01
Strategy and opportunity assessment
Define business outcomes, identify candidate workflows, assess feasibility and prioritise a practical portfolio based on value, risk, readiness and effort.
Artificial Intelligence Consulting Service
Generative AI Consulting for Governed, Practical Business Solutions
Dataconsultant helps business, data and technology leaders assess, design and implement generative AI solutions that are aligned with real workflows. The service combines use-case prioritisation, data readiness, architecture, prototyping, evaluation, governance and operational planning to support controlled adoption and measurable business outcomes.
- Business-led use-case prioritisation
- Model, data and architecture assessment
- Security, privacy and governance by design
- Evaluation, knowledge transfer and operational planning
Generative AI delivery model
From opportunity to controlled operation1
Use case and value
Decision, workflow, user and success criteria
PrioritiseDecision, workflow, user and success criteria
2
Knowledge and data
Sources, permissions, quality and retrieval
PrepareSources, permissions, quality and retrieval
3
Model and workflow
LLM, prompts, tools, guardrails and integration
DesignLLM, prompts, tools, guardrails and integration
4
Evaluation and controls
Groundedness, safety, cost and acceptance
ValidateGroundedness, safety, cost and acceptance
5
Operations and improvement
Monitoring, ownership, feedback and updates
OperateMonitoring, ownership, feedback and updates
ValueOutcome and adoption measures
TrustQuality, safety and oversight
ScaleArchitecture and operating model
Quick definition
What is generative AI consulting?
Generative AI consulting is structured advisory and delivery support for using foundation models and large language models in business processes. It connects business needs with data, model selection, retrieval, prompt and agent workflows, evaluation, governance, security, integration and operations.
When the service is commonly required
- Teams have many AI ideas but no defensible prioritisation method.
- A prototype works in a demo but is unreliable with real users or data.
- Leaders need architecture, risk and cost decisions before investment.
- Regulated or sensitive workflows require documented controls and human oversight.
- An organisation needs to move from isolated experiments to a repeatable operating model.
Service offering
Generative AI advisory and implementation support
The scope is adapted to the organisation’s use cases, data environment, risk profile and delivery maturity.
02
Solution architecture and vendor decisions
Evaluate model, cloud, data, retrieval, orchestration and integration options without assuming that one platform is suitable for every use case.
03
Prototype and pilot delivery
Design controlled experiments, build representative workflows, create evaluation sets and gather evidence before production commitments.
04
RAG and knowledge assistants
Design source ingestion, permissions, chunking, metadata, retrieval, citations, prompt orchestration and knowledge-maintenance processes.
05
Governance, security and assurance
Define accountability, approved use, model and vendor controls, testing expectations, data handling, oversight, monitoring and incident processes.
06
Production and operating-model support
Plan deployment, service ownership, monitoring, feedback, cost management, change control, training and continuous improvement.
Key value propositions
Make generative AI decisions with clearer evidence
Prioritised investmentFocus resources on use cases with a credible value and readiness case.
Reduced delivery riskIdentify data, integration, evaluation and control gaps early.
Better solution qualityTest against realistic tasks, failure modes and user expectations.
Sustainable operationsDefine ownership, monitoring, update and cost-management responsibilities.
Problems addressed
Common barriers to responsible generative AI adoption
Unclear business case
Ideas are selected because they are visible or fashionable rather than because they improve a decision, task or customer outcome.
Response: value framing, workflow analysis, prioritisation criteria and measurable success conditions.
Weak grounding and data readiness
Outputs are incomplete or inaccurate because source data, access controls, metadata and retrieval behaviour are not designed together.
Response: data and knowledge assessment, RAG design, permissions, quality checks and citation requirements.
Prototype-to-production gap
A demonstration does not account for scale, latency, user identity, integration, monitoring, support, resilience or operating cost.
Response: target architecture, non-functional requirements, deployment controls and operational transition planning.
Uncontrolled risk
Teams lack consistent rules for confidential data, harmful output, human review, vendor exposure, model changes and incident handling.
Response: use policies, risk classification, assurance gates, logging, oversight and accountable ownership.
Turn a generative AI idea into an assessable decision
Share the intended users, workflow, information sources and constraints for a practical scope discussion.
Suitability
Who the service is for
Good fit
- A business workflow and responsible owner can be identified.
- Relevant users and subject-matter experts can participate.
- Data or knowledge sources can be assessed and governed.
- Leaders are willing to test quality, risk, adoption and cost.
- The organisation needs independent guidance across business and technology decisions.
