Service
Generative AI development services
Generative AI development adds large language model capabilities, such as drafting, summarizing and answering questions, to your software. Stackvala builds these features on your own data, with evaluation and guardrails so results can be trusted.
Generative AI development for real business use
Generative AI development is the work of building applications on large language models (LLMs) and related models that can write, summarize, translate, reason over text and generate code or images. For businesses, the value is rarely the model on its own. It is the system around it: your documents and data, your permissions, your workflows and a way to check the results.
Stackvala builds generative AI solutions for companies in the USA that want practical gains such as faster support, quicker research, cleaner document handling and smarter internal search. We design for accuracy, privacy and cost control from the start, and we keep the architecture flexible so you are never tied to a single model vendor. The aim is a dependable tool that your staff trust, not a demo that impresses once.
- Copilots that draft emails, reports, proposals and responses
- Summarization of long documents, calls and tickets
- Question answering over your internal knowledge base
- Data extraction from invoices, contracts and forms
- Content generation with brand and compliance guardrails
- Code assistance and internal developer tools
Key points
Retrieval-augmented generation: grounding answers in your data
A general language model knows nothing about your private policies, products or customers, and it may confidently invent details. Retrieval-augmented generation, or RAG, solves this by searching your approved content first and giving the model only the relevant passages to answer from. The result is more accurate, up to date and traceable.
RAG is often a better first step than fine-tuning, because content updates take effect immediately and you can show where each answer came from.
Documents are cleaned, split and indexed, with access rules preserved.
Semantic and keyword search find the best passages for each question.
The model answers using only the retrieved context, citing sources.
Test questions measure accuracy, completeness and tone.
User ratings highlight gaps in content or retrieval.
RAG, fine-tuning or prompting: choosing an approach
Different needs call for different techniques. The table shows how we think about the choice.
| Approach | Best for | Strengths | Limitations |
|---|---|---|---|
| Prompt engineering | Simple tasks and quick prototypes | Fast and low cost | Limited control and consistency |
| RAG | Answers based on changing company knowledge | Current, traceable, no model training | Quality depends on content and retrieval |
| Fine-tuning | Consistent style, format or narrow specialist tasks | Reliable behaviour on repeated patterns | Needs good training data and maintenance |
| Agents and tool use | Multi-step tasks that call systems and APIs | Automates workflows end to end | Needs strong guardrails and review |
In detail
Safety, privacy and governance
Business use of generative AI raises fair questions. We answer them with design, not promises.
Providers and settings chosen so your data is not used to train public models, with clear documentation of data flows.
The assistant sees only what the signed-in user is allowed to see.
Content filters, prompt-injection defences and limits on actions the system can take.
Approval steps for outputs that carry legal, financial or safety risk.
Logs of prompts, sources and outputs for review and improvement.
Caching, model routing and usage limits keep bills predictable.
Our generative AI development process
We prove value quickly and invest further only when results justify it.
Identify use cases
Rank by business value, data readiness and risk.
Prepare data
Collect, clean and secure the content the system will use.
Prototype
A narrow working version tested on real examples.
Evaluate
Score accuracy, cost and speed against a baseline.
Integrate and launch
Connect to your tools, secure access and train users.
Monitor and improve
Track quality, costs and feedback as content and models change.
Technology we work with
The generative AI landscape moves quickly, so we separate your business logic from the model provider.
| Component | Examples |
|---|---|
| Models | Hosted LLM APIs, open-weight models, embedding models |
| Retrieval | Vector databases, hybrid search, re-ranking |
| Orchestration | Python and TypeScript services, workflow engines |
| Front end | Chat interfaces, side panels and embedded widgets |
| Infrastructure | AWS, Azure, Google Cloud, containers, secrets management |
| Evaluation and monitoring | Test sets, tracing, feedback capture, cost dashboards |
These systems sit inside broader applications, so we pair them with custom enterprise software and cloud and DevOps expertise. For conversational interfaces specifically, see AI chatbot development.
Key points
Cost and timeline
Generative AI budgets have a build cost and a running cost, and both should be visible before you commit.
We provide a written estimate that includes expected monthly run costs. See pricing models for engagement options.
Clean, organized content shortens the project significantly.
Model requests and hosting scale with usage.
Connecting to CRM, help desk or document stores needs engineering.
Higher accuracy requires more evaluation and tuning.
Content, prompts and models need regular attention.
Working with Stackvala
Share your goal and get a clear recommendation. No obligation.
We sign before you share any project details.
Payments tied to delivered milestones, with written scope.
Code, designs and documentation are assigned to you.
FAQ
Generative AI Development FAQs
Cannot find your answer? Tell us about your project and a real person will reply within one business day.
Contact StackvalaWhat is generative AI development?
It is building applications that use large language models and related models to generate or transform text, code or images, combined with your data, workflows and safeguards.
What is RAG and do we need it?
Retrieval-augmented generation searches your approved content and gives relevant passages to the model, so answers are grounded and traceable. It is often the best starting point for company knowledge assistants.
Is our data safe when using generative AI?
We design data flows with privacy in mind, select providers and settings that do not train public models on your data, and enforce access control and logging.
Can generative AI be wrong?
Yes. Models can produce incorrect output. We reduce this with grounding, evaluation, guardrails and human review for high-risk cases, and we monitor quality after launch.
How long does it take to build a generative AI solution?
A narrow prototype can be ready in a few weeks once data access is available. Production integration, security and evaluation add further time.
Do we have to use a specific model provider?
No. We keep the model behind a clean interface so you can change providers as quality, cost and policy evolve.
Can generative AI be added to our existing application?
Yes. Many projects embed summaries, search or assistants into an existing web or mobile product.
Start a project
Get a free estimate for Generative AI Development
Tell us what you are building. A real person reads every request and replies within one business day.
- NDA before you share details
- Written scope and price options
- You own the code