Healthcare/Online Scheduling/Public Sector/Retail/Web Development
Online Appointment Scheduling System
Online booking system for appointments and reservations
View projectGenerative AI & LLM
Client Challenges
Production Challenges After A Successful Generative AI Prototype
Solbeg is usually brought into Generative AI initiatives after an internal experiment has already shown potential. The next challenge is to prove that the system can handle real inputs, meet defined quality requirements, comply with data policies, and operate with predictable cost and latency.
A notebook or small internal tool handles curated examples but struggles with the variety of real inputs. There may be no access control, logging, versioning, or reliable way to roll back a change in behavior. Moving the prototype toward production can therefore require significant work around the model itself.
The team can see that some answers are wrong, but cannot say how often, in which categories, or whether the latest change improved the result. Without a repeatable evaluation process, stakeholders have no objective basis for approving customer-facing or business-critical use. The project may remain in pilot mode when quality cannot be measured consistently.
Legal, security, contractual, or internal data-handling requirements may restrict where customer data, source code, or regulated records can be processed. An architecture built around a public model API can become unsuitable after security review, forcing the team to reconsider deployment options after development has already started.
Cost can change with request volume, prompt size, retrieved context, and model choice. Response times that were acceptable during a demo may become a challenge inside a workflow used throughout the day. Without measuring these parameters under realistic load, the team cannot reliably estimate operating costs or expected performance before rollout.
Service Overview
Generative AI Engineering Across Assessment, Retrieval, Application Development, And Operations
Solbeg’s Generative AI services can include use case assessment, solution design, retrieval, model adaptation, application development, evaluation, and production operations. An engagement can begin with a new initiative or with an internal prototype that needs technical review and a clear production plan.
Review of candidate use cases against available data, expected output quality, business value, security constraints, and deployment requirements. The result can include a defined scope, target architecture, and criteria against which the system will be evaluated.
Connecting a model to documents, records, knowledge bases, and other internal sources so responses can use information specific to your organization. This can include ingestion, indexing, retrieval logic, content refresh, and enforcement of existing access permissions.
Selecting an appropriate model and shaping its behavior through prompt design, structured output formats, retrieval, and fine-tuning or other adaptation techniques when the use case requires them.
Building the software layer around the model, including user interfaces, orchestration of multi-step tasks, tool and API calls, workflow state, and fallback behavior when individual steps cannot be completed as expected.
Evaluation datasets, scoring methods, and regression checks that make output quality measurable across changes, together with controls for relevant risks such as prompt injection, unintended data exposure, and requests outside the intended scope.
Running Generative AI solutions in production with monitoring of quality, latency, reliability, and cost, as well as a controlled process for changing prompts, indexes, application logic, and model versions.
Problem & solution
The problem
Probabilistic model behavior requires a different validation approach
Software teams are accustomed to acceptance criteria that can be checked consistently: a test passes or fails, and a defect can usually be reproduced under the same conditions. A language model can produce different formulations for similar inputs, be correct in substance but wrong in structure, or fail on an input that looks very similar to one it handled successfully. Standard application testing alone is therefore not enough to determine whether a Generative AI system is ready for production.
The engineering challenge also extends beyond the model itself. Retrieval quality, source data, permissions, document structure, and integration with existing systems often determine whether the application produces useful results. Relevant information may be spread across several sources, contain conflicting versions, or follow access rules that were never designed for AI-assisted retrieval.
The work also crosses traditional team boundaries. Data and ML specialists may be able to prove that an approach is technically possible, while application engineers are responsible for turning it into a reliable service. Without shared quality criteria and clear ownership, the prototype can remain between those two disciplines without a defined path to release.
Solbeg's approach
Evaluation criteria are defined before production decisions
Before the production scope is finalized, Solbeg defines what acceptable output means for the use case and establishes an evaluation approach using representative inputs. Quality can then be compared across prompt changes, retrieval adjustments, model versions, and other modifications. Within Generative AI consulting, these criteria give technical and business stakeholders a shared basis for deciding whether a change is ready to release.
Data and retrieval are treated as part of the core engineering scope. Relevant sources are reviewed, indexing and segmentation approaches are defined, access rules are considered at retrieval time, and a process is established for keeping indexed content current. If the available source data cannot support the required output quality, that limitation can be identified during assessment before the full system is built.
The surrounding software is designed for production use. Solbeg’s Generative AI development services can include authentication, input and output logging under agreed data policies, prompt and index versioning, controlled rollout, monitoring, and integration with existing applications and services.
