AI Solution Technologies

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Generative AI & Copilot

A secure, permission-aware assistant that answers staff questions and completes tasks against the organisation's own approved information.

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Milestone based, or time and materials for evolving scopeDELIVERY MODEL

The problems this practice exists to solve

Staff cannot find the organisation's own information when they need it

Generic AI tools that do not know anything about the business

Inconsistent answers to the same question across teams

No way to verify where an AI-generated answer came from

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HOW THIS PRACTICE IS ORGANISED

5 specialist areas

Pick the one that matches your problem, or search the full list below.

Enterprise AI AssistantsSecure AI architecture within the client's own environment Microsoft CopilotReadiness assessment covering tenancy, permissions, data and licensing GenAI EngineeringRetrieval and context architecture design Grounding, Safety & EvaluationGuardrail architecture and configuration Machine Learning & MLOpsFeature engineering and model development

Every service in this practice

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Every service, grouped by sub-practice

Enterprise AI Assistants Explore Enterprise AI Assistants ›

Enterprise AI AssistantA secure, permission-aware assistant that answers staff questions and completes tasks against the organisation's own approved information.Enterprise RAG ImplementationRetrieval-augmented generation connecting an AI system securely to internal documents, databases, policies, contracts and knowledge bases so answers are grounded in the organisation's own trusted content.AI on ERP & FinanceGenerative AI applied to enterprise resource planning and finance systems for enquiry, explanation, exception handling and reporting.AI Financial ReconciliationMachine-assisted matching, variance explanation and exception routing across ledgers and subsidiary systems.AI Anomaly DetectionDetection of unusual patterns in transactions, operations or system behaviour, with explanation and routing for human review.AI Software DevelopmentDevelopment of custom applications where generative AI is a core part of the product rather than an add-on.

Enterprise AI AssistantA secure, permission-aware assistant that answers staff questions and completes tasks against the organisation's own approved information.Enterprise RAG ImplementationRetrieval-augmented generation connecting an AI system securely to internal documents, databases, policies, contracts and knowledge bases so answers are grounded in the organisation's own trusted content.AI on ERP & FinanceGenerative AI applied to enterprise resource planning and finance systems for enquiry, explanation, exception handling and reporting.AI Financial ReconciliationMachine-assisted matching, variance explanation and exception routing across ledgers and subsidiary systems.AI Anomaly DetectionDetection of unusual patterns in transactions, operations or system behaviour, with explanation and routing for human review.AI Software DevelopmentDevelopment of custom applications where generative AI is a core part of the product rather than an add-on.

Microsoft Copilot Explore Microsoft Copilot ›

Azure OpenAI IntegrationSecure integration of Azure OpenAI models into enterprise applications, data platforms and workflows within the client's tenancy.Microsoft 365 Copilot Readiness AssessmentAssessment of tenancy configuration, data hygiene, permissions, licensing and change readiness before Copilot is switched on.Copilot Data-Hygiene RemediationIdentification and remediation of the over-shared, duplicated and stale content that causes Copilot to surface information users were never intended to see.SharePoint & OneDrive Permission RemediationSystematic review and correction of site, library and item-level permissions across a SharePoint and OneDrive estate.Sensitivity Labelling for CopilotDesign and rollout of Microsoft Purview sensitivity labels and protection policies so Copilot respects information classification.Copilot Licence Optimisation & Seat PlanningAnalysis of usage and role fit to place Copilot licences where they generate measurable return rather than across the whole organisation.Copilot Studio Agent DevelopmentBuild of custom agents in Copilot Studio connected to enterprise data, workflows and approval processes.Microsoft Graph ConnectorsDevelopment and configuration of connectors that bring third-party and line-of-business content into the Microsoft Graph for Copilot grounding.Declarative Agents & Copilot ExtensibilityExtension of Microsoft 365 Copilot with declarative agents, actions and plugins tailored to specific business roles.Copilot Governance & GuardrailsPolicy, configuration and monitoring that define what Copilot may access, what it may do, and how usage is reviewed.Copilot Adoption & Change ManagementStructured adoption programme covering champions, role-based use cases, training and measured behaviour change.Copilot Usage Analytics & ROI MeasurementMeasurement of Copilot adoption, usage depth and time saved, reported against the business case that funded it.Role-Specific Copilot EnablementTailored enablement for sales, finance, legal, HR and service teams built around the actual work each role performs.GitHub Copilot EnablementRollout of GitHub Copilot to engineering teams with standards, security review, measurement and coaching.Microsoft Security Copilot ImplementationImplementation of Security Copilot into security operations, with playbooks, data connections and analyst enablement.Dynamics 365 Copilot EnablementActivation and configuration of Copilot capability inside Dynamics 365 sales, service and finance modules.Power Platform Copilot & Low-Code AgentsEnablement of business-built agents and low-code applications under a governed Power Platform environment strategy.

