Retail/Web Development
Web-based interior design platform
3D web tool for designing rooms and selecting furniture
View projectAI Consulting
Client Challenges
AI Planning Challenges Before Investment Decisions
Companies usually engage Solbeg for AI consulting at the decision stage, when ideas already exist but there is no consistent basis for comparing them. The immediate task is to determine which initiatives justify investment, what engineering and data they require, and how their results should be measured.
Product, operations, support, and other teams may bring forward AI ideas that are all reasonable in isolation. Without shared evaluation criteria, proposals are difficult to compare consistently, making it harder to identify which initiatives offer the strongest combination of business value, feasibility, and data readiness.
Estimates for the same idea may vary significantly depending on who is asked and which parts of the production scope are included. Planning and budgeting then become difficult, especially when data preparation, integration, security, infrastructure, and ongoing operation have not yet been assessed.
Licenses may have been purchased and prototypes demonstrated without establishing whether the underlying process improved. If there was no baseline or agreed success metric, the organization has little evidence for deciding whether to expand, change, or stop the initiative.
Legal, security, data, and engineering teams may each hold part of the information required to determine what data can be used, where it can be processed, and which controls are necessary. Without a coordinated assessment, initiatives can remain in review while these questions move between departments.
Service Overview
AI Advisory Across Opportunity Assessment, Architecture, Governance, And Delivery Planning
Advisory work can cover the full path from opportunity assessment to a delivery roadmap or focus on a specific decision that is currently blocking progress. Companies can engage Solbeg as an artificial intelligence consulting company and technology partner for a broader review of their AI initiatives or for an individual area that requires technical and business assessment, with implementation available when needed.
Mapping candidate processes, the decisions made within them, the people involved, and the results the process currently produces. Each opportunity is described in terms of the business problem, available data, expected improvement, and the conditions required for success before a particular technology is selected.
Assessment of the data available for the proposed use case, including how it is collected, stored, accessed, and maintained. The review can also cover existing environments, integrations, security constraints, and infrastructure requirements for model development and inference.
Comparison of candidate initiatives against factors such as expected business value, data readiness, engineering effort, dependencies, and operational risk. Our AI consulting services can turn the selected initiatives into a sequenced roadmap so investment and implementation can proceed in defined stages.
Comparison of relevant technical options, which can include commercial model APIs, open-weight models, traditional machine learning approaches, and custom model development where justified. The assessment also considers how the selected approach would integrate with the existing product and what operational responsibilities it would create.
Definition of how prompts, datasets, embeddings, model outputs, and other relevant information can be processed and stored, together with the access, review, and monitoring controls required by the organization.
Definition of the engineering roles, sequence of work, internal responsibilities, and operating model needed to move the selected initiative into implementation and ongoing support. The resulting plan can be executed internally, externally, or through a combined team.
Problem & Solution
The problem
Technology is often selected before success criteria are defined
AI initiatives frequently begin with a tool, platform, or model that someone wants to test. A pilot is then built before the organization has clearly defined which business decision or process the technology is intended to improve. Once an architecture has already been chosen, its technical and commercial constraints can start determining which use cases remain practical.
Measurement is another common gap. If the current process has no baseline, it becomes difficult to determine whether the AI-enabled alternative performs better in a way that matters to the business. General benchmark scores do not show whether a particular output is reliable enough for employees or customers to act on it, and different types of errors can have very different consequences.
Production requirements also extend far beyond the initial model or demonstration. A working system may need data pipelines, authentication, integration, monitoring, fallback behavior, versioning, security controls, and a defined process for managing quality changes. When this surrounding work is excluded from the original plan, a successful prototype may still be a long way from something the organization can operate reliably.
Solbeg's approach
Assessment starts with the decision, baseline, and operating conditions
Artificial intelligence consulting at Solbeg begins with the business process and the decision the proposed system is meant to support. The assessment considers who will use the output, what the current process produces, which errors are important, and what improvement would justify implementation. Technology options are then evaluated against those requirements.
Success criteria are defined with the stakeholders who understand the process and will ultimately use or approve the output. This can include a documented baseline, relevant business and technical metrics, and an evaluation method based on the organization’s own data. The same criteria can later provide a basis for deciding whether an initiative should proceed, change direction, or stop.
Recommendations cover the production environment as well as the model. Data pipelines, integration, deployment architecture, security boundaries, monitoring, operating cost, support responsibilities, and required roles are considered as part of the plan. AI strategy consulting is more useful when the resulting roadmap reflects the complete work required to make the proposed capability operational.
Because Solbeg also delivers software, data, cloud, QA, and AI engineering, implementation effort can be reviewed by teams familiar with the types of systems described in the roadmap.
