AI
AI Platform Development
RedZen builds AI platforms — the full product around a language model, not a demo. That means retrieval grounded in your data, guardrails so it fails safely, and an evaluation harness that measures whether the AI is actually getting better. Cost and delivery date are agreed before work begins.
Grounded in your data
Retrieval (RAG) over your content so answers are accurate, not invented.
Evaluated, not guessed
A measurement harness proves the AI improves rather than just changes.
Fixed price
Cost and delivery date agreed before we start — never hourly.
Why most AI products never ship
A demo that impresses in a meeting but is not reliable enough to put in front of customers.
A model wired to an API with no retrieval, so it invents answers about your business.
No guardrails, so an edge case produces something wrong, unsafe, or embarrassing.
No way to tell whether a change made the AI better or worse — just vibes.
We build the unglamorous engineering that turns a model into a product: retrieval, guardrails, and an evaluation harness — so what you ship is trustworthy, and you can prove it.
What you get
LLM applications
Products built on language models, designed so the AI is a feature that works, not a gimmick.
Retrieval (RAG)
Grounding answers in your own data so responses are accurate and current, not invented.
Guardrails
Validation and limits so the system behaves predictably and fails safely.
Evaluation
Measuring quality so you know the AI is improving, not just changing.
Full product
The interface, accounts, billing and backend around the AI — a product, not a prototype.
Agentic systems
When the AI needs to take actions, not just answer, we build the streaming loop, permission scoping and human-in-the-loop approvals that make it safe.
- A working AI platform, not a demo
- Retrieval grounded in your data
- Guardrails and evaluation in place
- The full product around the model
How we work
Frame
We pin down where AI genuinely helps and where plain software is the better tool.
Prototype
A working slice to prove the approach before the full build.
Build
The platform engineered with retrieval, guardrails and evaluation in place.
Improve
Ongoing tuning against real usage and measured quality.
RAG and grounding — why the AI won’t invent answers
The difference between an AI product you can ship and a liability is grounding. We build retrieval (RAG) over your own data so the model answers from what it retrieves — with the source behind the answer — rather than from its general training. When it cannot find an answer, it says so instead of inventing one.
For the underlying concepts, our explainers on generative AI and the Claude API are the informational companions to this page.
Guardrails and evaluation — engineering AI you can trust
Reliability is not a feature you add at the end; it is the architecture. We build validation and guardrails so the system fails safely on edge cases, and an evaluation harness that scores output quality against a fixed benchmark — so a model or prompt change is a measured decision, not a gamble.
This is the engineering RedZen runs on its own products, and it is the part most teams skip. It is also what makes an AI platform citable and trustworthy rather than a black box.
Common questions
What is AI platform development?
It is building the full product around a language model — retrieval, guardrails, evaluation, interface and backend — so the AI is reliable enough to put in front of real users, rather than a demo.
Which models do you use?
We choose based on the task, cost and privacy needs rather than defaulting to one. We will explain the trade-offs.
Can it use our own data?
Yes. Retrieval (RAG) over your data is usually what makes an AI product genuinely useful and accurate.
How do you stop it making things up?
Grounding in real data, guardrails, and an evaluation harness reduce this. We are honest about what AI can and cannot guarantee.
What is RAG?
Retrieval-augmented generation — the model retrieves relevant pieces of your data and answers from them, rather than from its general training, which keeps answers accurate and current.
Can you build an AI agent that takes actions?
Yes. Agentic systems that act — not just answer — need a streaming loop, permission scoping and human-in-the-loop approvals, which we build in from the start.
What does an AI platform cost?
It depends on scope, so we agree a fixed price after scoping — set before any work begins, never hourly.
Do we own the code?
Yes. The platform, the code and the infrastructure are yours, with no lock-in to us.
AI Platform Development
Tell us what you need and we will come back within one business day with a clear, honest next step — no obligation.