Generative AI Development Services
Our generative AI development services build applications that search, summarize, draft, and answer questions over your own documents and data. They connect to the systems you already run, and a person checks the output where a mistake would be costly.
Generative AI application
Answers with sources
RETRIEVAL (RAG)
- Documents
- Policies
- Databases
- ERP
YOUR DOCUMENTS AND DATA
What Is Generative AI Development?
Generative AI development is the work of building software that uses large language models to write answers, summaries, drafts, and structured data from a company's own information. The model is one part. The rest is the system around it, such as retrieval, integrations, and review steps.
A general chat assistant answers from its training. A custom application answers from your documents, records, and rules, and hands its output to the next step in a workflow. Generative AI consulting services pick where that pays off first.
How is generative AI different from agentic AI?
Generative AI creates content, such as an answer, a summary, or a listing description. Agentic AI uses that ability to take steps in other systems, like looking up an order or filing a ticket. Many projects use both, with a person approving the actions.
WHEN YOU NEED IT
Signs You Need Custom Generative AI
Custom generative AI makes sense when off-the-shelf assistants can't see your data, follow your rules, or reach your systems. Watch for these signs.
People hunt for answers by hand
Staff dig through policies, manuals, and shared drives to answer the same questions, or ask the one colleague who knows.
The same content gets written again and again
Teams write listings, summaries, or reports from the same kinds of inputs, and it takes hours every week.
The pilot demoed well and changed nothing
A chatbot impressed in a meeting, but it never reached real data or a real workflow, so nobody uses it.
Every vendor sells its own AI add-on
Each SaaS product offers its own AI layer at its own price, and none can see the data in the others.
Sensitive data keeps you off public tools
PII, health, or financial records can't go into a personal chat account, so teams avoid AI or use it unapproved.
Answers you can't trace to a source
An AI tool gives confident answers with no citation, so nobody can check them before acting on them.
OUR APPROACH
How We Build Generative AI for Production
Most generative AI pilots stall because nothing connects the demo to real data or real work. We build that connection around the model, with review steps. Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
Demo-first pilot
- One giant prompt
- No access to your data
- Answers with no sources
- Another portal to log into
Production build
Grounded in your data
Retrieval-augmented generation (RAG) pulls the relevant passages from your documents and records, so answers come from your sources and cite them.
Small prompts, chained steps
Models get less accurate as prompts grow. We split the work into smaller steps, such as reading a photo and then writing the listing, and test each one.
Checked before anyone relies on it
Models can give a different answer to the same question. We add verification steps, such as a second agent that checks each answer, and human review where mistakes are costly.
Delivered where people work
Output lands in the system your team already uses, not another chat window.
Which model should we use?
The one that fits the task. We work with frontier models such as Claude, GPT, and Gemini, and with local or smaller models where privacy, latency, or cost calls for them. Leading models are close in raw ability, so the system around them often matters more.
APPROACHES
Generative AI Approaches and When Each Fits
Most generative AI applications combine a few common approaches. Our LLM development services start with the simplest one that works on your content and add pieces only when the use case needs them.
| APPROACH | WHAT IT MEANS | WHEN IT FITS |
|---|---|---|
| Prompting | Send instructions and context to a hosted model | Drafting, summarizing, or classifying, where the input fits in one request |
| RAG | Retrieve passages from your documents or data, then answer from them | Answers must come from your own content and cite it |
| Chained steps | Split one job into smaller model calls, each with its own check | One large prompt gives uneven results |
| Fine-tuning | Train an existing model further on your own examples | Prompts and retrieval can't get the format or tone right |
| Local models | Run a smaller model inside your own environment | The task is narrow, or the data can't leave your environment |
WHAT WE BUILD
Generative AI Applications We Build
We build custom AI solutions that read, write, and answer from your own content, inside the systems your teams already use.
Q&A over policies and documents
For a healthcare support services company, we built an AI answer hub on Amazon Bedrock Knowledge Bases in its employee portal. One agent answers, and a second agent verifies the answer.
Search with citations
For a national veterinary management group, we added AI search to a document hub that hundreds of clinics use, with a chat assistant that cites the documents behind each answer.
Content written from images
For a PropTech startup, a model on Amazon Bedrock reads property photos and writes the listing description. The proof of concept grew into a production platform.
