Generative AI · LLM Application Development
Generative AI Development Services
Generative AI development is building software that uses large language models to write, summarize, extract and reason over your content. Aaga builds production LLM applications, from RAG knowledge assistants to in-product copilots and document AI, and picks the right model for each job, whether that is Claude, GPT, Gemini or an open-source model you host.
- Turn documents, tickets and data into answers your team can trust
- Add a copilot to your product or internal tools without rebuilding them
- Choose models on quality, cost, privacy and latency, not hype
- Ship with evaluation, monitoring and spend limits from day one
- Claude, GPT, Gemini, Llama and more
- Model-agnostic
- Prove quality on your data before scaling
- Pilot first
- Reply to your inquiry within one business day
- 1 day
Generative AI Solutions We Build
Most value from generative AI comes from a few proven patterns, applied to your own data and workflows.
RAG Knowledge Assistants
Answer questions from your policies, manuals, contracts and wikis, with citations to the source so people can check every answer.
In-Product Copilots
Add drafting, summarizing, search and smart suggestions inside your SaaS product, CRM or internal tools.
Document AI
Extract fields from invoices, forms, contracts, lab reports and IDs into structured data, with confidence scores and review queues.
Content Generation Pipelines
Generate product descriptions, reports, emails and marketing copy at scale, in your brand voice and with human review.
Semantic Search and Summaries
Search by meaning across documents, tickets and call transcripts, and summarize long threads into decisions and actions.
Fine-Tuned and Custom Models
Fine-tune open-source or hosted models when prompting and RAG are not enough, for tone, format or domain accuracy.
From Idea to a Production LLM App
Use Case and Data Review
We confirm the problem, the users, the data available and how success will be measured.
Prototype on Your Data
A working prototype on a sample of your real documents, compared across two or three candidate models.
Build the Evaluation Set
We collect real questions and expected answers with your team, and score every version against them.
Harden for Production
Access control, PII handling, prompt-injection defenses, caching, fallbacks and spend limits.
Launch and Monitor
Release to users with feedback buttons, quality dashboards and cost tracking, then improve each release.
How Do We Keep Generative AI Accurate, Secure and Affordable?
Generative AI is reliable when it is grounded, tested and watched. Aaga builds these controls into every project:
- Grounding. Answers come from your retrieved content, with citations, and the model is instructed to say so when the sources don't cover a question.
- Evaluation. Automated tests score accuracy, completeness and tone on your real questions before each release.
- Security. Role-based access to documents, PII redaction, audit logs and defenses against prompt injection.
- Data privacy. Options for enterprise API terms, private cloud deployment or self-hosted open-source models when data can't leave your environment.
- Cost control. Right-sized models per task, prompt caching, response caching, batching and per-user or per-feature spend limits.
Generative AI From an AI-Native Engineering Team
Large IT services firms suit organization-wide AI transformation programs. If you want one valuable LLM application live soon, a lean senior team is often the better fit.
| Aaga | Typical large IT services model | |
|---|---|---|
| First deliverable | A working prototype on your data | Strategy documents and a roadmap |
| Who you work with | Senior AI engineers who write the code | Consultants, then a separate delivery team |
| Model choice | Independent, tested on your data | Often guided by preferred partner platforms |
| Engagement size | Starts with a scoped pilot | Usually sized for larger programs |
| Cost | Affordable, scoped to the use case | Priced for enterprise-scale programs |
Comparison describes typical delivery models, not any specific company.
What Is Generative AI Development?
Generative AI development is the engineering work of turning large language models (LLMs) and other generative models into reliable business software. The model is only one part. A production application also needs data pipelines, retrieval, prompts, an interface, access control, evaluation, monitoring and cost management.
Aaga is an AI-native engineering company. We build generative AI features into new and existing products, and we stay with you after launch to measure and improve them.
Common Generative AI Use Cases for Business
The use cases that pay off most often share one trait: people spend a lot of time reading, writing or searching text. Examples include:
- Support and operations: answer agent questions from manuals and past tickets, summarize long cases, draft replies.
- Sales and marketing: generate proposals, product copy and personalized emails from CRM data.
- Finance and legal: extract terms from contracts, compare documents, summarize reports.
- Healthcare and labs: structure clinical notes or lab reports, with clinicians reviewing outputs.
- Product teams: in-app copilots that help users write, search, analyze or configure.
For more ideas, see our guide to generative AI use cases for business.
