AI & Machine Learning
Custom AI wired into real products — chatbots, agents, RAG, computer vision, and voice that ship, not just demo.
Most "AI projects" die in the demo stage — a slick prompt in a sandbox that never touches production data, auth, or real users. Ours don't. We build the plumbing around the model: retrieval pipelines that pull from your actual documents and databases (pgvector, LlamaIndex), agent orchestration that calls your internal tools and APIs (LangGraph), and the guardrails, logging, and citation trails that make an LLM output something your team can trust and ship. Whether it's a support assistant grounded in your knowledge base, a document-extraction pipeline, or a real-time voice agent, the work is the same discipline: wire a model into a system that has to work on Tuesday and still work in six months.
This is for companies with a specific, bounded problem — not "add AI to the roadmap." Support teams drowning in repetitive tickets. Ops teams re-keying data from scanned forms or IDs. Product teams that want personalization or churn prediction but don't have an ML team to build it. We work across OpenAI and Anthropic Claude for language tasks, PyTorch for custom vision and prediction models, and ElevenLabs alongside OpenAI Realtime for voice — picking the stack based on what the problem needs, not what's trendy.
Credibility here comes from scope discipline, not size. We start with a narrow, working slice on your own data — not a generic chatbot demo — because that's the only way to know if retrieval accuracy, latency, and edge cases will hold up before you commit budget to the full build.
Our process
Data & Scope Audit
We map your docs, databases, or image/audio sources against the use case — chatbot, agent, vision, or voice — and define what accuracy and latency look like before writing code.
Grounded Prototype
We build a working slice on your real data: a RAG pipeline in pgvector and LlamaIndex, or a first-pass vision/voice model, so retrieval quality is proven early, not assumed.
Agent & Integration Build
Tool-calling logic goes in via LangGraph, connected to your APIs and internal systems, with citations, guardrails, and logging so outputs are traceable, not a black box.
Hardening & Handoff
We load-test edge cases, tune prompts and retrieval against real usage, and hand off a production-hardened system with monitoring, not a demo that breaks under real traffic.
What we offer
Chatbots & Assistants
LLM support and sales assistants grounded in your docs, on web, WhatsApp, or Slack
AI Agents & RAG Systems
Tool-using agents and retrieval systems that answer from your private data, with citations
Computer Vision & OCR
Object detection, document/ID extraction, and quality-inspection pipelines
Voice & Speech AI
Real-time voice agents, transcription, and live translation with OpenAI Realtime and ElevenLabs
ML & Recommendation
Custom prediction, churn, fraud, forecasting, and personalization models
Technologies we use
Frequently asked questions
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AI & Machine Learning
AI and machine learning development is the work of connecting language, vision, voice, or prediction models to a real product so they answer from your own data and keep working in production. At Prixelo, that means LLM chatbots and assistants grounded in your documents, tool-using AI agents and retrieval (RAG) systems that cite their sources, computer vision and OCR pipelines, real-time voice agents, and custom prediction or recommendation models. It suits companies with a specific, bounded problem: support teams handling repetitive tickets, operations teams re-keying data from scanned forms, or product teams that want personalization without an in-house ML team. Delivery covers a data and scope audit, a working prototype on your real data, integration with your APIs and internal systems, and a hardened handoff with monitoring and logging. A working prototype typically lands in 2-4 weeks, and a production-hardened version in 8-12 weeks. A scoped chatbot or RAG integration starts from $7,000; AI agents and custom vision or voice systems start from $20,000+.
Questions buyers ask
What does AI and machine learning development include?
It includes everything around the model, not just the prompt. Typical scope covers LLM integration, retrieval over your documents with vector search, agents that call your internal tools and APIs, computer vision and OCR, voice and speech features, and recommendation or prediction models. Each build also includes guardrails, logging, and citation trails so your team can check where an answer came from, plus monitoring once the system is live.
What is RAG, and when does a business need it?
RAG (retrieval-augmented generation) means the system first searches your own content, such as help articles, policies, or database records, and then has the language model answer using only what it found. A business needs it when answers must reflect private or frequently changing information that a general model was never trained on, and when users or staff need to see which source an answer is based on.
Which problems are a good fit for a custom AI build?
The best fits are narrow, repeated tasks with clear inputs and a measurable result: answering recurring support questions from a knowledge base, extracting fields from scanned forms or IDs, routing or summarizing incoming requests, transcribing calls, or predicting churn from existing customer data. A broad goal like "add AI to the product" is usually better broken into one such task first, so accuracy can be tested before more budget is committed.
How do you reduce wrong or made-up answers from an AI system?
We ground answers in retrieved sources, show citations, and limit the system to the tasks it was scoped for. Before writing code, we agree on what acceptable accuracy and response time look like, then test against real examples from your data, including awkward edge cases. Logging every request and answer makes errors traceable, so prompts and retrieval can be tuned based on actual usage rather than guesswork.
What do we need to prepare before an AI project starts?
Mainly access to the data the system will work from: documents, database tables, sample images, or audio recordings, along with a handful of real examples of the questions or tasks it should handle. It also helps to name one person who knows the process well and can judge whether outputs are correct. We map these sources during the scope audit, so nothing needs to be perfectly organized beforehand.
What happens after the AI system goes live?
Handoff includes monitoring and logging, so you can see how the system is used and where it struggles. Real traffic usually surfaces questions and inputs that testing missed, so prompts and retrieval are tuned against that usage. Models and providers also change over time, so it is worth planning for periodic re-testing whenever the underlying model is updated or swapped.