a scoped pilot on a single, high-friction workflow (such as Clearing document triage, coursework extension routing, or Salesforce-to-SITS message buffering). Once proven in production against agreed operational metrics, the architecture is hardened and expanded across the wider estate.
Do our internal teams need machine learning specialists to maintain what you build?
No. We build on open, maintainable standards — standard Azure services, containerised microservices, and well-documented REST/GraphQL APIs. Every deployment includes complete architectural documentation, runbooks, and staff handover sessions so your existing IT and systems teams can operate and troubleshoot the systems independently.
How do you prevent agents from making unauthorised writes to core databases?
Agents operate strictly under the principle of least privilege. They communicate through controlled API endpoints and message queues, never through direct unvalidated database writes. Furthermore, all consequential actions (such as fee updates, grade changes, or disciplinary notifications) require explicit human approval via a staff control plane.
What infrastructure is required to host private or localised language models?
We typically deploy models within your existing cloud tenant (such as Azure OpenAI Service in UK South with zero data retention policies) or on dedicated virtual machines running open-weight models (such as Llama 3 or Mistral). All data stays within agreed UK/EEA boundaries, completely isolated from public model training loops.
What is the timeline for an initial architecture review and pilot?
Our AI Architecture Review is a fixed-scope engagement completed in ten working days. It delivers a full systems map, data risk register, and scoped pilot design. A subsequent proof-of-value pilot typically runs between four to six weeks to reach production readiness.
Not sure which service fits your current bottleneck?
An AI architecture review maps your workflows and data models, identifying precisely where automation and integration will deliver measurable payback without operational disruption. Fixed scope, ten working days, from £7,500 + VAT.