AI that works, not just demos
Production-ready AI agents, LLM pipelines, and RAG systems — built to run reliably on your data, at your scale, without hallucinating.
What It Is
AI built for production, not prototypes
AI development at amfire goes beyond calling an OpenAI API and streaming text to a chatbox. We design systems — retrieval pipelines that ground responses in your actual data, agents that can reason across multiple steps and use tools like database queries, web search, or email sending, and evaluation frameworks that measure accuracy before anything touches production.
Every AI system we build is observable: you can see what the model was asked, what it retrieved, what it decided, and how much it cost — per request, per user, per feature. Because the difference between a compelling demo and a production AI product is everything that happens when the model gets it wrong.
What We Build
Deliverables
Decode the Stack
Technologies we use
The models, frameworks, and infrastructure behind your AI product.
Case Studies
AI systems we've shipped
Clearpath — AI Document Processing Agent
An autonomous agent that reads construction permit PDFs, extracts key dates, obligations, and clauses, then populates structured records in the SaaS platform — replacing 4 hours of manual work per project.
TalentScout — AI Recruitment Assistant
A RAG-based assistant that answers candidates' questions from a company's internal knowledge base, screens resumes against role requirements, and drafts shortlist summaries for hiring managers.
FAQ
Common questions
What's the difference between an AI integration and an AI agent?
An AI integration calls a model (like GPT-4o) to generate text or classify data in a fixed pipeline. An AI agent goes further — it can reason about a goal, decide which tools to call, take actions (send emails, query databases, browse the web), and iterate until the task is complete. We build both.
How do you keep AI responses accurate and avoid hallucinations?
We use RAG (Retrieval-Augmented Generation) to ground responses in your actual data, structured output schemas to constrain model responses, and eval frameworks to measure accuracy before deployment. For production agents we also implement human-in-the-loop checkpoints for high-stakes actions.
Can you build AI features on top of our existing product?
Yes — this is the most common scenario. We connect to your existing database, APIs, and user context, and layer AI capabilities on top without a full rebuild.
Which AI models do you work with?
Primarily OpenAI (GPT-4o, o1), Anthropic (Claude 3.5 Sonnet), and Google (Gemini 1.5 Pro). For latency-sensitive or cost-sensitive cases we also work with open-source models via AWS Bedrock or self-hosted Ollama.
How do you handle the cost of running AI in production?
We implement caching, prompt compression, model routing (expensive models only for complex tasks), and observability dashboards so you can see cost per user/feature in real time. AI costs are always factored into our architecture recommendations.
Ready to add AI to your product?
We'll audit your use case, recommend the right model and architecture, and build it production-ready.
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