Application engineering, data engineering, QA, and AI and Machine Learning development can be coordinated within the same delivery scope. This allows retrieval behavior, application logic, and evaluation criteria to evolve together instead of being passed between teams as separate deliverables.
When this service fits
Business And Product Use Cases For Generative AI
Generative AI is most useful when the task has a defined business outcome, suitable source data, and a clear role for automated output or human review. Solbeg evaluates these conditions together with quality, security, and operating requirements before the architecture and model approach are finalized.
Policies, specifications, proposals, technical documentation, and other internal knowledge may be distributed across several systems. A retrieval-based assistant can help users find relevant information in context and, where required, reference the underlying source. Existing access rules need to be reflected in the retrieval process so users do not receive content they are not permitted to access.
Support teams may repeatedly prepare similar answers from the same product documentation, customer history, or knowledge base. Generative AI can prepare drafts for an agent to review, while the workflow defines when human approval is required and how uncertain or unsupported requests are handled.
Contracts, invoices, claims, reports, and other documents may arrive in different layouts while the required information ultimately needs to enter a structured business system. Extraction into a defined schema can automate part of that process, with validation and human review for cases that do not meet the required confidence or business rules.
The feature has to work inside an existing application, respect user and tenant boundaries, integrate with the existing architecture, and remain supportable after release. Generative AI solutions embedded in commercial products therefore require more than model access alone, including monitoring, data isolation, predictable operational behavior, and a defined release process.
Employees may begin using general-purpose AI tools before the organization has established an approved process for handling company information. An internal or sanctioned alternative can introduce defined access, logging, and data-handling rules while still providing enough practical value for people to use it in their daily work.
Leadership may need to understand which use cases are technically feasible, what data they depend on, what implementation options exist, and which initiatives do not justify further investment. An assessment can provide that decision framework without requiring a full production build first.
Reports, summaries, or standard documents may be prepared repeatedly from information already available in internal systems. Generative AI can produce a draft for specialist review when the source data and expected output structure are sufficiently defined, shifting part of the effort from initial preparation to validation.
Scope of Work
Engineering Responsibilities In A Generative AI Engagement
Team composition depends on the use case, source data, integrations, security requirements, and deployment model. LLM development services can combine the capabilities required for production delivery while leaving selected responsibilities with the client’s internal team.
AI solution architecture
Definition of the use case boundaries, target architecture, model-selection criteria, deployment options, and the main trade-offs between output quality, latency, data constraints, and operating cost.
Data and retrieval engineering
Ingestion from relevant source systems, preparation and segmentation of content for indexing, implementation of retrieval logic, and definition of the refresh process. The scope can also include document-level and user-level permissions so retrieval follows existing access rules.
Model adaptation
Prompt design, structured output contracts, model comparison, and fine-tuning or other adaptation methods when required by the task. Candidate approaches are evaluated against the project’s own representative inputs rather than selected only from general benchmarks.
Application and integration development
Implementation of the user-facing application and service layer, multi-step workflows, tool and API calls, integration with business systems, authentication, rate limiting where appropriate, and failure handling for external dependencies.
Evaluation and testing
Creation of evaluation datasets, scoring methods, and regression checks for relevant output types. Testing can also cover misuse and failure scenarios such as prompt injection, unintended retrieval of restricted information, and requests outside the intended scope.
Deployment and monitoring
Setup of the agreed inference environment, versioning of prompts, indexes, application configuration, and models where appropriate, together with a rollback process. Monitoring can cover output quality, latency, failures, and cost-related metrics.
Data handling and security controls
Definition of where data is processed, which information can be logged or retained, how sensitive data is protected, and how access is controlled. The implementation is aligned with the security requirements agreed for the project and with the client’s internal review process.
Why Solbeg
How Solbeg Connects Generative AI With Production Engineering
Solbeg delivers Generative AI within a broader software engineering practice, allowing model work, data engineering, application development, QA, and production operations to be coordinated within one engagement.
AI work is delivered as software engineering
Teams evaluating a Generative AI development company usually need more than a working model demonstration. Solbeg applies production engineering practices around the model layer, including version-controlled configuration, staged releases, monitoring, and rollback procedures. This makes later changes to prompts, retrieval, integrations, and models easier to evaluate and manage.
Quality criteria are established before production rollout
Evaluation data and acceptance criteria can be defined with the client’s subject matter experts as part of the engagement. This gives stakeholders a repeatable basis for reviewing changes instead of relying only on individual demonstrations or subjective impressions of output quality.
