Services
AI Development Services
UniQual designs and ships AI-powered product features: large language model applications, retrieval-augmented generation (RAG) over your data, and the evaluation and deployment work needed to run them reliably — not just demos.
Problems this service addresses
Prototypes that never reach production
Many AI demos stall after a notebook or chat UI. We take systems through evaluation harnesses, managed deployment, and monitoring for drift, cost, and latency so they stay usable after launch.
Models without grounding in your data
Generic chatbots invent answers. We design RAG pipelines with vector search — including pgvector, which we have used in shipped product work — so responses stay tied to your knowledge base and business rules.
Automation without guardrails
Workflow AI that touches CRM records, documents, or customer actions needs human-in-the-loop controls. We include evaluation, logging, and checkpoints for high-risk steps alongside the automation itself.
Capabilities
- Custom LLM applications and fine-tuned ranking or coaching experiences
- RAG pipelines with vector search (pgvector evidenced in shipped work)
- In-app AI chat and coaching surfaces
- Document and menu intelligence with OCR where the product requires it
- AI workflow automation with guardrails (see AI Integrations answers)
- Evaluation harnesses and production monitoring for cost, latency, and quality
Technologies we use
Drawn from stacks UniQual already lists across services and answers content.
How we approach the work
Step 01
Discovery and architecture
We map data sources, risk surface, and non-functional requirements before locking an architecture and timeline — the same discovery discipline we use across product engineering.
Step 02
Build with evaluation
Model selection, fine-tuning where needed, RAG or assistant design, and evaluation harnesses run alongside the product UI — not as an afterthought.
Step 03
Deploy and operate
Production deployment on managed infrastructure with monitoring for drift, cost, and latency, plus handover of repositories and runbooks when you are ready to own the system.
See also our engagement process and how we build scalable applications.
Frequently asked questions
What does an AI development company do?
An AI development company like UniQual designs, builds, and deploys production-grade AI systems — machine learning models, large language model (LLM) integrations, retrieval-augmented generation (RAG) pipelines, and autonomous AI agents — owning the full lifecycle from data strategy through MLOps.
Read moreWhat is AI automation for business workflows?
AI automation applies LLMs, agents, and workflow tools (n8n, Zapier, Make) to automate repetitive business processes — customer support, sales research, content operations, document processing, and internal knowledge search. UniQual integrates OpenAI, Claude, and Gemini into your existing CRM, ERP, and communication stack with guardrails and human-in-the-loop.
Read moreHow long does a typical software project take?
Most MVPs ship in 4–8 weeks. Full enterprise-grade platforms range from 3 to 6 months depending on scope, integrations, and compliance. Timelines are locked during the discovery sprint so there are no moving targets mid-build.
Read moreDo you own the IP and source code?
You do — 100%. All source code, Figma files, documentation, CI/CD pipelines, and infrastructure are yours from day one. We hand over clean repositories and runbooks so your internal team can keep shipping without us.
Read moreCan you work alongside our in-house engineering team?
Yes. UniQual regularly embeds alongside in-house engineers, mentors teams, and contributes to existing codebases. We adapt to your tools, Git workflow, sprint rituals, and code review standards—no process friction.
Read moreRelated
Talk to UniQual
Planning an AI product or integrating models into an existing stack? We can help you scope it.
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