AI Development & LLM Integration Services
Most businesses do not need an AI research lab — they need AI working inside the products and processes they already run. StellarCode integrates large language models and machine learning into real software: support chatbots trained on your knowledge base, document-processing pipelines, AI copilots inside your dashboard, and agentic workflows that carry out multi-step tasks instead of just answering questions.
We are pragmatic about model choice — Claude, GPT, Gemini or open-source — and design for cost per request, latency, accuracy and data privacy, not hype. Everything ships with evaluation metrics so you can see, in numbers, whether the AI is actually doing its job.
What you get
LLM & agentic AI integration
Claude, GPT and open-model integrations with tool use, retrieval and multi-step agent workflows embedded in your product.
AI chatbots & copilots
Customer-support and internal assistants grounded in your own documents and data — with guardrails, not hallucinations.
Retrieval-augmented generation (RAG)
Vector search over your knowledge base so AI answers cite your real content, policies and prices.
Document & data processing
Extraction, classification and summarisation pipelines that turn PDFs, emails and forms into structured data automatically.
Process automation with AI
Agent workflows that triage tickets, draft responses, reconcile records and hand off to humans at the right moments.
Evaluation & cost engineering
Accuracy benchmarks, prompt versioning, caching and model-routing that keep quality up and the API bill down.
Technologies we use
- Claude API
- OpenAI API
- LangChain
- pgvector
- Python
- TypeScript
- Supabase
- AWS Bedrock
AI Development & Integration — FAQ
What does an AI integration project cost?
A grounded support chatbot or document-processing pipeline typically runs $5,000–$20,000; deeper agentic workflows and AI copilots inside existing products usually land $15,000–$60,000. Because our engineers are based in Sri Lanka, these are commonly well below Western-agency quotes for equivalent scope, and every project starts with a fixed quote.
Which AI models do you work with?
We work with Anthropic Claude, OpenAI GPT, Google Gemini and leading open-source models, and we routinely mix them — a cheap fast model for classification, a stronger model for reasoning. The choice is driven by your accuracy, latency, privacy and budget requirements, and we benchmark on your real data before committing.
How do you stop the AI from giving wrong answers?
Three layers: retrieval grounding so the model answers from your documents instead of memory; guardrails and output validation that constrain what it may say and do; and an evaluation suite of real test cases that every prompt or model change must pass before deployment. For high-stakes actions we design human-in-the-loop approval steps.
Is our company data safe when using LLMs?
Yes, when architected properly. We use API tiers where providers do not train on your data, keep sensitive fields out of prompts through redaction and tokenisation, and can deploy within your own cloud (for example AWS Bedrock) when data must not leave your environment. Data-flow documentation is a standard deliverable.
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