13 services

Artificial Intelligence

Applied AI engineered into the systems, workflows and decisions that run the enterprise.

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Overview

Artificial intelligence is now an engineering discipline as much as a research one. Durable value comes from treating models as components in larger systems: connected to governed data, embedded in real workflows, measured continuously and overseen by people who understand both the technology and the business it serves.

iQsolutions designs and builds those systems. Our AI practice spans strategy, custom and open-weight language models, retrieval-augmented knowledge systems, agentic and multi-agent architectures, document intelligence, conversational interfaces, predictive analytics and computer vision, with governance and reliability engineering running through all of it.

We are deliberately model-agnostic. We work across commercial and open-weight models, select them through evaluation rather than preference, and design architectures that let you change providers as the field moves. AI outputs are probabilistic, so evaluation, monitoring and human oversight are part of every design, not an afterthought.

AI outputs are probabilistic. The systems we build include evaluation, monitoring and human review proportionate to the decisions they support, and AI-generated analysis is never presented as a substitute for qualified professional judgment.

Services in this discipline

AI Strategy and Consulting

Identify where AI earns its place in your operations, assess data and risk readiness, and build a sequenced roadmap your engineering and leadership teams can execute.

Custom LLM Solutions

Domain-adapted language model systems built on open-weight and commercial models, with fine-tuning, evaluation and private deployment where your data and requirements demand it.

Generative AI Applications

Production applications that use large language and multimodal models to draft, summarise, transform and explain content inside the tools your teams already use.

Agentic AI Engineering

AI agents that plan, call tools and act within defined permissions, engineered with durable execution, observability and human approval at the points where it matters.

Multi-Agent Systems

Coordinated teams of specialised AI agents with defined roles, shared state and supervisory control, for work too broad or varied for a single agent to handle reliably.

AI Workflow Automation

Business processes that combine deterministic automation with AI judgment steps, so unstructured inputs, exceptions and routing decisions no longer stall the work.

AI Model Integration

Connect foundation models and machine learning services to your applications, data and identity systems through secure, observable and provider-independent integration layers.

Enterprise Knowledge Systems and RAG

Retrieval-augmented generation over your documents, data and policies, with permission-aware search, cited answers and measurable retrieval quality.

Intelligent Document Processing

Extract, classify and validate information from invoices, forms, contracts and correspondence, combining OCR, layout models and LLMs with human verification where it matters.

Conversational AI

Chat and voice assistants for customers and employees that understand intent, take action through your systems and hand over to people with full context.

Predictive Analytics

Machine learning models that forecast demand, risk, churn and failure, built on governed data pipelines, deployed where decisions are made and monitored for drift.

Computer Vision

Vision systems that detect, inspect, count and read from images and video, deployed in the cloud or at the edge, with careful attention to accuracy, privacy and site conditions.

AI Governance and Reliability

The policies, evaluation practices, controls and monitoring that let AI systems operate responsibly, aligned with the NIST AI RMF, ISO/IEC 42001 and emerging regulation.

What sets this discipline apart

01
Engineered for production
We build for the conditions AI meets after launch: real users, inconsistent data, load, failure and change. Durable workflows, observability, security review and CI/CD are part of the first release, not deferred to a later phase.
02
Evaluation before claims
Every system is measured against task-specific test sets developed with your domain experts. We report what evaluation shows, including limitations, and use it to decide whether a prompt, model or architecture change is actually an improvement.
03
Grounded in your enterprise
Models become useful when they are connected to your systems of record, documents and permissions. We engineer the retrieval, integration and identity layers that give AI real business context while respecting who is allowed to see what.
04
Governed by design
Human oversight, audit trails, data protection and risk classification are built into the architecture and aligned with the NIST AI RMF and ISO/IEC 42001, so accountability is supported by the system rather than documented after the fact.

How engagements run

AI Discovery Sprint

A focused engagement to test a specific opportunity. We frame the decision, assess data and integration readiness, prototype against real material and evaluate the results, giving you evidence to decide whether, and how, to invest further.

Dedicated AI Product Team

A cross-functional team of AI engineers, data engineers, software engineers and product leads that builds and evolves an AI capability alongside your staff, working within your governance, tooling and release processes.

AI Platform Modernisation Program

A structured program to establish shared AI infrastructure across the organisation, including a model gateway, retrieval services, evaluation pipelines, observability and governance controls, so individual teams can build AI features on a consistent, secured foundation.

Discuss your AI initiative

Share your context and an iQsolutions engineer will respond with considered next steps — no obligation, no automated sales sequence.

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