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GenAI Engineering

Every GenAI Engineering service AI Solution Technologies delivers, within the Generative AI & Copilot practice.

Retrieval engineering including chunking, hybrid searchPrompt and context management as controlledModel benchmarking against real client tasksBilingual and multilingual evaluation capability

BUILT ON

The problems GenAI Engineering exists to solve

Answer quality that varies unpredictably between users and questions

Prompts managed as untracked text scattered across teams

Retrieval that returns plausible but irrelevant evidence

Running cost rising faster than measured benefit

Discuss your challenges

What is GenAI Engineering?

Every GenAI Engineering service AI Solution Technologies delivers, within the Generative AI & Copilot practice.

SENIOR-LED

Retrieval and context architecture design

Materially higher answer accuracy on the tasks that matter

PRODUCTION-READY

Versioned prompt library with evaluation results

Predictable, attributable running cost

KPI-DEFINED

Model selection analysis against the client's tasks and constraints

Changes released with evidence rather than by assumption

Get a free
discovery sprint

Before you commit, we help you confirm the use case, check data readiness, define the KPI, and work out which GenAI Engineering services actually fit the problem you have.

For a limited number of teams each quarter, we run the full sprint free of charge.

Claim a free sprintTrusted by data leaders
across industries

STAGE DELIVERY7

DiscoverUnderstand your challenges and opportunities
AssessEvaluate data readiness and current state
DefineDefine KPIs, scope and success metrics
Deliver valuePrototype, validate and deliver quick wins

No commitmentExplore the value, risk-free.
Focused outcomesWe define impact, KPIs and success.
Fast & collaborativeDelivered by experts, built for your team.

How GenAI Engineering runs

Confirm objectives, stakeholders, constraints and success measures

Review current systems, data, controls and delivery readiness

Prepare the target design, backlog, governance and implementation plan

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

Configure, integrate, test and deploy the approved solution

Train users, transfer knowledge and establish operating procedures

Monitor adoption, performance, cost, quality and improvement opportunities

KEY CAPABILITIES

What we bring to GenAI Engineering

01Retrieval engineering including chunking, hybrid search and re-ranking
02Prompt and context management as controlled assets
03Model benchmarking against real client tasks, not public leaderboards
04Bilingual and multilingual evaluation capability
05Cost engineering across model routing, caching and quotas

What you get, and what follows

A complete GenAI Engineering foundation that drives clarity, control and confidence across your organisation.

DELIVERABLESEverything we deliver to build a strong, governed GenAI Engineering capability.

Retrieval and context architecture design
Versioned prompt library with evaluation results
Model selection analysis against the client's tasks and constraints
Cost model with optimisation recommendations
Evaluation framework and baseline scores
Engineering documentation and knowledge transfer

BUSINESS OUTCOMESThe outcomes you achieve, and the impact that follows.

Materially higher answer accuracy on the tasks that matter
Predictable, attributable running cost
Changes released with evidence rather than by assumption
Reduced dependency on any single model provider
An engineering practice the client's own team can maintain

Book a scoping call

No obligationYou keep the written summary either way
Tailored to youFocused on your data, priorities and goals
Expert-ledDelivered by specialists with real-world experience

The services inside 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.

INVESTMENT & DELIVERY

How we engage on
GenAI Engineering

A proven delivery approach that is transparent, flexible and built to deliver lasting value.

DELIVERY MODELSFixed price or milestone basedFixed priceMilestone based, or time and materials for evolving scopeManaged-service retainer with agreed service levelsDelivered by a senior-led Australian team across 7 stages, from discovery through to optimisation.

WHO THIS IS FOR