AI-powered automation
Intelligent process automation across operations — the repetitive analysis and hand-offs that quietly consume the most expensive engineering hours.
AI & agentic systems
Agents that act.
Context that persists.
Models that stay in your building.
Agentic AI and autonomous multi-agent systems, MCP-powered context intelligence, AIOps and predictive analytics — architected so the intelligence runs where your data already lives, with no external AI dependency and no telemetry leaving the network.
Deployment
100%
Self-hosted models — private cloud or on-premises
Data egress
Zero
No telemetry leaves your private network
Agent domains
4+
Network, security, performance, compliance
Isolation
Air-gap
Complete internet isolation available
01 — AI & machine learning
The foundation layer: prediction, perception, language
Six capability areas that most engagements draw from before anything agentic is introduced. Get these right and the agents have something reliable to reason over.
Predictive analytics
Machine learning models for forecasting, demand planning and risk assessment — with the feature pipelines and retraining loop that keep them honest.
Natural language processing
Document analysis, sentiment analysis and conversational interfaces — including intelligent document processing with OCR, classification and extraction.
Computer vision
Image recognition, quality inspection and visual analytics — deployed to edge inference hardware where latency and bandwidth rule out a round trip.
Real-time intelligence
Stream processing and real-time analytics for instant insight and fraud detection — decisions taken while the event is still happening.
Recommendation engines
Personalised recommendation systems for commerce and content platforms, tuned against the business objective rather than click-through alone.
02 — Agentic AI & multi-agent systems
Agents that execute, coordinate and escalate
Not a chatbot with a knowledge base. An orchestrated set of specialists, each fine-tuned on a domain, each able to use tools, each answerable to a master orchestrator that decomposes the task and synthesises the answer.
Master orchestrator agent
Task decomposition · Agent coordination · Response synthesis
Network analysis
BGP/OSPF · SDN · Routing · Traffic patterns
Security agent
CVE database · MITRE ATT&CK · Threat detection
Performance optimiser
Bottlenecks · Capacity planning · Tuning
Compliance & policy
PCI-DSS · SOC 2 · GDPR · HIPAA
Autonomous business agents
Self-operating agents that execute complex business tasks end to end, rather than surfacing a recommendation for somebody else to action.
Multi-agent orchestration
Coordinated networks of specialised agents, with a master orchestrator handling decomposition, sequencing and conflict between them.
Self-learning agent frameworks
Adaptive agents that continuously learn from interaction — so organisational expertise is encoded and retained rather than walking out the door.
Goal-oriented task automation
Agentic systems that take a high-level business objective and derive the plan, rather than requiring the plan as input.
Tool-using AI agents
Agents equipped to interact with APIs, databases and enterprise systems directly — with permissions, approval gates and a full audit record.
Conversational workflow agents
Natural language interfaces that let people delegate work to the system in their own words, in their own operational vocabulary.
03 — MCP context intelligence
The part everyone skips, and then rebuilds
An agent is only as good as the context it can reach. Most AI programmes stall not on model quality but on context: it is stale, it is scattered across six systems, it cannot be shared safely, and it does not survive the session.
We build Model Context Protocol platforms that treat context as infrastructure — aggregated, normalised, governed, versioned and continuous. Every agent in the estate draws from the same context surface, and every access to it is authorised and recorded.
The MCP-powered RAG pipeline pairs a vector store — Milvus, Qdrant or Weaviate — with runbooks, network diagrams and a historical incident knowledge base, embedded and retrievable in context.
- Unified context management platform — MCP-based systems that aggregate, normalise and maintain context across the estate.
- Real-time context synchronisation — dynamic updating across distributed AI systems, so two agents never reason from different truths.
- Multi-modal context integration — text, images, structured data and time-series combined into one addressable surface.
- Secure context sharing & governance — MCP-compliant distribution with access controls, so sensitivity travels with the data.
- Context-aware AI routing — intelligent routing of requests to the specialised model best suited to answer them.
- Persistent memory & continuity — long-term context retention across sessions, so the system remembers what it was told last quarter.
04 — Analytics & intelligence engine
Three tiers of detection, one root-cause engine
Statistical methods catch the obvious. Machine learning catches the multivariate. Deep learning catches the sequential and the relational. Running all three, and reconciling them, is what reduces false positives to something an on-call engineer will still trust at 3am.
TIER 01
Statistical methods
- Z-score and IQR outlier detection
- ARIMA / SARIMA time-series forecasting
- Prophet for seasonal patterns
TIER 02
Machine learning models
- Isolation Forest for multivariate anomalies
- One-Class SVM novelty detection
- Autoencoder reconstruction-error scoring
TIER 03
Deep learning
- LSTM sequence anomaly detection
- Variational autoencoders (VAE)
- Graph neural networks (GNN)
- Transformer multi-modal analysis
Anomaly detection categories
Performance
Latency spikes, CPU and memory saturation, throughput drops.
Network
Packet loss, routing anomalies, unusual traffic patterns.
Security
DDoS, port scanning, unauthorised access attempts.
Application
Error-rate increases, resource leaks, slow responses.
Infrastructure
Hardware failure, disk saturation, temperature events.
Root cause
Correlation across all five categories into a single causal narrative.
05 — Enterprise-wide AI capability
Where AI meets the rest of the estate
AIOps & smart operations
Next-generation IT operations with predictive monitoring, automated triage and incident correlation across the whole toolchain.
Intelligent process automation
RPA combined with AI for genuine end-to-end workflow automation, including the exception paths that break naive RPA.
AI-powered cybersecurity
Behavioural threat detection and zero-day exploit identification, with agents trained against CVE data and MITRE ATT&CK.
Intelligent document processing
AI-powered OCR, document classification and structured data extraction from the paperwork that still gates the process.
Enterprise conversational AI
Advanced assistants and omnichannel support that route to systems of record rather than to a generic FAQ.
Predictive analytics platform
A shared modelling platform for forecasting and risk assessment, so each new use case does not rebuild the pipeline.
Deployment posture
Private by design, not private as an upgrade
Most AI monitoring solutions require sending sensitive telemetry to external cloud providers — which violates telecom compliance obligations, sovereignty regulation and internal security policy for a large share of the organisations that most need the capability.
Our architecture assumes the opposite default. Models, embeddings, inference and vector storage all run on your infrastructure.
Self-hosted models
LLMs, embedding models, inference and classical ML — all running on your own GPU infrastructure.
No data egress
Zero telemetry leaves your private network. There is no external AI dependency to audit or renegotiate.
Private PKI
An internal certificate authority issues all TLS/SSL, with AES-256 encryption in transit and at rest.
Air-gap option
Complete internet isolation for the environments where that is the only acceptable answer.
Put agents to work
Start with one domain,
one agent, one measurable loop.
The programmes that succeed do not begin with a platform. They begin with a single painful, repeatable analysis task and an agent that closes the loop on it end to end.