Generative AI Solutions

Build Enterprise Generative AI That Can Be Grounded, Controlled, Evaluated and Operated

DataConsultant helps organisations turn high-value generative AI opportunities into production-aware solutions that connect approved enterprise data and knowledge with model access, retrieval, orchestration, guardrails, applications, evaluation and monitoring. The focus is not a “magic answer” demo; it is a usable business capability with defined owners, evidence, controls and operating responsibilities.

Evaluate Your Enterprise GenAI Opportunity → Review the Reference Architecture

Enterprise data and knowledge grounding

Risk and control aware design

Evaluation before release decisions

Operational monitoring and improvement

Illustrative Enterprise GenAI FlowProduction-aware

User taskQuestion, draft, summarisation, analysis or workflow request

Enterprise contextIdentity, permissions, approved knowledge, structured data and business rules

Use-case policyAllowed scope, prohibited data, review thresholds and escalation rules

Retrieval & orchestrationContext assembly, search, tools, prompts and workflow logic

Foundation modelSelected model endpoint with controlled inputs and output handling

Evaluation & monitoringQuality, factuality, safety, latency, cost and operational signals

Release decision: output must fit the business risk

✓Supported and suitable: deliver to the user or workflow with the required evidence.

!Uncertain or incomplete: clarify, retrieve again, route to review or limit the action.

×Outside policy or authority: refuse, block the action or escalate to an accountable owner.

2. Business Problem

Why Generative AI Pilots Often Fail to Become Dependable Enterprise Capabilities

The model is only one component. Production adoption depends on data, context, workflow fit, evaluation, permissions, governance, operating ownership and a clear way to handle uncertainty.

Use cases chosen by novelty

Experiments start without a clear user, decision, workflow, value hypothesis or acceptance threshold.

Knowledge is fragmented

Content is duplicated, stale, poorly classified, inaccessible or missing ownership and refresh processes.

Retrieval is untested

Vector search is added without evidence that the right sources are found, filtered and ranked for the task.

Controls sit outside the design

Privacy, confidentiality, prompt injection, output handling and tool permissions are reviewed too late.

No evaluation baseline

Teams cannot compare models or releases because representative test sets and scoring criteria were never defined.

Integration is underestimated

The demo is disconnected from identity, source systems, case tools, APIs, approvals and operational workflows.

Human oversight is vague

Review is promised without defining who reviews, what triggers review, what evidence they see or what they can change.

Production behaviour is invisible

Quality, source changes, failures, latency, cost and user feedback are not connected to an improvement process.

Permissions are prompt-based

Sensitive data and tool access depend on instructions instead of enforceable identity, entitlement and system controls.

Cost has no operating model

Token use, retrieval, model routing, evaluation and support costs are not measured against the value of the workflow.

3. Current State → Target State

Move From Isolated GenAI Experiments to a Governed Production Capability

A target state connects business value to data, architecture, control and ongoing operations instead of treating deployment as the finish line.

Current State

Multiple uncoordinated proofs of concept
Prompts and source choices depend on individuals
Limited evidence of factuality or groundedness
Permissions and sensitive-data handling are unclear
Model choice is disconnected from use-case risk
No defined release, incident or regression process

Target State

Qualified portfolio tied to business workflows
Governed enterprise knowledge and context
Model and retrieval choices tested against requirements
Guardrails, identity and tool permissions enforced
Release criteria supported by repeatable evaluation
Monitoring, ownership and improvement embedded in operations

Start With the Business Decision, Not the Model Catalogue.

Qualify the user, task, required evidence, error tolerance, data boundary and workflow action before choosing the architecture.

Evaluate Your GenAI Opportunity →

4. Solution Mechanism

How an Enterprise Generative AI Solution Turns a User Task Into a Controlled Outcome

The core flow is use case → enterprise data and knowledge → retrieval or context → foundation model → orchestration → guardrails → application → evaluation → monitoring.

Use Case

Define the user, task, business decision, workflow and acceptable error boundary.

Enterprise Knowledge

Select approved documents, structured data, metadata, business rules and user context.

Retrieval / Context

Search, filter, rank, assemble and refresh the context needed for the task when applicable.

Foundation Model

Route to a suitable model endpoint based on capability, risk, latency, privacy and cost constraints.

