Generative AI Development Services

What can generative AI realistically do in our product today — and what still can’t be trusted to it?

Generative AI can assist with a range of content workflows, including summarization, drafting, translation, and transcription. It can also support routine software development tasks, such as code generation and documentation. 

In customer-facing applications, generative AI can enable personalized recommendations and automate responses to common requests. Combined with other AI techniques, it can also help classify and prioritize customer requests, orders, and support tickets based on predefined criteria. 

However, it cannot guarantee factual or numeric accuracy with 100% certainty and should not be allowed to make independent high-risk decisions; for instance, AI-generated code can contain critical errors or security vulnerabilities. This limits AI’s ability to operate independently in high-risk fields without expert human oversight and output validation and, at this stage in its growth, makes it a valuable tool that streamlines certain workflows rather than automating them.

Here at SysGears, we can assess your business goals and tell you realistically whether or not generative AI, at the level it is available today, can achieve them with an acceptable balance of quality and risk, and how much human evaluation it would require.

How much does it cost to build generative AI solutions?

At SysGears, we use a time-and-materials pricing model, with rates ranging from $39 to $80 per hour based on the seniority, role, and level of involvement of each of our experts working on your project. We can provide a rough project estimate before committing, and a tailored one after assessing the overall scope of the work, to simplify resource planning and allocation.

Do we need a custom model, or is fine-tuning or RAG on an existing foundation model enough?

A custom model may be worth considering when existing models cannot meet key requirements for capabilities, deployment, privacy, or performance. Whether to build one depends on the use case and available resources for the project. RAG can provide access to frequently updated sources at response time and retrieve the information most relevant to a given query or task. Fine-tuning can adapt a model’s behavior or style and improve how consistently it follows specific output patterns. The right approach depends on the use case, and these methods can be combined.

We can assess your intended use case for the AI model and recommend an approach that will best fit the way your solution is expected to be used, ensuring an optimal balance between time & cost, performance, and accuracy.

How do you build guardrails, human review, and fallbacks into an AI-generated workflow?

When working on AI-powered workflows, we take multiple steps and approaches to ensure resilience and accuracy:

• Pre-processing guardrails: screening user input, masking personally identifiable information where appropriate, and proactively applying security controls to reduce prompt injection risk.
• Post-processing guardrails: enforcing strict output formats with validation frameworks, executing fast checks before expensive classifiers, and allowing models to say “I don’t know” when they don’t have enough trusted data for a conclusive answer.
• HITL review: explicitly stopping high-stakes workflows for human validation, as well as flagging and logging human-required actions in a dedicated location.
• Fallbacks: defining specific failure models, set iteration, tool, and execution limits, and fall back to a rule-based response or an alternative model if the primary one times out.

Who owns the model, the prompts, the fine-tuned weights, and the training data at the end of the engagement?

You own all project-related code and intellectual property that is not otherwise subject to other licenses, such as third-party tools. This is explicitly outlined in the legal documentation we sign at the start of our partnership to protect both parties from IP theft and other forms of fraud.

What happens if our chosen model gets deprecated or the provider changes pricing — are we locked in?

We sit down with your stakeholders and lead technical staff to discuss the current state of the project and the way forward together. When we design or update the backend, we can account for potential model changes. The effort to switch providers will depend on the model-specific integrations and features the solution uses. In the event that you are locked in due to choices that were optimal at the time, we are ready to assist you in untangling the dependency on one provider if that’s best for your business operations, even if it involves major refactoring of the solution’s code.

When should we use OpenAI/Anthropic APIs versus a self-hosted open-source model like Llama?

The answer depends on what you plan to use the model for. Total costs of these options vary with usage volume, AI model requirements, infrastructure, and the effort needed to run and maintain the system. Typically, managed APIs can prove more cost-effective for small and mid-sized products by helping businesses avoid the fixed infrastructure and maintenance costs of self-hosting. Self-hosting is usually considered when there are specific requirements around deployment, data control, customization, or sufficiently large and predictable workloads. 

Our business analysts can assess your business environment and generative AI plans to help you identify the best approach for your unique case.