section services -- generative ai
Generative AI development services built to sound like you.
Fine-tuned foundation models and custom generative systems for text, image, and media, built on your own data and shipped to production, not left as a prompt-engineering demo.
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Trusted by industry giants, enterprises, and startups
definition
What is generative AI development?
Generative AI development is the practice of adapting or building models that create new content, text, images, audio, or code, rather than predicting or classifying against existing data. Most engagements start from a foundation model, GPT, Claude, or Llama, and fine-tune it to your domain rather than training a model from scratch.
key takeaways
Generative AI creates new output; predictive AI/ML scores or classifies existing data, they solve different problems and often get bundled together by mistake.
The generative AI market is projected to grow from $71.36 billion in 2025 to $890.59 billion by 2032, a 43.4% CAGR (MarketsandMarkets, 2025).
92% of Fortune 500 firms have already adopted generative AI in some form (McKinsey / Menlo Ventures, 2025), the question has moved from whether to adopt it to how well it's implemented.
You don't need to train a model from scratch; you need the right foundation model and a fine-tuning approach matched to your data.
// fine-tuning vs. retrieval-augmented generation
Should you fine-tune a model or use retrieval-augmented generation? Fine-tuning bakes your domain knowledge and voice into the model's weights, it's the right call when the pattern is stable and reused constantly, tone, format, terminology. Retrieval-augmented generation instead feeds the model your documents at query time, which fits fast-changing content, policies, inventory, pricing, better than retraining could ever keep up with. Most production systems end up using both: a fine-tuned model for voice and format, retrieval for facts that change weekly.
capabilities - seven
What our generative AI team does.
Seven capabilities, one goal: generative output that reads like your business, in production.
- 01
Generative AI consulting and strategy
Use-case triage and a build-vs-adapt decision, foundation model or custom architecture, before any engineering starts.
- 02
Foundation model fine-tuning
GPT, Claude, and Llama adapted to your data and voice, so outputs read like your business wrote them, not a generic assistant.
- 03
Custom generative model development
Purpose-built generative systems for teams whose use case outgrows what a fine-tuned foundation model can deliver.
- 04
Text and content generation
Drafting, summarization, and content generation wired into your existing editorial or support workflow, not a separate tool.
- 05
Image and media synthesis
Text-to-image and media generation built on Stable Diffusion and comparable models, for product, marketing, and design teams.
- 06
Retrieval-augmented generation
Your documents and data connected to the model at inference time, so answers stay grounded in your actual content instead of the model's training data alone.
- 07
Model deployment and support
Inference infrastructure, monitoring, and an iteration cadence, so output quality in month twelve matches month one.
method
How does a generative AI engagement actually work?
A Syndell generative AI engagement runs five stages: model selection, data preparation and fine-tuning, solution development, integration and deployment, and ongoing monitoring. Every stage ends with output you can evaluate against your own content.
- 01
Use-case assessment and model selection
// outcome
-> a decision on which foundation model, or whether a custom model, fits your use case and budget
- 02
Data preparation and fine-tuning
// outcome
-> a model adapted to your domain, evaluated against your own content, not a generic benchmark
- 03
Solution development
// outcome
-> the interface, API, and guardrails around the model, a model alone is not a product
- 04
Integration and deployment
// outcome
-> the generative system live inside your existing tools and workflows
- 05
Monitoring and iteration
// outcome
-> ongoing evaluation and prompt or fine-tuning updates as usage and models evolve
models and tools our team builds with
- GPT-4 / OpenAI
- Anthropic Claude
- Meta Llama
- Stable Diffusion
- LangChain
- Whisper
- AWS Bedrock
- Azure AI
- Vector databases
-- generative ai, in numbers --
projected size of the generative AI market by 2032, up from $71.36B in 2025, a 43.4% CAGR
src - MarketsandMarkets, 2025of Fortune 500 firms have adopted generative AI in some form as of 2025
src - McKinsey / Menlo Ventures, 2025growth in enterprise generative AI spending, from $11.5B in 2024 to $37B in 2025
src - Menlo Ventures, 2025the honest part
Why does a generic chatbot answer feel wrong?
An out-of-the-box foundation model is trained on the entire internet, not your business. It doesn't know your terminology, your tone, or your policies, so it hedges, generalizes, or invents details that sound plausible but aren't true for your product. Fine-tuning and retrieval-augmented generation both exist to close that gap.