Azure OpenAI IntegrationSecure integration of Azure OpenAI models into enterprise applications, data platforms and workflows within the client's tenancy.Microsoft 365 Copilot Readiness AssessmentAssessment of tenancy configuration, data hygiene, permissions, licensing and change readiness before Copilot is switched on.Copilot Data-Hygiene RemediationIdentification and remediation of the over-shared, duplicated and stale content that causes Copilot to surface information users were never intended to see.SharePoint & OneDrive Permission RemediationSystematic review and correction of site, library and item-level permissions across a SharePoint and OneDrive estate.Sensitivity Labelling for CopilotDesign and rollout of Microsoft Purview sensitivity labels and protection policies so Copilot respects information classification.Copilot Licence Optimisation & Seat PlanningAnalysis of usage and role fit to place Copilot licences where they generate measurable return rather than across the whole organisation.Copilot Studio Agent DevelopmentBuild of custom agents in Copilot Studio connected to enterprise data, workflows and approval processes.Microsoft Graph ConnectorsDevelopment and configuration of connectors that bring third-party and line-of-business content into the Microsoft Graph for Copilot grounding.Declarative Agents & Copilot ExtensibilityExtension of Microsoft 365 Copilot with declarative agents, actions and plugins tailored to specific business roles.Copilot Governance & GuardrailsPolicy, configuration and monitoring that define what Copilot may access, what it may do, and how usage is reviewed.Copilot Adoption & Change ManagementStructured adoption programme covering champions, role-based use cases, training and measured behaviour change.Copilot Usage Analytics & ROI MeasurementMeasurement of Copilot adoption, usage depth and time saved, reported against the business case that funded it.Role-Specific Copilot EnablementTailored enablement for sales, finance, legal, HR and service teams built around the actual work each role performs.GitHub Copilot EnablementRollout of GitHub Copilot to engineering teams with standards, security review, measurement and coaching.Microsoft Security Copilot ImplementationImplementation of Security Copilot into security operations, with playbooks, data connections and analyst enablement.Dynamics 365 Copilot EnablementActivation and configuration of Copilot capability inside Dynamics 365 sales, service and finance modules.Power Platform Copilot & Low-Code AgentsEnablement of business-built agents and low-code applications under a governed Power Platform environment strategy.