Orchestration

Apply prompts, tools, workflow logic, structured inputs, state and action sequencing only as needed.

Guardrails

Enforce policy, identity, validation, refusals, output constraints, approvals and escalation rules.

Application

Deliver the capability through the approved interface, API, case process or business workflow.

Evaluate & Monitor

Measure quality and operational signals, investigate failures and feed controlled improvements back into releases.

5. Use-Case Qualification

Map Business Priority to the Required GenAI Evidence and Control Level

Not every use case needs the same model, retrieval method or approval path. This illustrative matrix shows how business risk changes architecture and evaluation requirements.

Business Use Case	Primary GenAI Task	Typical Enterprise Context	Risk Level	Evaluation Focus	Decision / Action Boundary
Internal knowledge assistant	Question answering and summarisation	Policies, procedures, product and operating knowledge	Medium	Retrieval relevance, source faithfulness, citation quality, refusal behaviour	Assist User validates material decisions
Customer-service copilot	Draft response and next-best guidance	Approved knowledge, case context, customer entitlement	High	Factuality, policy alignment, sensitive-data handling, escalation	Review Human approves material responses
Document drafting	Generate first drafts from structured instructions	Templates, clauses, style rules, approved facts	Medium	Completeness, unsupported claims, version and source control	Draft Accountable owner signs off
Analyst research assistant	Summarise, compare and organise evidence	Reports, datasets, approved external sources, metadata	Medium	Source coverage, traceability, uncertainty and evidence gaps	Support Analyst owns conclusion
Tool-connected workflow	Plan and execute bounded actions	APIs, workflow state, permissions, business rules	High	Tool selection, action validation, permission boundaries, recovery	Control Confirm or restrict sensitive actions

6. Technical Reference Architecture

Production-Aware Generative AI Architecture

The reference architecture separates user experience, context, model access and business actions so identity, retrieval, evaluation, monitoring and policy can be applied at the right layer.

Applications & Channels

Copilots, portals, service desks, embedded workflows, APIs and role-specific experiences.

Orchestration & Guardrails

Prompts, workflow state, policy checks, tool routing, output validation and approval logic.

Model Gateway & Models

Approved endpoints, routing, version controls, usage limits and provider boundaries.

Knowledge, Retrieval & Tools

Search, vector retrieval, structured data, APIs, enterprise tools and source refresh processes.

Identity & AccessUser, service, source and tool entitlements

EvaluationTest sets, rubrics, regression and human review

ObservabilityQuality, latency, cost, failures and feedback

GovernanceInventory, ownership, risk, approvals and change control

Design the Context, Controls and Evaluation Path Before Production Release.

A production architecture should make source boundaries, model access, tool permissions, human review and monitoring explicit.

Discuss Your GenAI Architecture →

7. Data & Knowledge Requirements

What Has to Be Ready for Generative AI to Use Enterprise Context Reliably

Generative AI quality is constrained by the information it receives. Source governance, metadata, permissions and refresh behaviour are part of the solution.

Approved knowledge sources

Policies, manuals, product information, cases, contracts, procedures or other authorised content.

Named source owners
Version and freshness expectations
Inclusion and exclusion rules

Structured enterprise data

Customer, product, transaction, operational, reference or analytical data where the use case requires it.

Defined fields and semantics
Quality and timeliness checks
Purpose and access boundaries

Retrieval metadata

Chunk, document, domain, sensitivity, ownership, dates and entitlement attributes that improve selection.

Filtering and ranking metadata
Source traceability
Refresh and deletion propagation

User and workflow context

Role, permissions, task state, case context and business rules needed to interpret or constrain a request.

Authenticated identity
Least-privilege context
Action and escalation limits

8. Governance, Risk & Control Model

Build Controls Around the Generative AI System, Not Only Around the Prompt

The control model should reflect the use case, data sensitivity, model and supplier choices, user population, degree of automation and consequence of an incorrect or unauthorised action.

Use-case ownership

Named business owner, technical owner, data or knowledge owner and accountable release authority.

Scope • accountability • acceptance

Data & privacy

Purpose boundaries, minimisation, sensitive-data handling, source permissions, retention and deletion requirements.

Input • retrieval • logs • outputs

Prompt & retrieval security

Separate trusted instructions from untrusted content, filter sources, test injection paths and limit downstream impact.

