Generative AI That Ships to Production with Measurable Output Quality
We build text, image, code, and multimodal generative AI applications — fine-tuning, RAG, and custom model integration. Quality is measured before launch, not assumed.
Generative AI engineering
GPT-4, DALL-E, Stable Diffusion
100%
code & IP yours from day one
6–10 wks
48h
Avg. Response Time
no surprises, ever
What is generative AI development?
Generative AI development is the engineering of production software systems that use AI foundation models to generate new content — text, images, code, audio, or multimodal combinations — rather than just classifying or predicting from existing data. This includes large language model (LLM) applications, image generation pipelines, AI code generation tools, content automation platforms, and document intelligence systems. CodeShiper builds these systems end to end: use case definition, model selection, fine-tuning when warranted, quality control pipeline, application development, and production deployment — with output quality measured before launch.
Four modalities, one engineering team
We build generation systems across all four major output modalities — text, image, code, and multimodal combinations. Each requires different model selection, evaluation, and quality control approaches.
LLM-powered content, documents & language automation
AI image creation, editing & product visual automation
AI developer tools, code review & engineering automation
Systems that see, read, reason, and generate together
Where generative AI creates real value
The use cases below share two traits: clear measurable output quality and a business case that justifies the engineering investment.
Content Automation Platforms
Generate blog posts, product descriptions, email sequences, ad copy, and social content at scale — with brand guidelines and style constraints enforced programmatically. Humans review edge cases; the AI handles volume.
AI-Powered Search & Discovery
Semantic search that understands user intent rather than matching keywords. Combines embeddings for concept search with generative summaries of results. Relevant for e-commerce, knowledge bases, and enterprise content portals.
Document Intelligence Systems
Extract structured data from unstructured documents — contracts, invoices, medical records, research papers, insurance forms. LLM-powered extraction with confidence scores and human review flagging.
Product Visual Automation
Generate consistent product photography, lifestyle visuals, and marketing assets from product inputs. Replace expensive photo shoots with AI pipelines for e-commerce and D2C brands at scale.
Developer Tooling & Copilots
Internal AI tools that accelerate your engineering team: code review automation, test generation, documentation writing, PR summarization, and codebase Q&A. Built specifically for your stack and conventions.
AI-Native SaaS Features
Add generative AI capabilities to your existing SaaS product — writing assistants, smart autocomplete, AI summarization, automated reporting, and personalized content generation. API-first, integrates into your existing stack.
The model stack we deploy in production
Model selection is driven by your quality, latency, cost, and privacy requirements — we evaluate options against your real use case before committing.
Output quality is measured, not assumed
Generative AI can produce impressive demos and terrible production outputs. We build the evaluation infrastructure before writing the generation code.
Evaluation test sets
We build a set of real test cases from your use case before development. Every release is scored against this set. Quality regression blocks deployment.
Structured output schemas
Outputs are constrained by JSON schemas that define the format, required fields, and validation rules. The model cannot return malformed output.
Automated quality pipelines
Automated scoring of generation quality, hallucination rate, safety compliance, and latency on every release — not just before launch.
Human review workflows
For high-stakes generation (legal, medical, financial), we build human review queues with confidence thresholds and approval workflows.
What we measure
From use case to production generation system
Six stages with measurable milestones. You see real generated output against your test cases at every sprint — not a demo on cherry-picked inputs.
Use Case & Output Quality Definition
1–2 wksDefine the generation task in measurable terms: what is a good output? Bad output? Edge case? Establish the evaluation criteria before writing a line of generation code.
Model Selection & Baseline Evaluation
1–2 wksEvaluate candidate models on your real use case. Prompting vs RAG vs fine-tuning comparison on your data. We choose the approach with the best quality-to-cost ratio for your production volume.
Prompt Engineering & Data Pipeline
1–3 wksBuild the prompt architecture, output schemas, and data ingestion pipeline. For fine-tuning: prepare training data, run training, and evaluate against baseline.
Application Development
SprintsFull application layer — backend API, generation orchestration, quality filtering, frontend UI, auth, and integrations. Sprint demos with real generation output against your test cases.
Quality & Safety Testing
1–2 wksAutomated evaluation pipeline measuring generation quality, safety, consistency, and latency. Content safety classifiers for public-facing applications. Manual review of edge cases and adversarial prompts.
Launch & Continuous Improvement
OngoingProduction deployment with monitoring, quality dashboards, and continuous evaluation. Model updates, prompt improvements, and new generation features on retainer.
Generative AI development cost
Cost depends on the modality, data volume, whether fine-tuning is required, and the application scope.
| Project type | Example scope | Timeline | Indicative cost |
|---|---|---|---|
GenAI Feature Embedded in existing product | Text/image generation feature added to existing product — prompt pipeline, API, quality filter | 5–10 weeks | $20,000–$50,000 |
Standalone GenAI App New product or platform | Full app with generation pipeline, fine-tuning, frontend, auth, quality evaluation dashboard | 3–5 months | $50,000–$120,000 |
Enterprise GenAI Platform Multi-modal + compliance | Multi-modal generation, custom trained models, human review workflows, compliance logging, scale | 5–9 months | $120,000+ |
Fine-tuning requirements, training data preparation, and multi-modal scope are the primary cost drivers. Final pricing follows a free scoping call.
What makes our GenAI work in production
Generative AI that ships and stays accurate takes more than API integration. Here is what our approach delivers.
Quality defined before we build
We define what good and bad output looks like in your specific use case before writing a line of generation code. Evaluation first, generation second.
Fine-tuning when it earns its cost
We run a baseline comparison before committing to a fine-tuning run. Fine-tuning is the right answer for some use cases. It is not the default answer.
Structured outputs, not free text
We constrain generation outputs with schemas. Your downstream systems get predictable data, not free-form text that needs another parsing layer.
Cost-per-generation tracked from day one
We model generation cost at architecture time. You know what each generation call costs before you scale to thousands of daily users.
Senior engineers throughout
The engineers who scope the project build it. No handoff to a junior team. The people who understand your requirements are the ones making architecture decisions.
Continuous evaluation post-launch
Quality dashboards, automated evaluation on every model update, and prompt optimization on retainer. Generation quality needs ongoing attention as models change.
Frequently asked questions
Generative AI development — what it is, what it costs, how long it takes, and how we ensure output quality.
What is generative AI development?
What types of generative AI applications can you build?
How much does generative AI development cost?
How long does a generative AI project take?
When does fine-tuning make sense vs. prompting?
Which generative AI models do you work with?
How do you control output quality in generative AI applications?
Can generative AI integrate with our existing product?
How do you handle data privacy in generative AI applications?
Who owns the generated content and model weights?
Let's Talk
Have a GenAI use case in mind? Let's see if it's the right problem for AI.
Tell us what you want to generate, what quality means to you, and what scale you are targeting. We will scope the project honestly and give you a line-item estimate with no obligation.