When this service fits
Business And Technical Decisions That Benefit From AI Advisory
AI advisory is useful when an organization needs evidence before committing budget, choosing an architecture, scaling a prototype, or building an internal capability. Solbeg can assess one priority decision independently or connect it to a broader AI roadmap.
Several initiatives are competing for funding, but they have been proposed using different assumptions and cannot be compared consistently. An assessment can establish expected outcomes, data requirements, engineering scope, dependencies, and effort ranges so the portfolio can be reviewed using the same decision criteria.
A supplier has proposed a solution, but your internal team does not have enough information to evaluate the architecture, pricing assumptions, data requirements, or production implications. An independent review can identify what the proposal covers, what responsibilities remain with your organization, and what additional work may be required around integration, security, operation, or scaling.
Transactional, operational, document, or other historical data may already exist without being used for prediction, classification, search, or automation. Working with an AI consultancy at this stage can establish which use cases are realistic with the available data and which would first require additional collection, labeling, cleanup, or integration work.
A team has demonstrated that an idea can work under controlled conditions, and leadership now needs to understand what would be required to turn it into a supported production capability. The assessment can cover evaluation quality, data pipelines, architecture, security, integration, monitoring, operational ownership, and other work that may not be represented in the prototype.
Residency, confidentiality, contractual, or internal security requirements may restrict the use of particular public AI services. An assessment can identify technically valid deployment options and explain how each affects infrastructure, operations, output quality, latency, and cost.
Inference charges, infrastructure usage, human review, or support effort may increase after launch. A review can examine model selection, request patterns, prompt and context size, caching, architecture, deployment choices, and other cost drivers to determine where changes may be practical.
The roadmap may require business analysis, data engineering, data science, ML engineering, application development, MLOps, QA, or other capabilities that are not currently available internally. An assessment can identify which roles are actually required, when they become necessary, and which responsibilities make sense to keep inside the organization.
Scope of Work
AI Advisory Workstreams Across Strategy, Data, Architecture, And Delivery Planning
Solbeg defines the advisory scope around the decisions the client needs to make and the technical environment those decisions affect. Artificial intelligence consulting services can combine business analysis, data readiness, feasibility, architecture, operations, security, evaluation, and implementation planning within one engagement.
AI opportunity and process analysis
Mapping of the target process, decision points, users, current performance, and the problem the proposed AI capability is expected to address. The output can include a documented problem statement, baseline, and initial success criteria that later engineering work can use.
Data readiness assessment
Review of data sources, coverage, quality, accessibility, labeling, versioning, and relevant governance requirements. The assessment identifies preparation work that may be needed before model training, retrieval, evaluation, or production use becomes practical.
Feasibility and model approach
Comparison of appropriate technical approaches, including commercial model APIs, open-weight models, traditional machine learning, and custom models where relevant. Evaluation requirements and realistic constraints are defined using the data, accuracy needs, and operating conditions of the specific use case.
Solution and integration design
Definition of how the AI capability would fit into the existing software environment, including interfaces, authentication, queues, caching, timeouts, retries, fallback behavior, and handling of cases where model output does not meet the required criteria.
Infrastructure and operations planning
Definition of development, training, inference, deployment, monitoring, and release requirements. The scope can include model and dataset versioning, CI/CD, latency monitoring, infrastructure usage, inference cost, and the operational responsibilities required after launch.
Data handling and security review
Assessment of where data can be processed, which information can be transmitted to external systems, whether sensitive data requires masking or other controls, and which access, logging, storage, and retention requirements apply to relevant data and model artifacts.
Evaluation and acceptance planning
Definition of how the proposed system would be measured, including evaluation datasets, relevant quality metrics, regression testing, edge cases, and failure scenarios. The plan distinguishes testing of conventional software behavior from evaluation of probabilistic model output.
Roadmap, effort, and team composition
Conversion of the recommended direction into a sequenced implementation plan with dependencies, required roles, decision points, and effort ranges. This part of AI consultancy services helps determine whether the proposed roadmap can be staffed and executed under the available delivery model.
Why Solbeg
How Solbeg Connects AI Advisory With Production Engineering
Solbeg combines AI advisory with software development, data engineering, cloud, QA, mobile, and support capabilities. Recommendations can therefore be reviewed against the same engineering considerations that will later determine whether the proposed solution can be built, integrated, and operated effectively.
Recommendations are informed by production engineering
Architecture options, implementation dependencies, and effort considerations can be reviewed by engineers familiar with comparable delivery work. For teams evaluating an AI consulting company, Solbeg connects advisory recommendations with the practical requirements of building, integrating, testing, and operating the resulting system.
Feasibility is assessed against your data and requirements
General benchmarks do not represent the exact data distribution, error profile, and operating environment of a specific organization. Advisory work can define evaluation around your available data and the baseline of the current process, giving stakeholders a more relevant basis for investment decisions.