Prompt injection • source trust • isolation

Model & supplier governance

Approved models, provider terms, data handling, version changes, dependencies and fallback expectations.

Selection • change • third parties

Tool permissions

Allow only necessary actions, validate parameters, require confirmation for sensitive steps and log material operations.

Least privilege • validation • approval

Evaluation & release

Representative test sets, documented acceptance criteria, regression evidence and exception handling.

Quality • safety • groundedness

Human oversight

Define when a person must review, what evidence they receive, their authority and the escalation path.

Review • override • escalation

Monitoring & incident response

Observe failures, quality changes, source issues, latency, cost and incidents with clear investigation ownership.

Observe • investigate • improve

NIST AI RMF: Generative AI ProfileNIST AI 600-1 provides a cross-sector companion profile for managing generative AI risks. Review NIST resource ↗

ISO/IEC 42001:2023An international AI management-system standard that can inform governance processes where relevant to the organisation. Review ISO overview ↗

OWASP GenAI / LLM Security GuidanceA security-awareness resource for common LLM and generative AI application risks; apply it alongside enterprise security engineering. Review OWASP project ↗

9. Evaluation & LLMOps

Measure the Behaviour That Matters to the Use Case Before and After Release

Evaluation should connect model, retrieval and application behaviour to a release decision. Production monitoring then checks whether sources, prompts, models, integrations and user behaviour change the result over time.

Evaluation Evidence Required Before Release

Task relevanceRepresentative tasks, expected behaviour and failure examples.Acceptance criteria

Source groundednessTraceable evidence linking material responses to approved context.Grounding review

Retrieval qualityQueries, source coverage, ranking behaviour and known retrieval gaps.Test evidence

Policy alignmentRisk-based scenarios for refusals, restricted content and sensitive workflows.Control review

Tool-action correctnessPermission, parameter, confirmation and failure-path tests for enabled actions.Action assurance

Human acceptanceUser review of workflow fit, usefulness, uncertainty and escalation behaviour.Release decision

ObserveCollect approved quality, error, latency, cost, usage and operational signals.

InvestigateSeparate source, retrieval, prompt, model, tool, UI and process causes.

ImproveChange only the component supported by evidence and document the reason.

Regression testRe-run representative tests before promoting a model, prompt, retrieval or workflow change.

Release & recordApprove, reject or limit the change and retain the evidence required by the operating model.

Make Evaluation and Monitoring Part of the Product, Not a Pre-Launch Checklist.

Define what must be measured, who investigates failures and how controlled changes move back into production.

Plan Your GenAI Operating Model →

10. Operating Model

Clarify Who Owns the Use Case, Knowledge, Model, Controls and Release Decision

Generative AI crosses business, data, technology and risk boundaries. Production ownership should be explicit before the solution is allowed to influence material decisions or actions.

Business / Product OwnerDefines user value, task boundaries, operating process, acceptance criteria and business accountability.

AI / Engineering TeamBuilds application, orchestration, prompts, model integrations, APIs, testing and deployment automation.

Data / Knowledge OwnerOwns approved sources, metadata, quality, freshness, lifecycle, access and content-change responsibilities.

Platform / Cloud OwnerManages environments, model services, identity integration, network boundaries, cost controls and resilience.

Risk / Privacy / SecurityDefines proportionate review, control requirements, escalation, evidence expectations and specialist approvals.

Evaluation / AssuranceMaintains test strategy, representative cases, rubrics, regression evidence and independent challenge where needed.

Operations / SupportOwns incidents, service monitoring, source refresh, issue triage, user feedback and controlled improvements.

Release AuthorityApproves production use, limitations, thresholds and material changes based on documented evidence.

DataConsultantCan support assessment, design, implementation, evaluation, documentation, handover and managed workstreams according to scope.

11. Transformation Roadmap

Move From Opportunity to an Operable Generative AI Capability

The sequence should expose high-risk assumptions early, then build only the architecture and controls justified by the use case. Delivery duration is confirmed during scoping.

1. Qualify

Business case & riskUser, task, decision, value, error tolerance, review needs and initial feasibility.

2. Ground

Data & knowledgeSource inventory, permissions, metadata, quality, freshness and retrieval feasibility.

3. Design

Architecture & controlsModel access, retrieval, orchestration, guardrails, integration, identity and monitoring design.