GenAI Engineering Explore GenAI Engineering ›

Prompt Engineering & Prompt Library ManagementDevelopment, testing and central management of production prompts as versioned, reviewable assets rather than ad-hoc text.Prompt Versioning & RegistryA controlled registry recording every production prompt, its version, its owner, its evaluation results and its release history.Context Engineering & Window OptimisationDesign of what information is placed in front of a model, in what order and at what cost, to maximise answer quality.Retrieval Architecture DesignDesign of chunking, indexing, hybrid search, filtering and re-ranking so retrieval returns the right evidence for the question asked.Vector Database ImplementationSelection, deployment and tuning of vector search infrastructure such as Azure AI Search or pgvector within the client's environment.Embedding Model SelectionEvaluation and selection of embedding models against the client's content, languages and retrieval accuracy requirements.Knowledge Graph Construction for GroundingConstruction of an entity and relationship graph over enterprise content so generative AI can reason across connected facts.GraphRAG ImplementationRetrieval-augmented generation over a knowledge graph, enabling multi-hop questions that vector search alone cannot answer.Multimodal AIImplementation of AI over documents, images, audio and video in addition to text.Document Intelligence & Intelligent Document ProcessingExtraction and validation of structured data from complex documents including forms, contracts, drawings and scanned records.Conversational & Voice AIDesign and delivery of voice and conversational interfaces integrated with enterprise systems and human handover.Arabic-English Bilingual AI Evaluation & TuningEvaluation, tuning and quality assurance of AI systems operating in Arabic and English, covering accuracy, tone, dialect and cultural appropriateness.Model Selection & BenchmarkingStructured comparison of candidate models against the client's tasks, data, latency, cost and sovereignty requirements.Small Language Model & Edge DeploymentDeployment of smaller models on constrained, disconnected or privacy-sensitive infrastructure.Model Fine-Tuning, LoRA & DistillationAdaptation of models to a client's domain, terminology and task set where prompting alone is insufficient.Model Routing & FallbackAutomatic routing of requests between models by task complexity, cost and availability, with graceful degradation.LLM Gateway & API ManagementA central, governed gateway for all model access providing authentication, quota, logging, cost attribution and policy enforcement.Semantic CachingCaching of semantically equivalent requests to reduce inference cost and latency without degrading answer quality.Token Cost Optimisation & GenAI FinOpsMeasurement and reduction of generative AI running cost by workload, team and use case, with chargeback and forecasting.A/B Testing & Experimentation for GenAIControlled experimentation infrastructure that measures whether a prompt, model or retrieval change actually improves outcomes.

Prompt Engineering & Prompt Library ManagementDevelopment, testing and central management of production prompts as versioned, reviewable assets rather than ad-hoc text.Prompt Versioning & RegistryA controlled registry recording every production prompt, its version, its owner, its evaluation results and its release history.Context Engineering & Window OptimisationDesign of what information is placed in front of a model, in what order and at what cost, to maximise answer quality.Retrieval Architecture DesignDesign of chunking, indexing, hybrid search, filtering and re-ranking so retrieval returns the right evidence for the question asked.Vector Database ImplementationSelection, deployment and tuning of vector search infrastructure such as Azure AI Search or pgvector within the client's environment.Embedding Model SelectionEvaluation and selection of embedding models against the client's content, languages and retrieval accuracy requirements.Knowledge Graph Construction for GroundingConstruction of an entity and relationship graph over enterprise content so generative AI can reason across connected facts.GraphRAG ImplementationRetrieval-augmented generation over a knowledge graph, enabling multi-hop questions that vector search alone cannot answer.Multimodal AIImplementation of AI over documents, images, audio and video in addition to text.Document Intelligence & Intelligent Document ProcessingExtraction and validation of structured data from complex documents including forms, contracts, drawings and scanned records.Conversational & Voice AIDesign and delivery of voice and conversational interfaces integrated with enterprise systems and human handover.Arabic-English Bilingual AI Evaluation & TuningEvaluation, tuning and quality assurance of AI systems operating in Arabic and English, covering accuracy, tone, dialect and cultural appropriateness.Model Selection & BenchmarkingStructured comparison of candidate models against the client's tasks, data, latency, cost and sovereignty requirements.Small Language Model & Edge DeploymentDeployment of smaller models on constrained, disconnected or privacy-sensitive infrastructure.Model Fine-Tuning, LoRA & DistillationAdaptation of models to a client's domain, terminology and task set where prompting alone is insufficient.Model Routing & FallbackAutomatic routing of requests between models by task complexity, cost and availability, with graceful degradation.LLM Gateway & API ManagementA central, governed gateway for all model access providing authentication, quota, logging, cost attribution and policy enforcement.Semantic CachingCaching of semantically equivalent requests to reduce inference cost and latency without degrading answer quality.Token Cost Optimisation & GenAI FinOpsMeasurement and reduction of generative AI running cost by workload, team and use case, with chargeback and forecasting.A/B Testing & Experimentation for GenAIControlled experimentation infrastructure that measures whether a prompt, model or retrieval change actually improves outcomes.

