RAG AND AI AGENT DEVELOPMENT
Generative AI Development: ChatGPT, RAG and AI Agents
Assistants, knowledge search and tool-using agents, engineered to run safely in production.
We help businesses in the US and Australia build generative AI products on their own data. We design ChatGPT-style assistants, retrieval-augmented generation (RAG) systems and AI agents, with the testing, guardrails and monitoring needed to trust them.
A Great Demo Is Easy. A Reliable AI Product Is Not.
Building a generative AI prototype takes days. Making it accurate, secure and affordable for real users takes careful engineering.
Common problems appear quickly: answers that are confident but wrong, sensitive data showing up where it should not, costs that climb with usage, and agents that take actions nobody approved.
Production-grade generative AI needs grounded answers, strong guardrails, automated evaluation and monitoring after launch.
Hallucinations
Answers sound right but are not grounded.
Data Leakage
Sensitive data reaches the wrong users.
Rising Costs
Model usage costs grow with every user.
Inconsistent Results
Quality drifts as prompts and data change.
Our Generative AI Services
ChatGPT-Style Assistants
Secure assistants for employees and customers, branded for your business and connected to your systems.
RAG and Knowledge Search
Retrieval pipelines with vector search that ground answers in your documents and data, with sources shown.
AI Agents
Tool-using agents that plan and complete multi-step tasks through your APIs, within approved limits, often alongside our intelligent automation work.
AI Integration
Connect language models to your CRM, ERP, ticketing and data platforms through secure APIs.
Multimodal and Voice
Assistants that understand images, documents and speech, and respond by voice where it helps.
Evaluation and LLMOps
Automated testing, monitoring, cost tracking and version control on an AI-ready cloud.
Have a generative AI idea to test?
In a 30-minute call we’ll look at your use case and tell you honestly what it will take to run it in production.
Practical AI, Real Workflows
How We Engineer Generative AI
Our engineers combine the right model with your data, your tools and strong guardrails. Every solution keeps people in the loop for important decisions, as part of our wider AI and generative AI services.
Under the hood, we:
- Design prompts and orchestration for consistent, on-brand answers
- Build retrieval pipelines with vector search over your content
- Fine-tune or adapt models only where it clearly pays off
- Give agents safe, limited access to your tools and APIs
- Test outputs automatically against real questions before release
- Protect against prompt injection and data leakage
We start with a focused prototype, measure quality with real users, then harden it for production.
What We Aim to Improve
What Your Organization Gains
The outcomes we design for from day one, tracked against the measures we agree with you.
Grounded answers
Responses backed by your sources.
Time back for teams
Less searching and drafting.
Safer AI
Guardrails and access controls.
Controlled costs
The right model for each task.
Consistent quality
Evaluated and monitored.
Faster delivery
From prototype to production.
Our Process
A clear path from idea to a generative AI product your users trust, guided by our technology consulting team.
1. Discover
We clarify the use case, users, data sources and success measures.
2. Prototype
We build a working prototype on your data and test it with real users.
3. Evaluate
We measure accuracy, safety and cost, and improve prompts, retrieval and models.
4. Productionize
We add security, integrations, monitoring and guardrails for launch.
5. Operate and Improve
We monitor quality and cost, and keep improving as usage grows.
Ready to move beyond the prototype?
Bring it to a 30-minute call and leave with a clear next step.
Who We Work With
We partner with teams that want generative AI that works reliably for real users. We work with:
- Companies building internal AI assistants and knowledge search
- SaaS and product teams adding AI features
- Customer service and operations teams adopting AI agents
- Organizations moving GenAI pilots into production
Work with us the way that suits you: start with a focused prototype, move to a production build, or add AI and LLM engineers through our staff augmentation and consulting model.
Explore related capabilities: AI workflow automation and conversational AI solutions.
For independent guidance, see the NIST AI Risk Management Framework.
Technology and Platforms
We are model-neutral and choose the best model and tools for each job.
Language Models
OpenAI, Anthropic, Google and open-source models such as Llama and Mistral
AI Platforms
Azure OpenAI, Amazon Bedrock, Google Vertex AI
Vector Search
Pinecone, Weaviate, PostgreSQL with pgvector
Frameworks
LangChain, LlamaIndex and custom orchestration
Evaluation
Automated evaluation, tracing and monitoring tools
Speech and Vision
Speech-to-text, text-to-speech and vision models
Engineering
Python, TypeScript, APIs and modern web frameworks
Security, Privacy and Responsible AI
Generative AI needs strong controls to be trusted. We build security and responsible AI practices into every solution, and your teams make the final call.
- Private deployment: models run inside your cloud or with settings that keep your data out of model training.
- Access control: assistants only retrieve content each user is allowed to see.
- Prompt-injection defenses: inputs, tools and outputs are checked and limited, following guidance such as the OWASP Top 10 for LLM Applications.
- Evaluation: outputs are tested for accuracy, bias and safety before and after launch.
- Audit trails: prompts, responses and agent actions are logged for review.
- Recognized frameworks: governance aligned with the NIST AI Risk Management Framework and ISO/IEC 42001 where relevant.
Frequently Asked Questions
How much does a generative AI project cost?
It depends on the use case, data sources and integrations. Most clients start with a fixed-price prototype, then move to production. We’ll give you a clear estimate after the discovery call.
How long does it take to build a RAG assistant?
A focused prototype on your own data usually comes first, so you can test quality early. We agree the timeline and success measures before work starts.
What is RAG?
Retrieval-augmented generation lets an AI model answer questions using your own documents and data, with sources shown, which makes answers more accurate and easier to verify.
Which language models do you use?
We are model-neutral and compare models from OpenAI, Anthropic, Google and the open-source community, choosing the best fit for quality, cost, speed and data needs.
Is our data used to train public models?
No. We use private deployments and vendor settings that keep your data under your control.
How do you stop AI agents from taking the wrong actions?
Agents get limited permissions, approval steps for important actions and full activity logs, and we test them against real scenarios before launch.
Can our data stay in Australia?
Yes. For Australian clients we can deploy AI services in Australian cloud regions where available and required.
What happens on the AI Discovery Call?
It’s a 30-minute conversation about your goals and current AI plans. We’ll suggest where to start, outline next steps and answer your questions. There’s no obligation.
Ready to Build Generative AI That Works?
Book a 30-minute call with our team. We’ll review your use case and show you the fastest path to production.