AI & Generative AI Development Services
AI features built around a real workflow, not a demo.
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AI development, in a business context, means building features — chatbots, document processing, automated drafting — powered by large language models connected to your own data. DuCodes builds these with explicit fallback behavior for when the model is uncertain, rather than shipping something that guesses confidently.
Generative AI is genuinely useful for a narrower set of problems than the current hype suggests — and genuinely powerful for the problems it fits. We build AI-powered features (chat interfaces, document processing, internal knowledge search, automated drafting) where there's a clear, measurable task the model is doing, and a fallback for when it gets something wrong.
A lot of our AI work is integration, not model training: connecting an LLM API to your actual data and workflow through retrieval, function calling, and careful prompt design, with proper guardrails around cost, latency, and failure modes.
Why work with DuCodes on this
We scope for the failure case first
Before building, we define what happens when the model is wrong or unsure — a chatbot that confidently guesses is worse than one that says "let me connect you to a person."
Grounded in your data
Where accuracy matters, we use retrieval-augmented generation against your actual documents/database rather than relying on a model's general knowledge.
Cost and latency are part of the design
LLM API calls have a real, ongoing cost — we design around that from the start instead of discovering it after launch.
Not every problem needs AI
Part of our job is telling you when a simpler rules-based system or a better-designed form would solve the problem more reliably and cheaply than a model.
How we work
Problem definition
We identify the specific task, the acceptable error rate, and what a human fallback looks like.
Data & integration mapping
What data does the model need access to, and how does it get there securely?
Prototype
A working proof-of-concept against real (or representative) data before committing to full build-out.
Build & guardrails
Production integration with rate limiting, monitoring, and fallback behavior for API failures or low-confidence responses.
Evaluation & iteration
AI features need ongoing evaluation against real usage — prompts and retrieval get tuned after launch, not just before it.
Technology we use
Frequently asked questions
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