AI systems built for production
A working demo proves that AI can do something. Production requires much more: reliable data, integration with existing systems, measurable quality, permissions, monitoring and clear boundaries around what the system can do. We build RAG systems, AI agents, and computer vision on your real data, with retrieval, permissions, and evaluation engineered in, so answers stay grounded and quality is something you can measure.
"They're not vendors; they're partners. It's a hugely valuable relationship to us."
Daniel Wolfe · Director, IsaiX Technologies · Verified Clutch review
An AI workflow that cut assessment-summary time 40 to 50% while the inspector kept the decision.
SAVI 360 supports precision-cleaning and compliance assessments across healthcare and education. It reads the inspection record, retrieves the relevant passages from approved procedures and drafts an editable summary with the supporting evidence attached. The inspector reviews, corrects and approves.
Retrieval-augmented generation · citation-backed evidence · explicit no-answer behavior · human approval
Where AI projects become difficult
The difficult part usually appears after the demo, when real data, users, permissions and business risk enter the system.
The prototype works, production does not
Real data, permissions, latency, edge cases and users who behave differently from the test set change the problem.
Answers sound convincing but cannot be trusted
Retrieval quality, citations, access control and explicit no-answer behavior have to become part of the product.
The agent can act, but should not do everything
Identity, defined actions, policy checks and a clear handoff to a person need to be engineered in.
Nobody has defined what good looks like
Accuracy, time saved, cost and latency need explicit thresholds before the system earns the right to scale.
Store photographs turned into reviewable product and shelf evidence
A production computer-vision system identifies shelves, products and gaps from store photographs. It combines visual and text signals, rejects weak candidates and routes uncertain cases to human review. Hubpix was subsequently acquired by IB Group.
Production-ready means more than choosing a model
AI capabilities that sit inside real products and workflows
Enterprise knowledge and RAG
Search, assistants and evidence-backed workflows grounded in approved information.
Agents and workflow automation
Defined actions across business systems, with permissions, policy checks and escalation.
AI-enabled products
Search, recommendations, extraction and conversational features inside existing software.
Computer vision and operational AI
Product recognition, visual inspection, retail audits, support and analytics.
Outcome first. Model last.
Model choice matters, but it is rarely the durable differentiator. The harder engineering sits around data, integration, evaluation, permissions and operations.
In our clients' words
We highly recommend Net Solutions to any business looking for a professional, innovative, and trustworthy IT development team. They have been instrumental in our success, and we look forward to continuing our collaboration for many years to come! From day one, NS has demonstrated a deep understanding of our business goals and has provided innovative, tailored solutions that have truly transformed our eComm architecture. We’ve had the privilege of working with NS for our IT development needs, and we couldn’t be more satisfied with their exceptional service and expertise.
They’re very transparent and honest, which is why I gave them more work.
They always take the time to understand and give suggestions on how to make certain ideas work best.
Is Net Solutions the right fit?
Net Solutions is typically a good fit when the AI initiative matters enough that data access, integration, measurable quality, permissions and long-term ownership matter more than a quick demo. We can own delivery or work alongside your existing product, engineering and data teams.
Common questions
What types of AI development do you do?
Enterprise knowledge and RAG, AI agents, intelligent workflows, AI features inside existing products, computer vision, support and analytics.
Which AI models do you work with?
We are model agnostic. The right model depends on quality, latency, security, cost and the job to be done, with architectures that avoid single-provider lock-in.
How do you reduce hallucinations?
Constrained data sources, retrieval, citations, evaluation, confidence thresholds, explicit no-answer behavior and human review, matched to the use case.
Can you add AI to an existing application?
Yes. A significant share of our work is adding AI capabilities to existing products and workflows.
Can you work alongside our internal AI team?
Yes. We can own a defined workstream or work with existing product, engineering, data and security teams.
What does an AI project cost?
The cost driver is rarely the model; it's the data access, integration, and evaluation around it. We start by proving the riskiest assumption in a small phase, which keeps early spend low and gives you a real basis to price production.
Why not just use ChatGPT or Copilot?
Those tools are strong for general productivity but can't reliably access your data, enforce your permissions, or meet a measurable quality bar for a specific process. Production AI needs grounding, boundaries, evaluation, and human checkpoints, which is the engineering around the model.
Take the AI use case beyond the demo
Tell us what you want AI to improve, which systems it needs to work with and how the business would know it is succeeding. We will help determine what to test first and what it will take to run reliably in production.
