Service

Generative AI Development

LLM-powered products and features that generate text, images, and workflows — safely.

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What This Service Delivers

Generative AI can transform customer experience, content operations, and internal productivity — but production Gen AI is different from a demo. We engineer retrieval, evaluation, cost control, and safety into every generative feature so it performs in production.

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Investment

$20,000 – $100,000

Timeline

8–20 weeks

Expected outcome

Hallucination controls, grounding, and evaluation baked in from day one.

Key risks to plan for

Custom model pre-training · Ongoing optimization (add AI Optimization) · Content creation itself

Production quality

Hallucination controls, grounding, and evaluation baked in from day one.

Cost engineered

Model selection, caching, and prompt optimization keep token costs under control.

Data safety

Your data stays yours — no training on your inputs, no leakage.

Measurable output

Automated quality scoring so you can ship with confidence.

Investment & Timeline

Starting from

$20,000

Typical project range

$20,000 – $100,000

Typical timeline

8–20 weeks

What's included

✓ Prompt/system engineering
✓ RAG pipeline or fine-tuning
✓ Evaluation harness
✓ Production deployment

What's not included

✕ Custom model pre-training
✕ Ongoing optimization (add AI Optimization)
✕ Content creation itself

Factors affecting price

Scale of generation volumeGrounding/data engineering requiredLatency and compliance requirements

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What You Get

Use-case validation and prompt/system design

RAG or fine-tuning as appropriate

Generation APIs with evaluation harness

Cost and latency optimization

Safety and moderation controls

Deployment and monitoring

Technology

PythonTypeScriptLangChainOpenAI / AnthropicVector DBsFastAPI

Industries

E-commerce AI Education AI SaaS AI

Related Services

AI Agent Development RAG Development

Frequently Asked Questions

Do you fine-tune models or use RAG?

It depends on the use case. RAG is usually faster and cheaper for knowledge-heavy features; fine-tuning helps when you need consistent style or domain behavior. We recommend the right approach for each feature.

How do you handle hallucination?

Grounding with retrieval, constrained output formats, automated evaluation, and human review for high-stakes outputs.

What can generative AI do for my business?

Customer support, document generation, content production, code assistance, sales enablement, and internal knowledge work — see our use-case catalog.