Content generation systems
Drafting engines for product descriptions, reports, proposals, and marketing copy that follow your brand rules and route output through human approval where it matters.
Generative AI
Generative AI Development for Real Business Workflows
Generative AI is easy to demo and hard to trust. We build generative systems for writing, search, documents, and language work that are grounded in your data, measured for accuracy, and safe to put in front of customers.
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Generative AI
Generative AI solutions we build
Practical generative systems that take real work off your team, not novelty features.
Knowledge assistants and search
Assistants that answer questions from your documents, policies, and wikis with sources attached, so staff stop hunting through folders for answers.
Document intelligence
Summarization, extraction, and classification for contracts, invoices, forms, and records, turning unstructured paperwork into structured data your systems can use.
Natural language processing
Sentiment analysis, entity extraction, translation, and text classification tuned to your domain vocabulary and measured against labeled examples.
Image and media generation
Controlled image generation and editing pipelines for product visuals and creative variants, with style rules and review steps built in.
Generative AI integration
Generative features added to your existing product or internal tools through clean APIs, with cost controls and usage monitoring from day one.
Seen a generative AI demo you want to make real?
Share the use case and a sample of your content. We will tell you what it takes to make it accurate, safe, and affordable, with a fixed USD quote.
Start a ProjectHow it works
How we build generative AI systems
- 01
Define
We agree the task, the users, and what a good output looks like in measurable terms.
- 02
Prepare the data
Source content is gathered, cleaned, and structured for retrieval or tuning.
- 03
Prototype
A working prototype on your data tests quality and cost before the full build.
- 04
Engineer guardrails
Grounding, content filters, and human review points are designed in, not added later.
- 05
Evaluate
Outputs are scored against test sets for accuracy, tone, and failure cases.
- 06
Launch and monitor
The system ships with output monitoring, cost tracking, and a feedback loop.
Why AIoptimix
Why build generative AI with AIoptimix
Generative AI we run ourselves
Our own publishing engine researches, drafts, reviews, and illustrates content with AI. The guardrails we build for you are the ones we rely on for our own output.
Accuracy is measured, not assumed
Every system is scored against test sets before launch and monitored after it, so quality problems show up on a dashboard instead of in a customer complaint.
Costs designed in
Model choice, caching, and routing are planned around your real usage, so the monthly bill is predictable before you scale.
Your data stays yours
Prompts, tuned models, pipelines, and generated assets belong to you in writing, with self-hosted options where data cannot leave your infrastructure.
Tools and stack
The generative AI stack we work in
Models
- Frontier model APIs
- Llama
- Mistral
- Whisper
- Open-weight models
Frameworks
- LangChain
- LlamaIndex
- Hugging Face
- Sentence Transformers
- spaCy
Retrieval
- PostgreSQL
- pgvector
- Pinecone
- Qdrant
- Elasticsearch
Delivery
- Python
- FastAPI
- Next.js
- Docker
- AWS
- Azure
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 is generative AI development?
It is the work of turning generative models into dependable business software: choosing the model, grounding it in your content, designing prompts and guardrails, measuring output quality, and integrating it into the tools people already use.
How is this different from LLM development?
LLM development focuses on the language model layer itself, such as fine-tuning and retrieval architecture. Generative AI development covers the whole application built on top: content workflows, document processing, search, media generation, and the review steps around them.
How do you stop generative AI from making things up?
By grounding answers in your own sources, requiring citations where facts matter, testing against known questions, and adding human review for high-stakes outputs. Errors cannot be removed entirely, but they can be measured and kept low.
Is our data used to train public models?
No. We configure providers so your data is not used for training, and for sensitive work we can deploy open-weight models inside your own infrastructure.
What does a generative AI project cost?
It depends on scope, data preparation, and usage volume. A focused prototype costs far less than a full production system, and every engagement starts with a written scope and a fixed USD quote.
Put generative AI to useful work
Tell us the task you want generative AI to take on. You will get a candid view of what is achievable and what it would take.
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