Model choice is treated as an engineering decision
Models can be compared against the requirements of the actual use case, including output quality, data policy, latency, deployment constraints, and operating cost. The architecture can also separate the application from the model layer where practical, reducing unnecessary dependency on the provider selected for the initial experiment.
Integration is part of the solution scope
A production AI capability usually depends on existing databases, document stores, APIs, identity systems, and business applications. Solbeg plans those integrations as part of the engineering scope so the resulting capability fits into the actual workflow instead of remaining an isolated assistant or demonstration.
Cost and performance are measured during delivery
Solbeg can track request volume, model usage, retrieved context, latency, and other cost drivers during development and testing. For companies evaluating a Generative AI consulting company, including these parameters in the engineering scope provides a more realistic basis for estimating operating requirements before broader rollout.
Comparison
Hosted Model APIs Vs. Open-Weight Models In Your Own Environment
One of the early architecture decisions is whether the application will use a hosted model service or run an open-weight model in infrastructure you control. The choice affects implementation effort, data handling, operational responsibility, scalability, and cost structure.
Engagement Process
Generative AI Delivery Across Feasibility, Evaluation, Build, And Deployment
Solbeg resolves feasibility, data, quality, and architecture questions before the highest-cost production work begins. The scope can also account for discovery, data preparation, or prototyping that has already been completed internally.
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Discovery and use case scoping
We review candidate use cases with the relevant business and technical stakeholders and identify which ones have a sufficiently defined output, available data, and measurable business objective. The result is an agreed shortlist and a clearer definition of what each use case would need to deliver.
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Data and feasibility review
We examine the relevant source data, its structure, quality, accessibility, and permission model and assess whether it can support the intended use case. This stage helps define the architecture, deployment options, major constraints, and areas where the current data may need additional preparation. Solbeg’s Generative AI consulting services can confirm the proposed direction or identify reasons to change it before a larger implementation is funded.
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Evaluation design and prototype
Representative inputs and expected results are defined together with the relevant subject matter experts, and an evaluation approach is established. A prototype can then be measured against those criteria so stakeholders can review performance on their own use cases rather than only on generic examples.
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Production build
Engineering, data, QA, and other required specialists implement the production solution, including retrieval where applicable, the application layer, integrations, safeguards, and the release process. Evaluation continues throughout development so changes can be compared against the agreed criteria.
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Deployment and operation
The system is deployed into the agreed environment with monitoring appropriate to the use case, including relevant quality, latency, reliability, and cost metrics. Documentation and operating procedures are prepared for the people who will maintain or support the solution after release.
Completed projects
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View projectFAQ
Key Questions Before Starting A Generative AI Project
Usually not. Training a foundation model from scratch requires substantial data, compute, and specialist resources and is unnecessary for most business applications. A more common approach is to use an existing model together with retrieval, prompt engineering, structured outputs, or fine-tuning when the use case requires additional adaptation.
Yes. One option is to deploy an appropriate open-weight model in infrastructure controlled by your organization or another agreed environment. This gives you more control over where inference takes place, but it also introduces responsibility for compute infrastructure, deployment, scaling, monitoring, and model operations. The appropriate option depends on your security requirements, expected workload, required output quality, and operational constraints.
The release criteria are defined against an evaluation approach agreed for the use case. Representative inputs, expected outcomes, and scoring rules can be established with your subject matter experts, allowing different versions of the system to be compared consistently instead of being approved only on the basis of a demonstration.
It depends on the use case. The required source information needs to be available in a form that can be accessed and governed appropriately, but it does not have to be perfectly standardized before assessment begins. Contradictory, outdated, incomplete, or poorly structured sources can affect output quality, so identifying those limitations is part of the data review.
Operating cost depends on factors such as request volume, model choice, context size, retrieval behavior, infrastructure, and deployment model. These drivers can be measured during development and testing so the expected operating profile is understood before broader rollout. For self-hosted models, compute capacity and utilization become important parts of the calculation.
Yes. An existing provider or model choice can be used as a starting point and evaluated against the project’s requirements. Clients comparing an LLM company often want to avoid unnecessary dependence on one model provider. Solbeg can structure the surrounding application and integration layer so model dependencies are clearly separated where practical. If another model performs differently against the agreed evaluation criteria, the comparison can be made using the project’s own inputs and requirements.
A new model can be evaluated against the same representative dataset and acceptance criteria before a change is made. Keeping prompts, retrieval, integrations, and application logic separated from the model layer where practical makes replacement more controlled. At Solbeg, evaluation and version management are treated as part of the production solution. When comparing a Generative AI services company, it is worth confirming that these capabilities are included beyond the initial prototype.
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