Hire AI Developers
Hire AI developers from Lumoonix to turn a business problem into a useful, measurable AI capability. We support AI-powered product features, LLM integrations, retrieval-augmented generation (RAG), workflow automation, agents, and evaluation systems. The right solution is not always the largest model or the most autonomous agent; it is the simplest reliable system that meets the user need, respects data boundaries, and can be maintained after launch.
AI Development Services
Our AI development work can include discovery, model and API integration, prompt and tool design, retrieval pipelines, workflow automation, evaluation, deployment, and ongoing improvement. We start by identifying the user, the task, the expected output, and what a successful result looks like. This prevents teams from building a demo that looks impressive but fails in day-to-day use. Depending on the use case, the solution may combine an existing model API with conventional software logic, search, structured data, and human review rather than relying on a model alone.
LLM Integration: OpenAI, Claude, and Gemini
Different model providers offer different capabilities, latency, pricing, context windows, and operational characteristics. Lumoonix developers can integrate APIs from providers such as OpenAI, Anthropic Claude, and Google Gemini where suitable. The implementation should keep provider-specific logic manageable, protect API keys, handle rate limits, and define fallbacks where justified. We help assess quality and cost using representative tasks instead of choosing a model based only on public benchmarks. Model availability and API behaviour can change, so production systems should be designed for controlled updates.
RAG and Knowledge-Based Applications
Retrieval-augmented generation connects a language model to selected source material so answers can be grounded in relevant documents or records. A typical pipeline includes data ingestion, parsing, chunking, metadata, indexing, retrieval, and answer generation. The quality of the source content and retrieval strategy has a major influence on results. We can help design permissions-aware retrieval, document refresh processes, source references, and safeguards for cases where evidence is missing. RAG does not guarantee factual accuracy; it needs evaluation and clear behaviour when the system cannot find a reliable answer.
AI Agents and Workflow Automation
AI agents can use tools or follow multi-step workflows to complete tasks such as classifying requests, summarising information, preparing drafts, or initiating approved actions. The appropriate autonomy level depends on risk. Low-risk tasks may be automated end to end, while financial, legal, identity, or customer-impacting actions may require human approval. We design explicit tool permissions, input constraints, execution limits, logs, and recovery paths. A deterministic workflow with one model-assisted step is often easier to test and operate than a fully autonomous agent.
Evaluation, Guardrails, and Human Review
AI quality should be measured with a test set that reflects real user requests, edge cases, and known failure modes. Evaluation can consider correctness, groundedness, completeness, response format, latency, cost, and harmful or unauthorised behaviour. Guardrails may include structured outputs, validation rules, policy checks, retrieval restrictions, and human review. We recommend recording representative failures and using them to improve prompts, retrieval, tools, and test coverage. A model response should not be treated as verified simply because it sounds confident or fluent.
Data Privacy and Production Readiness
Before connecting AI to business systems, teams should determine what data can be sent to external providers, what must be redacted, and which users may access which records. Production architecture should consider retention, logging, access control, secrets, encryption, and provider terms. Systems that process regulated or sensitive information require additional legal, security, and compliance review. Lumoonix can support the technical design, but legal obligations and regulatory interpretations should be confirmed with qualified advisers. Monitoring should cover not only uptime but also response quality, usage, and unexpected cost growth.
Relevant Project Context
Lumoonix is developing the Cleared Advisory AI AML platform, which can be discussed as project context with an accurate description of its development status. Any public case study should clearly distinguish a project in development from a production deployment and should not imply outcomes or certifications that have not been verified. We use this same evidence standard for AI work generally: explain the business problem, the system’s role, the safeguards, and what has actually been delivered. Specific results should be published only when the team has approved and can substantiate them.
Hiring Model, Skills, and Rates
You can hire AI developers for a prototype, an integration sprint, a production feature, or ongoing product development. Relevant skills may include API integration, retrieval, embeddings, vector search, agent tooling, evaluation, Python or TypeScript, data pipelines, and deployment. We can share an approved rate card in USD, GBP, and INR, along with anonymised profiles and an assessment plan. Cost depends on seniority, architecture, data readiness, security requirements, and how much of the system already exists. A discovery phase can help separate essential features from speculative scope.
How We Vet AI Developers
A useful assessment asks a candidate to reason through a realistic use case: identify failure modes, propose an architecture, implement a small integration, or evaluate sample outputs. We look for software engineering fundamentals alongside model knowledge. Strong candidates understand when not to use AI, how to constrain tool access, how to test outputs, and how to explain uncertainty to stakeholders. We also assess collaboration skills, documentation, code review habits, and time-zone overlap so the developer can contribute effectively to your existing team.
Frequently Asked Questions
Can AI developers build a chatbot for our documents?
Yes, but the scope should include source ingestion, access control, retrieval quality, evaluation, and ongoing maintenance—not just a chat interface.
Do we need a custom model?
Often not; an existing model API plus good data and evaluation may be enough.
Can an agent take actions in our systems?
It can, when the tools and permissions are deliberately constrained and suitable approvals are included.
How do we control AI costs?
Track usage, choose models by task, cache where appropriate, limit unnecessary context, and monitor performance.
Start Your AI Project
Share the workflow you want to improve, the systems and data involved, the expected users, and how you will judge success. Lumoonix can help scope a practical first release, identify risks, and plan evaluation before expanding the solution. Contact us to discuss AI developer availability, skills, and engagement options.
Define Success Before Selecting a Model
Write down the current process, the users affected, the expected improvement, and the acceptable error rate before comparing models. Establish a baseline using the existing workflow so that an AI prototype can be evaluated against something meaningful. Include representative examples, difficult edge cases, and cases where the system should refuse or escalate. A small, measurable pilot makes it easier to decide whether to invest further and prevents subjective demos from being mistaken for production readiness.
AI Cost and Operational Planning
AI costs can include model tokens, embeddings, vector storage, hosting, observability, and human review. Estimate usage using realistic request volumes and context sizes, then monitor actual costs during a pilot. Caching, retrieval improvements, smaller models for simpler tasks, and shorter prompts can help, but quality must be measured alongside spend. Define usage limits and alerts before broad release. An AI feature should have an owner who reviews quality, provider changes, user feedback, and operational costs after launch.
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