Document checks for a human reviewer
For a national direct lender for business-purpose residential real estate, a language model compares tax returns with tax transcripts and writes a report. A human reviewer decides.
Structured data from long documents
For a healthcare technology company, a pipeline on Amazon Bedrock and AWS Lambda extracts medications, dosages, and physician notes from Epic medical records into standardized JSON. See AI & Business Process Automation.
Personalized coaching content
For a national residential flooring retailer, we built a coaching engine on AWS. A language model writes coaching for each sales rep from sales and outcome data, and HeyGen turns it into avatar video.
HOW IT WORKS
How a Generative AI Project Runs
A generative AI project runs in five steps, from one use case to an application in daily use.
STEP 1
Pick one use case
Start with one problem, often through an AI readiness assessment, and check that it pays for itself if it works as planned.
STEP 2
Prove it on your content
A proof of concept runs on a sample of your real documents and data, so you see answer quality before committing to a build.
STEP 3
Build the system around it
We add retrieval, permissions, and verification steps, and connect the application to your systems through APIs and MCP.
STEP 4
Launch where people work
The application goes live in the tools your team already uses, with a person reviewing output where a mistake would be costly.
STEP 5
Expand
Once one use case runs well, the next comes faster because it reuses the retrieval and connectors.
Testing quality before launch
Generative AI can sound right and still be wrong, so it needs testing on real questions before people rely on it. Verification steps check answers against their sources, and recurring errors go back into the prompts. A model is good, not perfect, and we design for that.
What your team gets at handoff
You own everything we build, including the application, its prompts, and its connectors. Services we can provide at handoff include knowledge transfer sessions and documentation of how the application works and where its review rules live. Ongoing support is also available.
RESULTS
Generative AI Results
A PropTech startup cut listing description writing from more than 30 minutes to under 5, and a healthcare support services company got AI answers that a second agent verifies.
<10 min
Per listing, down from up to a full day
Real Estate
AI Listing Creation for a Real Estate Client
Challenge: Realtors spent hours entering MLS listings by hand on outdated platforms.
Result: An Amazon Bedrock platform and a Chrome extension cut listing creation from up to a full day to under 10 minutes.
Read the real estate case study2x+
Email content output with fine-tuned AI
E-commerce
Generative AI Email Content for Wine Access
Challenge: Hand-written email campaigns drove over half of revenue, and writing them slowed every new winery launch.
Result: Fine-tuned models on Amazon SageMaker and Bedrock beat every test group, some days by more than 50% in revenue, and more than doubled content output.
Read the Wine Access case study"JBS feels more like an extension of our internal team than a traditional vendor relationship."
Senior IT Leader, AmeriVet Partners
TECHNOLOGY
How Retrieval-Augmented Generation Works
Retrieval-augmented generation finds the passages in your content that relate to a question, then has the model answer from those passages and cite them. Our RAG development services cover the whole chain, from indexing your content to checking the answers.
We choose technology for the job. Platforms we have built generative AI on include Amazon Bedrock and Bedrock Knowledge Bases, Azure OpenAI Service, LangChain, and AWS Lambda. LLM integration is often one API call from a system you already run, so existing BI and data teams can usually maintain it.
RETRIEVE
Your documents
Retrieval
Your data
ANSWER AND CHECK
Answer
Cites sources
Check
Second agent
Review
A person
COST AND TIMELINE
What Drives Generative AI Cost and Timeline
Cost and timeline depend mostly on how much content the application draws from, how many systems it connects to, and how much review its output needs.
- 01Volume and variety of content
- 02Systems the application connects to
- 03Accuracy and review requirements
- 04Security and data handling rules
- 05Number of users and languages
- 06Model usage and token costs
How the timeline is set
Timelines depend on scope, and the assessment or proof of concept sets them. As reference points, a PropTech startup's proof of concept took about 6 to 8 weeks, and a healthcare support services company's employee portal with AI answers took about 16 to 20 weeks to build at first.
How cost is set
We don't quote a number before we understand the work. The proof of concept shows real token usage and review effort, and each stage is estimated from those numbers. Token costs stay down when prompts are small and models run only when an event calls for it. If a build won't pay for itself, we'll say so.
ENGAGEMENT MODELS
Ways to Work With Us
Each model stands alone or leads into the next. Compare all the ways to work with us.