May not be the right fit
- The objective is only to add AI branding without a defined user need.
- No accountable owner can approve scope, data access or risk decisions.
- The organisation expects a model to guarantee factual output without evaluation or oversight.
- The required data cannot be used lawfully or securely.
- A simple rules-based automation would solve the problem more reliably and economically.
Common use cases
Where generative AI can support business work
Suitability depends on the decision, data, consequences of error and required human oversight.
A
Enterprise knowledge assistant
Help authorised users locate, summarise and cite policies, procedures, product information or technical knowledge.
B
Service-agent assistance
Draft responses, surface approved information and summarise interactions while retaining agent review and escalation.
C
Document analysis
Extract, compare, classify and summarise documents with validation for material decisions or regulated content.
D
Marketing and content operations
Support briefs, variants, repurposing and review workflows within brand, evidence and approval controls.
E
Software and data-team assistance
Support code, tests, documentation, query explanation and incident analysis with repository and access controls.
F
Analyst and operations copilots
Summarise information, prepare decision packs and guide repeatable tasks without replacing accountable judgement.
Capabilities
What the consulting work can cover
Opportunity and readiness
Stakeholder discovery, workflow mapping, value hypothesis, use-case scoring, data and knowledge readiness, capability assessment, risk classification and pilot definition.
Architecture and engineering
Model selection, prompt and context design, retrieval-augmented generation, embeddings, vector search, agents and tools, APIs, identity, integration, observability and deployment patterns.
Evaluation and assurance
Test-set design, human review, groundedness, retrieval quality, relevance, robustness, safety, red-team scenarios, acceptance thresholds, cost and latency analysis.
Governance and operations
Accountability, acceptable-use rules, model inventory, vendor review, privacy and security controls, monitoring, incident response, change management, training and service ownership.
Deliverables
Typical outputs from a generative AI engagement
| Deliverable | What it contains | Decision supported | Client input |
|---|---|---|---|
| Opportunity and readiness assessment | Use cases, value, feasibility, data readiness, risk, dependencies and prioritisation | Where to invest first | Sponsors, process owners and representative workflows |
| Target solution architecture | Models, data flows, retrieval, tools, integration, identity, controls and environments | How the solution should be built | Architecture, security and platform constraints |
| Prototype or pilot | Representative workflow, approved data, user interface or API, logging and test results | Whether the use case merits production investment | Data access, SMEs, testers and acceptance criteria |
| Evaluation and assurance pack | Test set, metrics, failure analysis, safety review, thresholds and limitations | Whether quality and risk are acceptable | Domain reviewers, policy and risk input |
| Governance and operating model | Roles, inventory, controls, review gates, monitoring, incidents, change and training | How the capability will be governed and operated | Risk, privacy, security, legal and service owners |
| Implementation roadmap | Work packages, dependencies, priorities, decision gates, estimates and KPIs | How to move from pilot to operation | Budget, delivery capacity and procurement constraints |
Define the evidence needed before production
Build the evaluation, control and operational requirements into the delivery plan from the start.
Service process
How Dataconsultant delivers generative AI consulting
Discovery and alignment
Clarify the workflow, users, desired outcomes, constraints, stakeholders and decision criteria.
Primary output: agreed problem statement and engagement scope.
Use-case and readiness assessment
Assess value, data, technology, risk, process change, user adoption and feasibility.
Primary output: prioritised use cases and readiness findings.
Solution and control design
Define the model, retrieval, integration, security, privacy, governance and evaluation approach.
Primary output: target architecture and control requirements.
Prototype or implementation
Build the agreed workflow with representative data, logging, access controls and acceptance conditions.
Primary output: working solution increment and technical documentation.
Evaluation and review
Test quality, groundedness, safety, robustness, latency, cost and user acceptance against the intended use.
Primary output: evaluation report, limitations and go-forward decision.
Operational transition
Confirm ownership, monitoring, support, change control, training, incident handling and improvement cadence.
Primary output: operating plan, roadmap and knowledge transfer.
Technology and frameworks
Platforms, patterns, standards and delivery considerations
Technology selection is use-case-led and vendor-neutral. Named ecosystems are assessed against requirements rather than treated as default recommendations.
Model and AI platforms
Data and retrieval ecosystem
Reference frameworks
Applicable legal, regulatory and contractual requirements must be confirmed for the organisation’s jurisdictions, sector, data and intended use by authorised specialists.
Assess model, platform and control choices together
A technically capable solution can still fail when data rights, user workflow, evaluation or operating ownership are incomplete.