The production scope is considered beyond the model itself
Data preparation, integration, security, monitoring, infrastructure, support, and operating cost can be included in the assessment. This reduces the risk of approving a roadmap that accounts for model development but leaves substantial production engineering work undefined.
Deployment constraints shape the recommendation
Where confidentiality, residency, contractual, or internal security requirements apply, technical options can include private-cloud, self-hosted, or on-premises approaches where appropriate. AI strategy consulting services can therefore be structured around actual deployment constraints instead of assuming that every use case can rely on a public model API.
Advisory can continue into implementation or stop at the plan
Solbeg can implement the recommended roadmap, transfer it to the client’s internal team, or support review of implementation performed by another supplier. Documentation is prepared so the outputs remain usable regardless of which team performs the next stage.
AI and the surrounding software can be considered within one engagement
AI capabilities usually have to operate inside an existing product or process, which can require application development, data engineering, integrations, infrastructure, QA, and support alongside model-related work. Considering these areas together reduces gaps between the AI recommendation and the software environment in which it has to operate.
Comparison
Portfolio-Level AI Strategy Vs. Single-Use-Case Assessment
Solbeg can assess several AI initiatives as one portfolio or examine a single priority use case in depth. An engagement with an artificial intelligence consulting firm can be structured around either model depending on the number of candidate initiatives, the level of technical uncertainty, and how investment decisions are made.
Engagement Process
AI Advisory Across Assessment, Architecture, And Investment Planning
Solbeg structures AI advisory around the decision the organization needs to make, the evidence required to support it, and the technical work needed if the initiative proceeds. Individual stages can also be scoped independently when only one question needs to be resolved.
-
Intake and scope definition
We clarify the question the assessment needs to answer, the business or technical decision it supports, the stakeholders involved, and who will approve the outcome. Required access, available information, and the boundaries of the review are agreed before detailed analysis begins.
-
Process and baseline review
Business analysts and engineers work with the relevant process owners to document how the current process operates, who uses its outputs, and what results it produces today. Stakeholders define what an improvement would need to look like for the proposed AI capability to justify further investment.
-
Data, infrastructure, and security assessment
Relevant data sources, data quality, pipelines, environments, integrations, security requirements, and processing constraints are reviewed. These findings narrow the technically valid options before a specific architecture or model approach is recommended.
-
Options, architecture, and effort modeling
Candidate approaches are compared against the agreed requirements. The output can include architecture directions, evaluation methods, infrastructure implications, major dependencies, effort ranges, and expected operating cost drivers.
-
Roadmap and handover
Findings are consolidated into a sequenced plan with dependencies, required capabilities, risks, decision points, and the evidence each stage should produce before further investment is approved. The roadmap is documented so it can be implemented by the client’s internal team, Solbeg, or another AI consulting firm.
Completed projects
View all projectsProjects we’re proud of
Examples of how we help companies turn ideas into reliable digital products that people use every day.
Real Estate/Web Development
Leading Home Renovation Contractor
Web app for calculating bathroom renovation costs
View project
Oil & Gas/QA/Software Development/Web Development
Data Automation Platform for Oil & Gas Operations
Mobile and web platform for vehicle service and ownership management
View projectFAQ
Key Questions Before Starting An AI Advisory Engagement
No. Understanding the current state of the data is part of the assessment. The review can establish what exists, how it is collected, how accessible and reliable it is, and what preparation individual use cases would require. In some engagements, improving data quality, availability, or governance becomes an earlier roadmap priority than model development.
Yes. The review can begin with documenting the current system, its architecture, data, evaluation approach, and observed behavior before recommendations are made. It can then assess what is already working, which production requirements remain unresolved, and what would be required to move the initiative forward. Independent reviews are one reason companies engage AI consulting firms separately from the original implementation provider.
Yes. An assessment can conclude that conventional software, process redesign, data engineering, search, rules-based automation, or another approach is more appropriate for the problem. Identifying this before a larger implementation begins is a valid outcome of advisory work.
Advisory focuses on what problem should be addressed, whether the proposed approach is feasible, what architecture and data it requires, how success should be measured, and what implementation would involve. AI and machine learning development services cover the engineering work required to build and operate the solution. When comparing AI consulting companies and implementation partners, it is useful to separate these responsibilities, while Solbeg can support either stage or both.
The exact participants depend on the scope, but access to the people who understand the process, data, technical environment, and relevant security or compliance constraints is important. This often includes a process or product owner, a data owner, technical stakeholders responsible for integration or infrastructure, and representatives who can clarify legal, security, or operational requirements.
Related Services
You may also need
End-to-end services to build, launch, and grow your digital product.