4. Build

Working capabilityApplication, prompts, retrieval, tool connectors, policy checks and observability instrumentation.

5. Evaluate

Evidence & remediationRepresentative testing, failure analysis, human review, regression and release criteria.

6. Release

Controlled productionApprovals, access, support, documentation, user guidance, limits and operational handover.

7. Operate

Monitor & improveProduction signals, incidents, source changes, vendor changes, cost, adoption and controlled updates.

The roadmap is tailored to the organisation’s environment, data readiness, platform choices, integration needs, assurance depth, change approvals and rollout scope.

12. Delivery Methodology

A Focused, Evidence-Led Path From Use-Case Definition to Operational Handover

DataConsultant can deliver a focused assessment, solution design, implementation, evaluation workstream or a broader end-to-end engagement depending on what the organisation already has in place.

1. UnderstandGoals, users, workflow, evidence, constraints and decision consequences.

2. QualifyValue, feasibility, error tolerance, data boundaries and suitable AI pattern.

3. DesignContext, model, retrieval, orchestration, guardrails, APIs and controls.

4. BuildImplement the minimum coherent capability and required integrations.

5. EvaluateTest representative scenarios, failures, security paths and acceptance criteria.

6. RemediatePrioritise evidence-backed fixes and rerun regression tests.

7. ReleaseDocument limits, approve controls, prepare users and transition support.

8. OperateMonitor quality, incidents, sources, costs, feedback and controlled change.

13. Deliverables & Expected Outcomes

Practical Outputs That Support Architecture, Release and Ongoing Ownership

Final deliverables depend on the agreed scope. Expected outcomes are qualitative because realised value depends on the client environment, adoption, data, controls and operational execution.

Typical Deliverables

Use-case qualification and scope
Current-state readiness findings
Target solution blueprint
Generative AI reference architecture
Data and knowledge requirements
Retrieval and context design
Model and orchestration decision record
Guardrail and control requirements
Evaluation strategy and test set
Risk and issue register
Integration specification
Release and operating playbook
Monitoring and LLMOps plan
Knowledge-transfer materials

Expected Qualitative Outcomes

Clearer use-case investment decisions
Stronger connection between enterprise knowledge and AI responses
More explicit ownership and release accountability
Better evidence for model and retrieval choices
More consistent evaluation and regression practices
Improved visibility of failure modes and limitations
Better-defined security and tool permission boundaries
More practical human-review and escalation paths
Clearer production monitoring responsibilities
More maintainable source and prompt change processes
Better alignment between AI capability and business workflow
More informed cost and operating decisions
Stronger documentation for handover
More repeatable improvement procedures

14. Commercial Model

Custom Scope & Pricing for Generative AI Solutions

DataConsultant does not publish a fixed price for this solution. A proposal is scoped around the business outcome, technical environment, risk profile and level of implementation or ongoing support required.

What Shapes the Consulting and Implementation Scope

The most relevant variables are the ones that change the work, evidence, architecture, control effort or operating responsibilities.

Use-case portfolioNumber of use cases, user groups, workflows and decision consequences.

Knowledge & retrievalSource count, quality, permissions, metadata, refresh and RAG complexity.

Models & orchestrationProvider landscape, routing, prompting, tools, agents and workflow logic.

Enterprise integrationsIdentity, APIs, data platforms, search, case systems and application interfaces.

Governance & assurancePrivacy, security, control design, evaluation depth, human review and approvals.

Operations & adoptionMonitoring, support, documentation, training, rollout scope and managed services.

Third-party cost boundary: cloud, model API, software, search, vector database, observability and other vendor charges are separate from DataConsultant consulting or implementation fees unless an agreed proposal explicitly includes them. Volatile vendor pricing should be confirmed from the relevant provider.

Build a GenAI Roadmap Around Your Real Data, Controls and Integrations.

Share the use cases, knowledge sources, platforms, user groups and review requirements so the scope reflects the actual production challenge.

Request a Generative AI Quote →

16. Frequently Asked Questions

Generative AI Solutions FAQs

Answers to common enterprise questions about use cases, RAG, data, models, evaluation, controls, implementation, operations and commercial scope.

What are Generative AI Solutions for an enterprise?

Enterprise generative AI solutions combine a defined business use case with approved data or knowledge, model access, prompting or orchestration, application logic, guardrails, evaluation, monitoring and accountable human oversight. The design should fit the decision or workflow rather than treating a model response as the complete solution.

