Artificial Intelligence
LLMs, agents, and machine learning that ship to production.
We design and build AI-native products — from RAG pipelines to autonomous agents — grounded in a decade of production engineering discipline.
Discuss your projectBy 2026, AI is no longer a differentiator bolted onto a product — it's an expectation baked into how software gets built, from customer-facing chat interfaces to internal workflows that once required a dozen manual steps. The landscape has moved past demos: businesses now need retrieval systems that don't hallucinate, agents that complete real tasks reliably, and models chosen for the job rather than the hype. The common pitfall is treating AI as a prototype exercise — a slick proof of concept that never survives contact with real data, real users, or real cost constraints. Good AI work looks unglamorous: predictable, monitored, and woven into existing systems rather than bolted on.
Wholly Software treats AI systems the same way we treat any production software: with version control, testing, and monitoring from day one, not added after something breaks in front of a customer. We pick the model — OpenAI, Claude, Gemini, or a self-hosted open model — based on cost, latency, and data sensitivity, not brand preference, and we build custom pipelines instead of wiring together off-the-shelf tools that won't scale with your data. Every RAG system and agent we ship gets evaluated against real queries before launch, with guardrails for the cases where the model shouldn't act alone. We're upfront about where AI genuinely helps and where it doesn't.
Engaging Wholly for AI work gets you a scoped system built around a specific business outcome — not a chatbot for its own sake, but a support agent that resolves tickets, a pipeline that turns unstructured documents into structured decisions, or an internal tool that saves your team real hours. You get the model integration, the data pipeline, an evaluation suite to catch regressions, and a deployment plan for monitoring cost and accuracy in production. Because AI systems rarely stand alone, we pair this work with our backend and cloud teams to handle the APIs, data storage, and infrastructure the model depends on, so what you get is a working product, not a research artifact.
What's included
- Machine Learning
- Deep Learning
- Computer Vision
- Natural Language Processing
- Generative AI
- LLM Development
- RAG Applications
- AI Chatbots
- AI Automation
- AI Agents
- Prompt Engineering
- OpenAI, Claude, Gemini & Local Models
How we deliver artificial intelligence
Retrieval-Augmented Generation (RAG)
We design RAG pipelines that connect large language models to your actual documents, databases, and knowledge bases, so answers are grounded in your content instead of a model's training data. That means proper chunking, retrieval tuning, and citation — the difference between a demo that hallucinates and a tool your team trusts.
AI Agents & Workflow Automation
We build agents that don't just chat — they take action: triaging tickets, updating records, or running multi-step workflows across your existing tools. Each agent is scoped to a defined task with clear guardrails and a fallback to a human, so autonomy never comes at the cost of control.
Model Selection & Prompt Engineering
Not every task needs the most expensive frontier model. We evaluate OpenAI, Claude, Gemini, and open-source models against your actual use case, then engineer prompts and system context to get consistent, reliable output at a cost structure that scales with usage rather than against it.
Computer Vision & NLP Pipelines
Beyond chat interfaces, we build custom computer vision and natural language processing pipelines for tasks like document extraction, image classification, and content moderation — purpose-built models tuned to your specific data, not generic APIs stretched to fit a problem they weren't designed for.
Evaluation, Reliability & Deployment
Before anything ships, we build an evaluation suite that tests the system against real queries and edge cases, then instrument it for ongoing monitoring of accuracy, latency, and cost once it's live. AI in production needs the same rigor as any other critical system — we treat it that way.
Have a product in mind? Let's build it together.
Tell us about your idea and timeline — we'll get back to you with next steps within one business day.