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AI

Artificial
Intelligence

From custom autonomous agents and deep system integrations to training data and validation pipelines — we build AI that works reliably in production, not just in demos.

See success stories
100+ vetted AI community
contributors
99%+ validated data
accuracy rate
73% average uplift in
prediction accuracy
5+ industries
served
What We Deliver

The full AI stack, end to end

Custom Autonomous Agents

We design and build bespoke AI agents that perceive their environment, reason over structured and unstructured data, and take multi-step actions with minimal human oversight — going well beyond simple chatbots.

AI Integration

We embed AI capabilities — LLMs, vision models, classification engines — directly into your existing workflows, applications, and data pipelines, so intelligence amplifies the systems you already depend on.

Data Validation

Our crowd of 100+ vetted validators scrutinises your datasets for accuracy, consistency, and relevance across text, image, audio, video, and geo-local data. Multi-tier quality checks. Built-in seniority and spot-checking systems.

AI Training Data

We source, annotate, and curate diverse training datasets across modalities — text, image, audio, video, multilingual. Human verification at every step ensures your models train on data that reflects the real world, not its distorted proxy.

Intelligent Document Processing

We build AI pipelines that extract, classify, and route structured data from unstructured documents — contracts, invoices, forms, clinical notes — replacing manual data entry with automated, auditable ingestion at scale.

Predictive Analytics

We develop and deploy machine learning models for forecasting, anomaly detection, and risk scoring — turning your historical data into forward-looking intelligence that informs decisions before problems materialise.

Why AI

The gap between AI that demos and AI that ships

83% of AI projects never reach production. The failures aren't usually about model capability — they're about data quality, integration complexity, and the absence of production-grade engineering around the model. That's where we work. We don't hand you a proof-of-concept. We build the full pipeline, validate the data it trains on, and ensure it operates reliably inside your real infrastructure.

83%
of AI projects never reach production
$4.4T
potential value AI could add annually (McKinsey)
60–70%
of knowledge work could be partially automated
3.5×
higher ROI for AI-mature organisations vs early adopters
Success Stories

AI in production

Three examples of AI built to perform in real operational environments, not controlled conditions.

Banking Intelligent Document Processing Azure AI + Core Banking KYC Automation

85% faster loan origination through AI-powered document intelligence

A mid-tier retail bank's loan origination process was bottlenecked by manual KYC document review — identity documents, payslips, bank statements, and utility bills assessed by hand. xilix built an intelligent document processing pipeline on Azure AI that extracts, classifies, and validates all document types automatically, cutting origination time from days to hours and enabling the bank to process 4× the volume with the same team.

The Challenge

Loan officers manually reviewed stacks of submitted documents per application, cross-referencing data against core banking records to verify identity and income. Error rates were measurable, backlogs during peak periods stretched processing to five or more business days, and the team had no capacity to absorb volume growth without proportional hiring.

The Solution

xilix built an end-to-end document intelligence pipeline:

  • Multi-document classification — the model identifies document type, issuer, and validity state within seconds of upload, regardless of format or quality variation
  • Structured data extraction — name, address, income, and account details extracted with confidence scoring; low-confidence fields routed to human review only
  • Core banking reconciliation — extracted data automatically verified against existing customer records, with discrepancy flagging and fraud signal detection
Integrations
Azure AI Document Intelligence
OCR, classification, and structured extraction across all document types
Core Banking API
Real-time record reconciliation and applicant data verification
Outcomes
85%
faster loan origination
99.2%
extraction accuracy
4×
application volume, same headcount
60%
reduction in KYC processing cost
Fraud signals surfaced automatically
The model flags anomalies — inconsistent addresses, altered documents, mismatched income — that manual reviewers routinely missed under time pressure, giving compliance teams a risk-ranked queue instead of an undifferentiated stack.
Full regulatory audit trail
Every extraction decision, confidence score, and review flag is logged with timestamps, giving compliance officers complete, searchable evidence for regulatory examination.
Healthcare Predictive Analytics FHIR API + EHR Integration Readmission Prevention

28% reduction in avoidable readmissions through predictive risk scoring

A regional NHS trust was spending significant resource on reactive readmission management — identifying at-risk patients only after discharge, when intervention options were limited. xilix developed a predictive ML model integrated with the trust's EHR via FHIR API that identifies high-risk patients before discharge, enabling targeted clinical intervention while the patient is still in the building.