Assessment & Strategy
For companies that need clarity before committing to a build.
Start here if you're not sure what to change first. We review the system component by component, weigh difficulty against business impact, and recommend a path for each part, including what to leave alone. You get a roadmap you can act on with us or on your own.
- Technical assessment
- Architecture review
- AI opportunity mapping
- Modernization roadmap
- Prioritized recommendations
Best when: You need to understand the opportunity, risk, architecture, or roadmap before investing in a larger build.
Projects
For companies ready to build or modernize a defined system.
For a defined scope, senior engineers lead delivery from architecture through release. We work in stages, write tests for core components, and ship through CI/CD. You own everything we build, plus knowledge transfer and documentation tailored to your team.
- Modernization projects
- Custom applications
- Cloud platforms
- Data systems
- Integrations
- AI agents
Best when: You have a clear business problem, workflow, or platform that needs to be built, modernized, or connected.
Teaming / Managed Delivery
For companies that need experienced delivery capacity.
When you need more than a single project, services we can provide include architectural guidance for your existing team, hands-on development, QA, and ongoing support. Senior US-based engineers lead our builds, and nearshore support is an option for ongoing work.
- Senior-led teams
- Embedded engineering
- Ongoing development
- Knowledge transfer
- Flexible support
Best when: You need reliable engineering capacity, technical leadership, or ongoing delivery support without adding unnecessary management overhead.
Project stalled with another vendor? Software Project Rescue
WHY JBS DEV
How to Choose a Generative AI Consulting Company
Good generative AI consulting ends in software you can run, and sometimes in the answer that AI isn't the right fix. Ask these five questions.
- Have you taken generative AI past the demo?
- Yes. Our work includes an employee answer hub, a real estate listing platform, and tax document checks in a lender's underwriting system.
- Will it answer from our data?
- Yes. We build retrieval over your documents and records, and answers cite their sources so your team can check them.
- Who owns the prompts and the data?
- You do. Where we can, we build on your own model accounts and data stores, and you own everything we build.
- What happens when the model is wrong?
- A check catches it. Verification steps test answers against their sources, a person reviews output where mistakes are costly, and recurring errors go back into the prompts.
- Who does the work?
- Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
JBS Dev is a senior-led custom software development company, founded in 1999, that builds, modernizes, and integrates enterprise systems around the technology clients already own.
FAQ
Generative AI Development Services FAQs
What do generative AI development services include?
Generative AI development services cover choosing a use case, picking models, building retrieval over your content, testing prompts, connecting the application to your systems, and setting review steps. Ours end with an application in daily use, connected to the systems you already run.
What is the difference between generative AI and machine learning?
Generative AI creates content, such as answers, summaries, and drafts. Traditional machine learning predicts or classifies, such as forecasting demand or grouping customers, and it depends heavily on the quality and volume of your data. Language models are more forgiving. Many systems use both. See machine learning.
What is RAG, and do you offer RAG as a service?
RAG, or retrieval-augmented generation, finds the passages in your content that relate to a question and has the model answer from them, with sources. We build RAG systems you own instead of renting one. Where we can, the index, the prompts, and the data live in your own cloud account, under your control.
Do we need clean data before we start?
No. Language models handle messy, inconsistent content better than most teams expect, so perfect data is rarely the bar. The content a use case needs must exist and be reachable. The proof of concept shows whether your data is good enough, and what to fix first if it isn't.
How do you keep generative AI from making things up?
We limit what it answers from and check what it says. Retrieval grounds answers in your documents, answers cite their sources, and verification steps catch errors. A person reviews output where mistakes are costly.
Does our data leave our environment?
Not outside your control. Applications run in your environment, and where a hosted model is used, enterprise agreements bar the provider from training on or retaining your data. We apply the same controls you use for email: company licenses only, no personal accounts, and access limited to what each application needs. See AI governance and security.
How much does generative AI development cost?
It depends on the volume of content, the systems involved, the review steps, security rules, users, and token usage. We don't quote a number before we understand the work. The proof of concept measures real usage and review effort, and the estimate for each stage comes from those numbers.
How long does it take to build a generative AI application?
It depends on scope, and the proof of concept sets the timeline. As one reference point, a used-car retailer's generative AI photo pipeline was a one-year project for a team of four developers.
