LLM orchestration
Language models composed into product features with clean architecture, reliable output handling, and latency users never notice.
Generative AI
Hire Generative AI Developers Who Turn Demos Into Products
Every team has a generative AI demo now. Far fewer have a generative AI product users pay for. The difference is the developer who closes that gap, and that is who we place.
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Generative AI
What our generative AI developers build
Image and multimodal generation
Systems that generate and edit images and mixed-media content with diffusion and vision-language models, engineered for consistency at production volume.
Grounded generation with RAG
Outputs anchored in your own data through retrieval pipelines, so generated content is specific and defensible rather than fluent guesswork.
Agents and workflow automation
Generative models that plan and execute multi-step work inside your product, wrapped in the guardrails autonomy demands.
Domain adaptation
Fine-tuning and instruction design that align generation with your brand, format, and quality bar instead of generic model defaults.
Safety and evaluation frameworks
Output scoring, content filtering, and regression testing that keep generative systems trustworthy as models and prompts evolve.
From prototype to paying users
We build generative features into our own products, so we scope yours from experience. Tell us the idea and get an honest USD quote.
Start a ProjectHow it works
How a generative AI engagement runs
- 01
Pin down the value
What the generation is for, who consumes it, and what quality bar separates useful from embarrassing.
- 02
Meet shipped specialists
Developers with generative features running in real products, shortlisted for your modality and domain.
- 03
Select your engineer
Interviews on your terms, decision entirely yours, replacement free if the fit is wrong.
- 04
Iterate with metrics
Output quality measured from the first milestone, so improvement is visible and regressions get caught.
Why AIoptimix
Why build generative AI with AIoptimix
Products, not prompt enthusiasm
Our engineers have shipped generative features handling real users and real content constraints, including inside our own SaaS platforms. Enthusiasm is free; shipped systems are the credential.
Cost engineering included
Generation costs compound quietly. We architect for sensible model selection, caching, and batching from day one, so success does not arrive with a horrifying invoice.
Safety as a requirement
Filtering, guardrails, and evaluation are built into the definition of done, because one viral bad output can cost more than the whole feature earned.
Tools and stack
Our generative AI stack
Engineering
- Python
- FastAPI
- TypeScript
- Node.js
Generation
- LLM orchestration
- diffusion models
- embeddings
- vector search
Operations
- evaluation pipelines
- AWS
- Docker
Engagement models
Pick the model that fits.
Staff Augmentation
Vetted engineers who join your team, work your hours, and follow your workflow.
Learn more →Dedicated Teams
A complete unit with delivery management that owns your product end to end.
Learn more →Fixed Price
A written scope, a fixed USD quote, and a committed timeline before we start.
Learn more →FAQ
Frequently asked questions.
What does a generative AI developer do that an ML developer does not?
ML developers largely build systems that classify and predict; generative AI developers build systems that create text, images, and other content. The architectures, failure modes, and safety requirements differ enough that we treat them as distinct specializations.
How do you keep generated output on-brand and accurate?
By grounding generation in your data with retrieval, constraining it with structured prompt systems and fine-tuning where justified, and measuring output quality continuously with evaluation pipelines. Accuracy is engineered, never assumed.
When should we hire dedicated generative AI expertise?
When your feature goes past a thin API wrapper: custom agents, multimodal generation, domain adaptation, or reliability and safety stakes your team has not engineered for. The gap between demo and product is where projects usually die.
Can you take over a generative AI prototype our team built?
Yes, and it is a common starting point. We audit output quality, cost per generation, and failure handling, then productionize the layers that need it, keeping what your team got right.
Make your generative idea real
Tell us what your product should create and for whom, and we will scope the path from demo to launch.
Start a Project