Generative AI & LLM Applications

AIGard builds generative AI applications such as copilots and assistants that are grounded in your own data and connected to your systems. We handle retrieval, evaluation, guardrails and deployment, not just the prompt.

The work typically covers:

• Use-case discovery and feasibility
• Retrieval-augmented generation (RAG) over your data
• Model selection and fine-tuning
• Evaluation, hallucination testing and guardrails
• Integration with your products and tools
• Monitoring of cost, quality and safety

Generative AI & LLM Applications is usually the right investment in one or more of these situations:

• Teams lose time searching internal knowledge
• You want an AI assistant inside your product
• Documents need summarizing or drafting at scale
• A prototype works but isn't reliable enough for production

On a 30-minute scoping call we confirm whether it fits your case and recommend a team shape and first milestones.

The right large language model depends on your accuracy, cost, latency and privacy needs. AIGard tests candidate models from providers such as Anthropic, OpenAI and Mistral against your own data before recommending one.

Model selection sits within use-case discovery and feasibility, and can include fine-tuning where appropriate. Testing on your own data ties the recommendation to your use case, and monitoring of cost, quality and safety continues after deployment.

AIGard works to keep generative AI answers accurate by grounding responses in your data through retrieval, building an evaluation set from real questions, and adding guardrails and monitoring before launch.

Evaluation includes hallucination testing, so weaknesses are identified before users encounter them. After launch we monitor cost, quality and safety. This is the work that moves a prototype from functioning to being reliable enough for production.

AIGard designs generative AI solutions so that your data stays within your environment and under your chosen provider's enterprise terms.

Privacy needs are one of the criteria we apply when selecting a model, alongside accuracy, cost and latency. Our engineers work inside your environment under least-privilege access. How a provider treats your data is governed by the enterprise terms of the provider you choose, which we discuss during scoping.

By treating the model as one part of a system. Production work covers data pipelines, evaluation, deployment, monitoring and retraining as well as the model itself.

We agree the success metric first, assess whether your data supports the use case, and build the surrounding engineering so the system keeps performing after launch.

Privacy and security controls for each AI use case are defined up front, and engineers work inside your environment under least-privilege access.

We agree which AI tools and models may access your code and data, including retention and training settings, with human approval for sensitive actions.

For Generative AI & LLM Applications, AIGard engineers typically work with Anthropic, OpenAI, Mistral, Hugging Face, LangChain, LlamaIndex, Pinecone, Weaviate, Vertex AI and Python.

The stack is selected to fit the systems you already run, and engineers are matched to it before they join.

Generative AI & LLM Applications is available through any of AIGard’s three engagement models: staff augmentation, where engineers join your team; a dedicated team that runs the workstream; or software outsourcing, where AIGard delivers a defined project.

The choice depends on how much of the delivery you want to manage, and it can change later under the same agreement.

Engineers typically start within two to four weeks of the scoping call. The exact date depends on the roles, seniority and number of people required.

We confirm a realistic start date in writing before you commit, and you meet the proposed engineers before work begins.

AIGard engineers typically cost 30–50% less than the fully loaded cost of a comparable US-based hire, with no recruiting fees, benefits overhead or insurance costs on your side.

The figure for a specific engagement depends on the model, the roles, the seniority mix and the size of the team. We issue a written proposal after the scoping call, so the commercial terms are agreed before any work starts.

NDAs are signed before any access is given, and engineers work inside your environment under least-privilege access, with permissions limited to their responsibilities.

Access is reviewed as roles change and removed when work ends. Our practices align with ISO 27001, SOC 2, GDPR and HIPAA, with PII masking, encryption and audit logging built into data work. We review your access policies, data-handling needs and vendor requirements during scoping.

You do. Intellectual property assignment is written into every contract, in every engagement model.

That covers source code, designs, documentation, data, models and prompts produced for you. Third-party components and their licenses are identified, so ownership is clear at handover.