Service

Generative AI Development

LLM features, RAG and AI agents that hold up with real users and real data.

GPT-4ClaudeLangChainRAGPythonVector DB

Overview

What we deliver

A Gen AI demo takes a weekend. Making it accurate, affordable and reliable with real users takes engineering. We connect models like Claude, GPT and Gemini to your product, and build retrieval (RAG) over your own data, agents that take actions, and document workflows. We measure accuracy, watch costs and log what the model does, so you can trust it in production.

What's Included

Key capabilities

LLM Integration

Connect GPT-4, Claude, Gemini, or open-source LLaMA models to your existing product or workflow.

RAG Pipelines

Answers grounded in your own documents, databases and knowledge bases, with sources the user can check.

AI Agents & Automation

Agents that work through multi-step tasks using tools such as search, your APIs or code execution.

Fine-Tuning & Prompt Engineering

Domain-specific model alignment through fine-tuning, RLHF, and systematic prompt engineering.

AI Chatbots & Copilots

Assistants inside your product for customer support, internal knowledge or sales.

Document Intelligence

Summarise, extract from, classify and search large volumes of contracts, reports, emails and PDFs.

Technology

Our technology stack

LLM Providers

OpenAI GPT-4oAnthropic Claude 3.5Google GeminiMeta LLaMAMistral

Frameworks

LangChainLlamaIndexCrewAIAutogenHaystackSemantic Kernel

Vector Databases

PineconeWeaviateQdrantpgvectorChromaDBMilvus

Cloud AI

AWS BedrockAzure OpenAI ServiceGoogle Vertex AI

Observability

LangSmithLangfuseWeights & BiasesPhoenix

Who It's For

Common use cases

Customer support AI automation
Internal knowledge base assistants
Contract and document processing
AI-powered search and discovery
Sales intelligence and lead scoring
Code generation and developer tools

Not sure if this is right for you?

Talk to an engineer first.

We offer a free 30-minute discovery call to understand your problem and tell you honestly whether we're the right fit. No sales pitch.

Book a Discovery Call →

FAQ

Frequently asked questions

How do you prevent hallucinations in production?

Through RAG (grounding answers in verified data), output validation, confidence scoring, and human-in-the-loop fallbacks. We treat hallucination risk as an engineering problem, not a disclaimer.

Which LLM should we use?

It depends on your accuracy, cost, and latency requirements. GPT-4o and Claude 3.5 Sonnet are our go-to choices for complex reasoning. For cost-sensitive, high-volume use cases, we often use smaller fine-tuned models. We run benchmarks against your data before recommending.

How do you handle data privacy with LLMs?

We can route your data through private deployments (Azure OpenAI, AWS Bedrock), on-premise open-source models, or implement anonymisation pipelines before data hits any third-party API.

Also