# AI Solutions & Agents

> Retrieval-augmented agents on your own data, with guardrails, evaluation and observability built in, so your AI ships without hallucinating or leaking.

**Provider:** Oxwyn Studio (https://oxwynstudio.com)
**Page:** https://oxwynstudio.com/services/ai-solutions

## Summary

AI solutions at Oxwyn Studio means taking the idea you cannot stop thinking about and turning it into a real, grounded AI product: retrieval-augmented agents that answer from your own data, wired through the Vercel AI SDK and AI Gateway, with a prompt firewall, output validation, evaluation harness and cost observability built in from the first commit, so it survives real users instead of dying after the demo.

## What we can build - idea to product

- **Support agent.** Dream: “I wish customers could just ask our docs instead of emailing support.” Ship: A support agent grounded on your docs and tickets, with citations and safe fallback to a human.
- **Research copilot.** Dream: “My team spends hours reading reports to find three numbers.” Ship: A research copilot that retrieves, summarises and cites across your document corpus on demand.
- **Document extraction.** Dream: “We get thousands of invoices and someone types them in by hand.” Ship: A structured extraction pipeline that turns messy PDFs into validated, typed data with confidence scores.
- **Action agent.** Dream: “I want an assistant that can actually do things, not just chat.” Ship: A tool-using agent that books, updates and queries your real systems through guarded function calls.
- **Semantic search.** Dream: “Our users never find the right product in our huge catalogue.” Ship: A semantic search and recommendation engine over your catalogue using vector embeddings.
- **Napkin to product.** Dream: “I sketched an idea on a napkin. Can it even be built?” Ship: A blueprint, a working prototype, then a hardened production build. We start from the napkin.

## Why you need this

- **It worked in a notebook, it died in production** The demo dazzled, then real users arrived. Hallucinations, timeouts and runaway token costs turned a magical prototype into an unreliable feature nobody trusts.
- **Your knowledge is trapped in PDFs and old systems** The model has nothing real to stand on, so it confidently invents answers about your own product, pricing and policies. Without grounding, fluency is just a better-spoken guess.
- **There is no wall between user input and the model** Prompt injection, jailbreaks and unbounded spend stop being theory the moment you go live. Without guardrails, one crafted message becomes an incident, a leak or a bill.

## The method - build path

01. **Frame the outcome** - Define the job to be done, the success metric and the failure modes before any model is chosen, so we build a product, not a tech demo. (Discovery, Success metric)
02. **Ground the data** - Ingest, chunk and embed your knowledge into a vector store so answers come from your truth, not the model's imagination. (Ingestion, pgvector, RAG)
03. **Wire the agent** - Build the retrieval, tools and orchestration through the Vercel AI SDK with streaming, function calls and model routing. (AI SDK, Tools, Routing)
04. **Evaluate and harden** - Add a prompt firewall, output validation and an eval harness that scores accuracy and hallucination before anything ships. (Evals, Guardrails, Firewall)
05. **Ship and observe** - Deploy with cost caps, rate limits, tracing and dashboards so you see every call, every cost and every failure in production. (Observability, Cost caps, Tracing)

## What's included

- Grounded retrieval (RAG) over your own documents and data
- Agent orchestration with tools, streaming and model routing
- Prompt firewall and input sanitisation against injection
- Output validation and structured, typed responses
- Evaluation harness scoring accuracy and hallucination rate
- Cost caps, rate limiting and per-request budget controls
- Observability: tracing, token spend and latency dashboards
- A codebase you own, on the Vercel AI SDK and AI Gateway

## AI capabilities - six areas

### 01. RAG and retrieval
Ground answers in your own knowledge with chunking, embeddings and vector search, so responses are sourced and citable.
Techniques: pgvector, Embeddings, Reranking.

### 02. AI agents and tools
Agents that take action through guarded function calls, with orchestration, streaming and multi-step reasoning.
Techniques: AI SDK, Tool calls, Orchestration.

### 03. Structured extraction
Turn messy documents into validated, typed data with schemas and confidence scoring you can trust downstream.
Techniques: Schemas, Validation, Typed output.

### 04. Guardrails and safety
A prompt firewall, content filtering and output validation that stop injection, jailbreaks and unsafe responses.
Techniques: Firewall, Filtering, Validation.

### 05. Evaluation and observability
Eval suites that score quality plus tracing, token spend and latency dashboards so you see everything in production.
Techniques: Evals, Tracing, Dashboards.

### 06. Model routing
Route each task to the right model through the AI Gateway, balancing quality, latency and cost with caps and fallbacks.
Techniques: AI Gateway, Routing, Cost caps.

## Stack & tooling

Vercel AI SDK, AI Gateway, PostgreSQL + pgvector, Next.js, TypeScript, Evaluation tooling.

## Key concept - grounded AI (RAG)

Grounded AI (retrieval-augmented generation) answers from your own data rather than the model's memory. We ingest your documents into a vector store, retrieve the most relevant passages per query, and have the model answer from those with citations. A prompt firewall sanitises input and blocks injection, and output validation checks responses before they reach users. This is the difference between a confident guess and a sourced, verifiable answer.

## FAQ

### What does an AI solution from Oxwyn Studio actually include?

A real, deployed AI product: retrieval grounded on your data, an agent or assistant wired through the Vercel AI SDK, a prompt firewall and output validation, an evaluation harness, and cost and observability controls. You receive a codebase you own, not a locked demo.

### How do you stop the AI from hallucinating?

Two ways. First, grounding: answers are retrieved from your own documents and returned with citations, so the model reasons over real text instead of inventing it. Second, an evaluation harness and output validation that score and catch unsupported or malformed answers before they reach users.

### Can you turn my idea into a product even if it is just a concept?

Yes. That is the heart of this service. We start from the idea, frame the outcome and failure modes, build a grounded prototype, then harden it into a production build. The napkin sketch is a perfectly good starting point.

### What is RAG and why does it matter?

Retrieval-augmented generation. Instead of relying on what a model memorised, we ingest your knowledge into a vector database, retrieve the most relevant passages for each question, and have the model answer from those. It is the difference between a confident guess and a sourced answer.

### How do you protect against prompt injection and abuse?

Every request passes through a prompt firewall that sanitises input, detects injection and jailbreak patterns, and enforces rate limits and per-request budget caps. Outputs are validated against a schema before use, so a crafted message cannot leak data or trigger unsafe actions.

### Which models and providers do you use?

We build on the Vercel AI SDK and AI Gateway, which let us route each task to the right model, OpenAI, Anthropic, Google and others, and switch or fall back without rewriting the app. You are never locked to a single provider.

### How do you control AI running costs?

Model routing sends cheap tasks to cheap models, caching avoids repeat work, and hard cost caps plus rate limits stop runaway spend. Observability dashboards show token spend per feature so you can see and tune cost continuously.

### Do we own the AI product and its data?

Yes. You own the repository, the vector store and the data inside it. There is no platform lock-in, and your proprietary knowledge stays in infrastructure you control.

## Keywords

AI solutions, AI agent development, RAG development, retrieval augmented generation, custom AI product, LLM application development, AI chatbot development, grounded AI, AI guardrails, AI evaluation, vector search, production AI, AI consulting, Vercel AI SDK

## Next step

Bring us your idea: https://oxwynstudio.com/contact