Grounding, Safety & Evaluation Explore Grounding, Safety & Evaluation ›

Guardrails ImplementationInput and output controls covering content filtering, topic restriction, jailbreak resistance and unsafe-request handling.Hallucination Detection & Grounding VerificationAutomated checks that every generated claim is supported by retrieved evidence, with unsupported output blocked or flagged.Citation & Provenance EnforcementEnforcement of source citation on every answer so a user can verify where information came from.GenAI Evaluation FrameworkA standing evaluation capability using golden datasets, model-as-judge scoring and structured human review.Red-Team Prompt TestingAdversarial testing of a deployed generative AI system to find prompts that produce unsafe, non-compliant or incorrect output.Sovereign & In-Country AI DeploymentArchitecture and deployment of AI workloads that keep data and inference inside a specified jurisdiction to meet residency obligations.Data Residency Architecture for AIDesign of storage, processing and logging topology so that AI systems demonstrably satisfy data residency requirements.AI Output Quality MonitoringContinuous production monitoring of answer quality, refusal rate, citation coverage and user feedback.

Guardrails ImplementationInput and output controls covering content filtering, topic restriction, jailbreak resistance and unsafe-request handling.Hallucination Detection & Grounding VerificationAutomated checks that every generated claim is supported by retrieved evidence, with unsupported output blocked or flagged.Citation & Provenance EnforcementEnforcement of source citation on every answer so a user can verify where information came from.GenAI Evaluation FrameworkA standing evaluation capability using golden datasets, model-as-judge scoring and structured human review.Red-Team Prompt TestingAdversarial testing of a deployed generative AI system to find prompts that produce unsafe, non-compliant or incorrect output.Sovereign & In-Country AI DeploymentArchitecture and deployment of AI workloads that keep data and inference inside a specified jurisdiction to meet residency obligations.Data Residency Architecture for AIDesign of storage, processing and logging topology so that AI systems demonstrably satisfy data residency requirements.AI Output Quality MonitoringContinuous production monitoring of answer quality, refusal rate, citation coverage and user feedback.

Machine Learning & MLOps Explore Machine Learning & MLOps ›

Machine Learning SolutionsDevelopment of predictive and classification models for forecasting, risk scoring, segmentation and optimisation.MLOps & AI InfrastructureThe pipelines, registries, environments and release processes that take models from notebook to monitored production.

What we deliver

Secure AI architecture within the client's own environment

Permission-aware connection to approved internal sources

Citation and provenance on every response

Prompt and response logging for review

Evaluation results against a golden question set

User enablement and adoption support

The outcomes that follow

Answers grounded in the organisation's own information

Faster access to policy, contract and project knowledge

Consistency across teams and locations

A verifiable audit trail for every response

A foundation that extends to further use cases

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How every engagement runs

01Discoverconfirm objectives, stakeholders, constraints and success measures

02Assessreview current systems, data, controls and delivery readiness

03Designprepare the target design, backlog, governance and implementation plan

04Validatetest the priority requirements through a prototype, pilot or controlled design review

05Implementconfigure, integrate, test and deploy the approved solution

06Enabletrain users, transfer knowledge and establish operating procedures

07Optimisemonitor adoption, performance, cost, quality and improvement opportunities

BUILT FORGovernment agencies and regulated public-sector bodiesBanks, insurers, superannuation funds and professional-services firmsConstruction, engineering and infrastructure contractorsLarge enterprises and mid-market organisations undergoing modernisation

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What our customers say

Real feedback from the organisations we build with — what changed, in their words.

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Finance ManagerGCC Construction Group, Dubai

Project DirectorTier-One Contractor, Sydney

Commercial DirectorProperty Developer, Dubai

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