GODELIAN

Service — AI development

AI development, by someone who actually builds AI.

AI development means designing and shipping software with real artificial-intelligence capability — LLM features, retrieval-augmented generation (RAG), document analysis, and generative AI — wired into a product properly rather than bolted on as a wrapper. Godelian is led by Andrew, a founder and published AI researcher (Nature Scientific Reports) who has built AI systems for 15 years. We build custom and generative AI features server-side, logged, and reviewable, on Next.js and Supabase — typically for $15–30k in weeks — with deterministic code where correctness matters and AI where judgment genuinely helps.

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100%
Job Success Score on Upwork
Top 1%
Expert-Vetted on Upwork
5.0★
Average rating
$1.5M
VC funding raised

Most 'AI development' is a thin wrapper around someone else's API with a prompt and a hope. That ships fast and breaks quietly: no evaluation, no logging, no idea whether it's right. Real AI development treats the model as one component in a system you can reason about — grounded in your data, measured against real cases, and safe to put in front of customers.

Godelian builds it that way because the founder has actually done the science. Fifteen years building AI systems and published research (Nature Scientific Reports) means the judgment to know where AI belongs — extraction, drafting, classification, matching — and where it absolutely does not, like anything involving money, which stays deterministic code.

Every AI feature ships server-side (keys and prompts never exposed to the browser), logged so you can audit what the model did, and evaluated against real inputs before it goes live. RAG grounds answers in your content; a human approves anything consequential. It runs on the same production stack as the rest of your product — Next.js, TypeScript, Supabase — so the AI isn't a separate, fragile bolt-on.

What clients say

He's not just a strong builder; he's a founder who's developed real products. He didn't just improve the output — he pushed my thinking about the product itself. Investor-ready results.

Founder, AI product for Fortune 500 investor relations — 5.0★ Upwork review

Relevant work

Ratify

AI contract review built as a multi-LLM consensus pipeline with a human review layer and immutable, legally valid outputs — AI where it drafts, humans where it counts.

eCase

AI correspondence management used across UK government — the full intake-to-response journey, with AI assisting drafting inside an accountable, auditable workflow.

Darkmist

Insurance underwriting from document intake to risk decision — OCR and AI extraction feeding a structured, reviewable risk-scoring pipeline.

AI development — questions

What does an AI development company do?

An AI development company designs and builds software that uses artificial intelligence — LLM features, RAG (retrieval-augmented generation), document analysis, generative AI, and AI agents — as part of a real product. Done properly it means grounding the model in your data, handling it server-side, logging every call, and evaluating outputs, not just calling an API behind a chat box.

How much does AI development cost?

A custom AI feature or AI-powered MVP at Godelian typically costs $15–30k and ships in weeks. Cost depends on how much has to be grounded in your data, how many integrations are involved, and the evaluation and guardrails required — but AI has collapsed the build cost, so a real, working AI product no longer needs a six-figure budget.

What's the difference between real AI development and an AI wrapper?

An AI wrapper is a prompt sent to a public model with no grounding, evaluation, or logging — quick to demo, unreliable in production. Real AI development grounds the model in your data (RAG), keeps deterministic logic for anything that must be correct, evaluates outputs against real cases, logs everything for audit, and keeps a human in the loop where the stakes are high.

Which AI models and techniques do you use?

The latest frontier LLMs, plus retrieval-augmented generation (RAG) for grounding, structured output and tool use for reliability, evaluation harnesses to measure quality, and fine-tuning only when it earns its keep. The model choice follows the task; the engineering around it is what makes it dependable.

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