The Challenge

Clinical staff were making discharge decisions without a reliable view of readmission risk. Manual risk stratification was inconsistent across wards and often occurred too late for meaningful intervention. High-risk patients slipped through and returned within 30 days at significant clinical and financial cost to the trust.

The Solution

xilix built a predictive risk scoring system embedded directly into the clinical workflow:

  • Multi-factor risk model — trained on five years of anonymised patient data, incorporating diagnosis codes, comorbidities, prior admission history, medication complexity, and social vulnerability indicators
  • Real-time EHR integration — risk scores update dynamically as patient data changes during admission, surfaced within the existing clinical dashboard via FHIR API
  • Explainable risk flags — clinicians see not just a score but the top contributing factors, making the model's reasoning transparent and actionable for discharge planning teams
Integration
FHIR R4 API + EHR
Live patient data feed and in-workflow risk score delivery
Outcomes
73%
more accurate risk identification vs manual
28%
fewer 30-day readmissions
£3.8M
annual saving in avoided readmission costs
Day 1
risk scoring active from admission
Adopted without workflow disruption
The model surfaces scores inside the existing clinical dashboard via FHIR — no new system to log into, no change management requirement. Clinicians access AI-driven insight where they already work.
Model improves with every admission cycle
Continuous learning from new patient outcomes refines the model's predictions over time, with quarterly validation reviews ensuring performance stays calibrated to the trust's evolving patient population.
Telecom Custom Autonomous Agent Network Monitoring APIs Anomaly Detection

67% fewer unplanned outages through autonomous network fault prediction

A national telecom operator's network operations team was entirely reactive — engineers responded to faults after customer impact had already occurred. xilix built a custom autonomous AI agent that monitors network telemetry in real time, identifies anomaly patterns that precede failures, and initiates pre-emptive remediation workflows — converting a reactive support model into a proactive one.

The Challenge

The NOC team was operating on static threshold alerts that only fired after a fault was already affecting customers. False positive rates were high, alert fatigue was real, and the time between fault detection and engineer response averaged 47 minutes. Complex cascading failures — where one degraded component preceded a larger failure — were invisible until it was too late.

The Solution

xilix built an autonomous agent that operates continuously across the network telemetry stream:

  • Unsupervised anomaly detection — the model learns normal behaviour patterns per network segment and flags deviations with contextual severity scoring, replacing static thresholds
  • Cascading failure correlation — the agent identifies chains of related degradation signals across components, predicting compound failure risk before individual thresholds are breached
  • Autonomous remediation triggering — for known fault patterns, the agent initiates pre-approved remediation workflows autonomously, with escalation to the NOC only for novel or ambiguous cases
Integration
Network Monitoring APIs
Real-time telemetry ingestion, anomaly scoring, and remediation workflow triggers
Outcomes
67%
fewer unplanned outages
89%
fault prediction accuracy
4×
faster mean time to resolution
82%
reduction in NOC alert fatigue
Engineers focus on novel problems
Routine fault patterns are handled autonomously end-to-end. NOC engineers receive only high-confidence, contextualised alerts for genuinely novel situations — turning a reactive fire-fighting team into a strategic infrastructure unit.
Compound failure risk now visible before it materialises
The agent's cross-component correlation model catches cascading failure signatures up to 40 minutes before they would trigger conventional threshold alerts — giving the team a window to intervene before customer impact occurs.
Get Started

Have a dataset, a workflow, or a problem that needs AI?

Book a 60-minute discovery call. We'll look at your data, your current process, and tell you honestly what AI can and can't do for you — no hype, no inflated scope.

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