Engagement models
Flexible ways to engage
| Model | Best suited to | Typical focus | Client responsibility |
|---|---|---|---|
| Focused advisory | A specific decision or use case | Assessment, architecture, governance or evaluation review | Provide evidence and make decisions |
| Discovery and roadmap | Multiple ideas or an emerging AI programme | Portfolio, readiness, target state and prioritised roadmap | Executive sponsorship and cross-functional participation |
| Pilot or implementation project | A prioritised workflow with available data | Design, build, test, assurance and transition | Data access, SMEs, platforms and acceptance |
| Embedded specialist support | Internal teams needing additional capability | Architecture, engineering, evaluation or governance roles | Programme direction and team integration |
| Managed improvement support | Operational solutions requiring ongoing oversight | Monitoring, evaluation, updates, controls and reporting | Service ownership and decision escalation |
Illustrative examples
How the service may be applied
Controlled policy assistant
An organisation needs staff to find and understand approved policies across multiple repositories.
- Role-aware retrieval and source permissions
- Citations to approved policy passages
- Evaluation for relevance, completeness and unsupported claims
- Escalation when information is absent or ambiguous
Customer-service drafting assistant
A service team wants faster response preparation without allowing uncontrolled automated communication.
- Approved knowledge and tone rules
- Agent review before sending
- PII handling, logging and retention controls
- Measures for response quality, handle time and adoption
These examples are illustrative and do not represent guaranteed outcomes or named client results.
Outcomes and KPIs
Measure technical quality, business value and control effectiveness
Business and user measuresTask completion, cycle time, adoption, satisfaction, escalation and outcome quality.
Model and response qualityGroundedness, relevance, completeness, factuality, refusal and consistency.
Retrieval performanceRecall, precision, source coverage, citation accuracy and permission adherence.
Operational performanceLatency, availability, incident volume, support demand, token usage and cost per task.
Risk and control measuresPolicy exceptions, harmful-output rate, privacy events, unresolved findings and review completion.
Delivery and adoptionMilestone completion, acceptance pass rate, training completion, active use and feedback closure.
Pricing and cost factors
What affects the cost of generative AI consulting
Scope and use cases
Number of workflows, user groups, business units, languages and required outputs.
Data and integration
Source quality, access, preparation, permissions, APIs, systems and environment complexity.
Evaluation and risk
Test-set depth, specialist review, red teaming, regulatory scrutiny and assurance evidence.
Operating requirements
Scale, latency, availability, monitoring, support, model usage, training and ongoing improvement.
Request a scope based on your actual use case
Reliable estimates require clarity on users, data, integrations, risk, evaluation and operational expectations.
Why consider Dataconsultant
Business, data, AI and governance decisions in one delivery approach
Dataconsultant connects the intended business outcome with the data, architecture, evaluation, controls and operating model needed to sustain it. The approach is evidence-conscious, vendor-neutral and designed to make assumptions, limitations and decisions visible.
Assessment-led planning
Documented architecture
Evaluation before scale
Security and privacy input
Knowledge transfer
Flexible delivery models
Security, quality, privacy and compliance
Controls should reflect the use, data and consequence of error
Data protection and privacy
Data classification, lawful use, minimisation, redaction, access, retention, residency, data-subject considerations and provider terms.
Security
Identity, least privilege, secret management, encryption, environment separation, logging, threat scenarios, secure integration and incident response.
Quality and evaluation
Representative test data, documented metrics, human review, failure analysis, robustness testing, acceptance thresholds and regression checks.
Governance and compliance
Accountable owners, use-case inventory, risk classification, approval gates, human oversight, vendor risk, monitoring and evidence retention.
Delivery environment
Designed to work with existing enterprise ecosystems
The service can coordinate with internal teams, cloud and model providers, data platforms, software vendors, systems integrators, risk functions and managed-service partners.
Business teams
Process ownership, user needs, subject expertise and value measures.
Data and technology
Architecture, platforms, engineering, integration, identity and operations.
Control functions
Security, privacy, legal, compliance, risk, audit and procurement.
Delivery partners
Cloud, model, software, integration and managed-service providers.
Representative customer perspectives
How generative AI consulting can support delivery teams
The following representative testimonials illustrate the types of service experience organisations may value. They are not presented as verified client reviews or performance claims.
CP
★★★★★
Chief Product Officer“The engagement helped us separate attractive AI ideas from the workflows that had a defensible customer and commercial case. The team documented assumptions, data dependencies, user risks and evaluation criteria clearly, which gave product and engineering leaders a shared basis for deciding what to prototype.”