Which generative AI use cases are suitable for production?

Suitability depends on business value, task repeatability, data readiness, user population, error tolerance, security and privacy constraints, integration complexity, review needs and whether the required outcome can be evaluated. A use case with unclear ownership or unacceptable error consequences may need redesign, stronger controls or a non-generative approach.

Does every generative AI solution need retrieval augmented generation?

No. RAG is appropriate when the application needs relevant enterprise knowledge or current approved sources at response time. Some use cases can rely on model capabilities, structured tools, workflow logic or other data access patterns. Retrieval should be selected because it improves the required task, not because it is fashionable.

Can DataConsultant work with our existing AI platform and cloud environment?

Yes. The solution can be designed around the organisation’s approved cloud, model providers, data platforms, identity services, search or vector systems, application stack, observability tooling and governance controls. Architecture choices remain requirements-led and should reflect existing investments, security boundaries, skills and operating constraints.

How do you reduce hallucination and unsupported answers?

Controls can include use-case boundaries, source governance, retrieval and grounding, prompt design, citations where useful, structured output constraints, refusal or clarification rules, representative evaluation, human review for material decisions and production monitoring. These measures can reduce risk but do not guarantee that every generated output will be correct.

How are generative AI solutions evaluated before release?

Evaluation can use representative test sets, expected behaviours, quality rubrics, retrieval checks, factuality and groundedness review, safety tests, policy scenarios, tool-use tests, latency and cost observations, human review and regression testing. Release criteria should be tied to the actual business risk and user workflow.

What data and knowledge do we need to provide?

Inputs vary by use case. They may include approved documents, policies, product or service information, structured enterprise data, knowledge-base content, workflow definitions, metadata, access rules, user roles, historical examples, evaluation cases and relevant policies. Sensitive or regulated data should be assessed before it enters an AI workflow.

How do access controls work with enterprise generative AI?

Identity and entitlement should be enforced outside the language model wherever possible. Retrieval, tools and downstream actions should respect the authenticated user’s permissions, with least-privilege access, logging and approval steps for sensitive operations. The prompt itself should not be treated as an access-control mechanism.

Do you support AI agents and tool-connected workflows?

Agentic or tool-connected patterns can be considered when the use case genuinely requires multi-step reasoning or controlled actions. Tool permissions, action boundaries, confirmation steps, failure handling, auditability and human escalation become more important as autonomy increases. A simpler workflow is preferable when it can achieve the same result with lower operational risk.

How do you address privacy, security and responsible AI?

The design can incorporate data classification, minimisation, approved-use boundaries, identity and access controls, supplier review, prompt and retrieval security, output handling, audit logging, human oversight, evaluation and incident procedures. Applicable legal and regulatory obligations must be confirmed for the organisation’s jurisdiction, sector and use case by authorised specialists.

How long does a generative AI implementation take?

A reliable timeline is confirmed during scoping. It depends on use-case complexity, data and knowledge readiness, integrations, model and platform choices, security reviews, evaluation requirements, user experience, governance approvals, rollout scope and the maturity of the target operating environment.

How is Generative AI Solutions pricing determined?

DataConsultant does not publish a fixed price for this solution. Pricing is scope-led and can depend on the number and complexity of use cases, data sources, retrieval requirements, integrations, model providers, security and governance work, evaluation depth, user population, deployment environments, documentation, training and managed support. Third-party platform and model consumption costs are separate unless explicitly included in an agreed proposal.

Can DataConsultant support the solution after go-live?

Ongoing support can be scoped for evaluation, monitoring, source updates, prompt and retrieval improvements, incident review, control evidence, model or vendor changes, regression testing, adoption support and knowledge transfer. The operating model should identify which responsibilities stay with the client, DataConsultant and any platform providers.

Build Generative AI Your Organisation Can Evaluate, Govern and Operate.

Share the business workflow, enterprise context, risk boundaries and target environment. DataConsultant can help define a production-aware next step.

Discuss Your Generative AI Requirement →

Generative AI Solutions Enquiry

Request a Generative AI Scope Review

Share your contact details and requirement. DataConsultant can review likely solution patterns, evidence needs, architecture dependencies, control considerations and an appropriate engagement scope.