The fix isn't a longer prompt. A prompt can steer tone for one response, but it can't teach a model facts it was never shown, and it resets every conversation. Fine-tuning changes what the model has learned; retrieval-augmented generation gives it your current documents at the moment it answers. Development teams using generative AI tools already report double-digit velocity gains in production, but only once the model is grounded in real data, not left running on general internet knowledge. Syndell's engineering team runs out of a studio in Ahmedabad, serving businesses across the US, UK, and Australia, so your generative system gets the same grounding discipline regardless of where your team sits.
Development teams using AI tools report 15%+ velocity gains, with 50% of developers using AI coding tools daily, rising to 65% in top-quartile organizations.
Menlo Ventures, 2025
Demand for LLM development expertise has surged 340% since 2023, yet only 23% of companies have moved beyond experimentation to deploying commercial LLM models at scale.
Hostinger / Second Talent, 2025
recent work
Generative systems that ship.
clutch: 5.0/5 - google: 4.9/5
- education
Adaptive learning content platform
generated practice content personalized to each learner
read - case - ecommerce
Retail platform product content
catalog descriptions and merchandising copy generated at scale
read - case - entertainment
Entertainment platform build
content-heavy application serving a growing content library
read - case
"The level of communication and service was exceptional. Thanks to Syndell's efforts, we started getting more leads — the whole process was easy from start to finish."
engagement
Which engagement model fits you?
| Model adaptation sprint | Production generative build | Dedicated AI pod | |
|---|---|---|---|
| Best for | Testing whether a fine-tuned foundation model solves your use case | A defined generative feature, shipped and supported end to end | Ongoing generative AI work across multiple products or features |
| You get | A fine-tuned model and an evaluation report against your own content | Fine-tuned or custom model, API, interface, and guardrails | Embedded engineers who ship and retrain continuously |
| Timeline | Weeks to a validated pilot | Months to a production launch | Monthly, renews |
| Team | AI engineer + prompt specialist | Full generative AI squad | Your composition, our bench |
We skip pricing tables on purpose here - model choice and data volume are what actually drive cost, and a real number after one conversation beats a guess published in advance. Tell us what you're building.
frequently - asked
Five questions,
straight answers.
01What is generative AI development, and how is it different from traditional AI?
Traditional AI and machine learning predict, classify, or score against existing data. Generative AI creates new content, text, images, audio, or code, usually by adapting a foundation model like GPT or Stable Diffusion to your domain. If your goal is new output rather than a prediction, you want generative AI development.
02Which foundation models does Syndell work with?
We build on GPT-4 and the OpenAI family, Anthropic Claude, Meta Llama, and Stable Diffusion for image generation, choosing the model based on your latency, cost, and data-privacy requirements rather than defaulting to one vendor.
03Can a generative AI system be trained on our own proprietary data?
Yes. Fine-tuning and retrieval-augmented generation both let a foundation model reflect your domain and your documents, so outputs read like your business wrote them, not a generic assistant. Which approach fits depends on your data volume and how often it changes.
04How much does generative AI development cost?
It depends on whether you're adapting an existing foundation model or building custom infrastructure around it. We skip published rate cards because scope drives cost more than any standard package would suggest. Book a call and you'll get a real estimate against your actual use case.
05What industries see the strongest results from generative AI?
Content-heavy and document-heavy industries see the fastest returns: marketing, healthcare documentation, financial services, and ecommerce, where generating drafts, summaries, or product content at scale saves the most hours. 92% of Fortune 500 firms have already adopted generative AI in some form.
the practice
Explore our AI and data practice.
Every engagement draws on specialists across the practice. Go deeper on the specific help you need:
- AI consulting
use-case triage and a build-vs-adapt decision before you commit to a generative model
- AI and ML development
predictive models and forecasting, a different discipline from generative content
- AI agent development
autonomous agents that act on your systems, often built on top of a generative model
- Chatbot development
conversational interfaces, frequently the front end for a generative AI system
- AI integration
connecting a generative model to the CRM, ERP, or support desk you already run
Generative AI rarely lives alone. Most engagements pair it with predictive AI and ML development for the analytics side, and with computer vision development when the product needs to understand images as well as generate them.
-- next issue - your generative ai project --
Start your generative AI development project.
Tell us what you want to generate, we'll tell you honestly which model fits and what it takes to ship it grounded in your own data.
