IRAH
AI/ML engineering

Turn data into decisions—and decisions into better services.

We build production-grade AI systems around real workflows: document intelligence, computer vision, forecasting, anomaly detection, citizen assistance, RAG and operational analytics.

IRAH / ENTERPRISE PLATFORMLIVE
AI • DATA • TRUST
Capabilities

Applied AI for government and enterprise.

Our focus is not a demonstration model. It is a governed, measurable system integrated with existing data, applications and human decision processes.

Document intelligence

OCR, classification, extraction, validation and workflow routing for forms, certificates, invoices and legacy records.

Computer vision

Inspection, counterfeit detection, label validation, asset monitoring and visual quality control.

Predictive analytics

Demand forecasting, risk scoring, capacity planning, early-warning models and service prioritisation.

RAG & knowledge systems

Grounded assistants for policies, manuals, circulars, project documents and departmental knowledge.

Anomaly & fraud detection

Behavioural signals, velocity rules and machine-learning models to identify suspicious transactions and patterns.

Decision dashboards

Role-based analytics, geospatial views, alerts and explanation layers for administrators and field teams.

Responsible delivery

Governance is part of the architecture.

For public-sector AI, accuracy alone is not enough. Systems must be explainable, reviewable, secure and designed around human accountability.

Human oversight

Critical decisions remain reviewable with escalation and override paths.

Data governance

Purpose limitation, access controls, lineage, retention and quality checks.

Model monitoring

Drift, false positives, bias indicators, latency and service availability.

Secure integration

API controls, identity, audit logs, encryption and environment separation.

Delivery model

From data audit to production operations.

We reduce risk by proving value in stages.

01

Problem framing

Users, decisions, baseline metrics and failure impact.

02

Data readiness

Availability, quality, labels, privacy and integration constraints.

03

Prototype

Rapid model and workflow validation using representative data.

04

PoC

Controlled deployment with agreed technical and business metrics.

05

Hardening

Security, load, failure testing, monitoring and operational runbooks.

06

Scale

Phased rollout, training, governance reviews and continuous improvement.