01
Discovery Sprint
We define requirements, success metrics, model selection, architecture design, and evaluation criteria before writing a single line of production code.
🧠 LLMs & Agents
Custom AI Built for
Production
Lumo's AI engineering team designs and ships LLM-powered applications, autonomous agents, RAG pipelines, and fine-tuned models that work reliably at scale — not just in demos.
200+
AI systems designed and deployed
4–8 wks
Typical MVP to launch timeline
GPT / Claude / Gemini
Model-agnostic engineering approach
What We Build
AI Engineering That Ships to Production — Not Just Demos
Most AI development agencies build impressive prototypes. Lumo builds production systems. The gap between a working demo and a reliable AI product serving real users is enormous — it requires evaluation frameworks, edge case handling, prompt security, rate limit management, fallback logic, observability tooling, and cost optimization that takes real engineering discipline.
Our team builds across the full AI stack: retrieval-augmented generation (RAG) systems that ground LLMs in your proprietary data, autonomous agent systems that plan and execute multi-step tasks, fine-tuned models that specialize in your domain vocabulary and output format, and AI APIs that integrate cleanly into your existing software infrastructure.
We work with GPT-4o, Claude 3.5/3.7, Gemini 1.5 Pro, and open-source models from the Llama and Mistral families depending on your use case requirements. Latency-sensitive applications often benefit from smaller, faster models. Complex reasoning tasks demand frontier models. Cost-sensitive high-volume applications benefit from fine-tuned smaller models. We design for the right model at the right cost.
Every AI system Lumo ships includes an evaluation framework: a systematic test suite for output quality, a monitoring dashboard for production drift detection, and defined human review touchpoints where model confidence is low. We build observable systems — not black boxes. Everything we ship can be measured, debugged, and improved over time as your requirements evolve.
What We Build
LLM-powered applications (GPT, Claude, Gemini)
Autonomous AI agents + multi-agent pipelines
RAG systems with vector databases
Fine-tuned models (LoRA, QLoRA)
AI APIs + webhook integrations
Evaluation frameworks + test suites
Production monitoring + observability
How We Work
Our AI Development Process
02
Prototype + Eval
We build a working prototype and run it against your evaluation benchmark. You see real outputs on real data before we invest in production infrastructure.
03
Production Build
We engineer for production: error handling, rate limiting, fallback logic, logging, cost optimization, and security hardening — the full stack, not just the ML layer.
04
Deploy + Iterate
We launch to production, monitor performance, address real-world edge cases, and iterate on model selection, prompt engineering, and architecture based on live data.
Common Questions
AI Development FAQ
What kinds of custom AI systems does Lumo build?
We build LLM-powered applications, autonomous AI agents, Retrieval-Augmented Generation (RAG) systems, fine-tuned models, AI APIs, and multi-agent pipelines. Our stack includes OpenAI, Anthropic Claude, Google Gemini, and open-source models like Llama — we choose the right model for your use case and budget.
How long does it take to build a custom AI application?
MVPs of LLM-powered apps typically ship in 4–8 weeks. Complex agent systems with integrations, RAG pipelines, and evaluation frameworks take 8–16 weeks. We always start with a scoping sprint to define the requirements, architecture, and success metrics before any development begins.
Do you fine-tune models or only use off-the-shelf LLMs?
Both. For most use cases, prompt engineering and RAG with off-the-shelf models delivers production-quality results faster and more cost-effectively than fine-tuning. When fine-tuning is appropriate — for specialized domains, tone consistency, or high-volume inference cost reduction — we implement LoRA fine-tuning on open-source models.
How do you ensure AI systems work reliably in production?
We build evaluation frameworks from day one. Every AI system we ship includes a test suite covering edge cases, failure modes, and output quality benchmarks. We implement logging, monitoring, and human-in-the-loop review mechanisms appropriate to the risk level of the system. We also run red-teaming sessions to identify adversarial inputs before launch.
Can you build AI systems that integrate with our existing software stack?
Yes. Integrating AI into existing systems is our most common engagement. We've built AI layers on top of Salesforce, HubSpot, Slack, Notion, custom databases, REST APIs, and legacy systems. Our AI APIs are designed to slot cleanly into your existing architecture without requiring a full rebuild.
Ready to Build?
Let's Ship Your Custom AI System
Tell us what you want to build. We'll scope it, design it, and ship it — in production, not just in a demo.