DK
★★★★★
Director of Knowledge Management“Our knowledge-assistant concept became much more practical once source permissions, metadata, retrieval, citations and content ownership were addressed together. The consultants were direct about gaps in our repositories and gave us a phased design that our information-management and technology teams could operate.”
VH
★★★★★
Vice President, Customer Operations“The pilot focused on agent assistance rather than uncontrolled automation. Response quality, escalation, privacy and human review were designed into the workflow, and the evaluation showed where the model added value and where standard process rules remained the safer option.”
AR
★★★★★
AI Risk and Assurance Lead“We needed more than a policy document. The work connected use-case inventory, risk classification, model and vendor review, testing evidence, approval gates, monitoring and incident ownership. It gave our risk team a usable control model without removing responsibility from business and technology owners.”
ME
★★★★★
Head of Data Platforms“The architecture review was balanced and vendor-neutral. It covered model access, retrieval, identity, observability, cost, data movement and failure handling rather than concentrating only on prompts. That helped us reuse existing platform capabilities and avoid an unnecessary standalone stack.”
SL
★★★★★
Transformation Programme Director“The transition plan made ownership explicit across product, engineering, data, security and operations. We received evaluation thresholds, support processes, training needs, cost measures and an improvement backlog, so the pilot could move into controlled service management rather than remaining an isolated innovation project.”
Frequently asked questions
Generative AI consulting questions
Direct answers to common service, delivery, governance, technology and commercial questions.
What is a generative AI consulting service?
A generative AI consulting service helps an organisation identify valuable use cases, assess data and technology readiness, select appropriate models and architectures, design governance and controls, build and evaluate prototypes, and plan or support production implementation.
What is included in Dataconsultant’s generative AI consulting service?
Scope can include opportunity discovery, use-case prioritisation, data readiness, model and vendor assessment, prompt and workflow design, retrieval-augmented generation architecture, evaluation, security and privacy controls, governance, implementation planning, pilot delivery and operational transition.
Which organisations are a good fit for generative AI consulting?
The service is suitable for organisations with a defined business problem, accessible knowledge or process data, accountable sponsors, subject-matter experts and willingness to measure quality, risk and adoption. It can support startups, SMEs, enterprises and regulated organisations.
How are generative AI use cases prioritised?
Use cases are assessed against business value, user need, data readiness, model suitability, process impact, safety, privacy, security, regulatory exposure, integration effort, operating cost, adoption requirements and measurable success criteria.
Can Dataconsultant help with retrieval-augmented generation and enterprise knowledge assistants?
Yes. Support can cover source selection, ingestion, chunking, metadata, embeddings, retrieval design, access controls, prompt orchestration, citation behaviour, evaluation, monitoring and integration with approved enterprise systems.
How do you evaluate a generative AI solution?
Evaluation can combine task-specific test sets, human review, groundedness, relevance, completeness, factuality, safety, refusal behaviour, latency, cost, retrieval quality, robustness and user acceptance. Measures should reflect the intended use and risk level.
What security and privacy controls are considered?
Relevant controls may include data classification, approved-use rules, access management, encryption, logging, prompt and output handling, retention, redaction, model-provider terms, data residency, vendor risk, incident processes and human oversight.
How long does a generative AI consulting engagement take?
There is no reliable fixed duration without discovery. Timing depends on scope, stakeholder access, data readiness, integration complexity, risk review, procurement, model selection, evaluation requirements and whether the engagement includes a prototype or production implementation.
What affects generative AI consulting cost?
Cost depends on the number and complexity of use cases, data preparation, integrations, model and cloud choices, evaluation depth, security and compliance needs, user volumes, operating requirements, change management and the level of implementation support.
Can Dataconsultant work with our existing cloud, data and AI vendors?
Yes. The engagement can work with existing internal teams, cloud providers, model platforms, data platforms, systems integrators and software vendors. Responsibilities, access, constraints, decision rights and acceptance criteria are documented at mobilisation.
What client participation is required?
Useful participation includes an accountable sponsor, business process owners, subject-matter experts, data and architecture teams, security, privacy, legal or compliance reviewers, procurement and representative users for testing and adoption feedback.
Can the service continue after a pilot?
Yes. Follow-on support can include production engineering, evaluation operations, governance implementation, monitoring, model or prompt updates, knowledge-base maintenance, managed service support, training and continuous improvement.