How Do You Choose the Right Model?
There is no single best LLM. We shortlist models per task and test them on your examples:
| Option | Strengths | Consider when |
|---|---|---|
| Claude (Anthropic) | Strong reasoning, long documents, careful writing | Complex documents, analysis, agentic tasks |
| GPT (OpenAI) | Broad capability, mature tooling, multimodal | General assistants, mixed text and image tasks |
| Gemini (Google) | Long context, multimodal, Google Cloud integration | Large document sets, video or image input |
| Open-source (Llama and others) | Self-hosting, data control, fine-tuning freedom | Strict data residency, high volume, narrow tasks |
Models change fast. We keep a thin abstraction between your application and the model provider, so you can switch or mix models as prices and quality move.
RAG, Fine-Tuning or Both?
Retrieval-augmented generation (RAG) is the default for business knowledge. We index your documents with suitable chunking and embeddings, use hybrid keyword and vector search, re-rank results and pass only the best passages to the model. Answers cite their sources.
Fine-tuning adjusts a model's behavior with your examples. It is useful for strict output formats, a specific tone or a small model that handles one task cheaply. It does not replace RAG for facts that change. Read more in RAG for business.
What Goes Into a Production LLM Application?
A demo can be built in an afternoon. A system your team relies on every day has more moving parts. On a typical Aaga generative AI project we build:
- Data ingestion. Connectors to SharePoint, Google Drive, Confluence, databases, PDFs and scanned files, with OCR where needed and scheduled re-indexing when content changes.
- Retrieval layer. A vector database or search index, metadata filters and permissions, so each user only retrieves what they are allowed to see.
- Orchestration. Prompt templates, structured outputs in JSON, tool calls where needed, retries and fallbacks to a second model.
- Interface. A web app, an add-on inside your existing product, a Slack or Microsoft Teams bot, or an API for your developers.
- Evaluation and monitoring. Test sets, automated scoring, user feedback, traces of each request and dashboards for quality, latency and spend.
- Security and governance. Single sign-on, role-based access, PII redaction, audit logs and usage policies that match your compliance needs.
Skipping any of these is how promising pilots stall. Planning for them from the start is how they reach real users.
Generative AI vs AI Agents vs Chatbots
Generative AI applications mostly create or understand content: drafts, summaries, extractions and answers. When the software must also take actions across systems, it becomes an agent, covered on our AI agent development page. When the main goal is a conversational channel for customers, see AI chatbot development. For broader model and data work, see AI engineering and machine learning.
Why Aaga for Generative AI
You work directly with senior engineers who build and test the system. We are not tied to one model vendor, so recommendations follow your results. We start with a scoped pilot on your data rather than a long program of workshops. And because Aaga builds on its own platform for permissions, workflows and integrations, the surrounding application comes together faster and costs less than building it from scratch.
Frequently Asked Questions
Generative AI development services cover designing, building and running applications that use large language models or other generative models. That includes RAG assistants, copilots, document extraction, content generation and fine-tuning, plus the evaluation, security and hosting needed to run them in production.
Retrieval-augmented generation (RAG) finds the most relevant passages in your own documents and gives them to the model before it answers. The model then responds from your content, with citations, instead of relying on what it memorized during training. It is the most common way to make LLM answers accurate for a specific business.
Use RAG when the model needs your facts, especially facts that change. Consider fine-tuning when you need a consistent format, tone or specialized behavior that prompting can't achieve, or a smaller, cheaper model for a narrow task. Many projects use RAG first and fine-tune later, if at all.
It depends on the task, language, data sensitivity, latency and budget. We test candidate models such as Claude, GPT, Gemini and open-source models like Llama on your own examples, then recommend the best balance of quality and cost. We design the app so you can switch models later.
We use enterprise API terms that exclude training on your data where the provider offers them, restrict document access by user role, redact sensitive fields and log usage. If data must stay in your environment, we can deploy open-source models in your own cloud.
We route simple tasks to smaller models, cache repeated prompts and answers, keep context lean, batch background jobs and set spend limits. Cost per request is tracked on a dashboard, so spend stays visible.
A prototype on your data can often be ready in a few weeks. Taking it to production, with evaluation, security and integrations, depends on scope. We share a timeline and budget range after a short discovery call.
Build a Generative AI App That Earns Its Keep
Bring one use case and a sample of your data. We'll show you a working prototype and an honest view of quality and cost.
Scope My GenAI Project