We build LLM-powered applications, RAG pipelines, and autonomous agents that go beyond demos — real systems handling real workloads, reliably and cost-efficiently.
From simple chat interfaces to complex multi-agent systems — we cover the full AI development stack.
Connect GPT-4, Claude, Gemini, or open-source models (Llama, Mistral) directly into your product with streaming, function calling, and structured outputs.
Build retrieval-augmented generation pipelines that let your AI answer questions accurately from your own documents, databases, or knowledge bases.
Autonomous agents that plan, use tools, browse the web, write code, and take actions — all orchestrated with LangChain, LangGraph, or custom frameworks.
Replace keyword search with vector-based semantic search using Pinecone, Weaviate, Qdrant, or pgvector. Find the right results every time.
Extract, classify, and structure data from PDFs, invoices, contracts, and forms automatically — no more manual data entry.
End-to-end pipelines that ingest data, process it with AI, and route outputs to your systems — all running on autopilot.
We map your use case, data sources, and success metrics before writing a line of code.
Working proof-of-concept in days — test the AI approach before full build commitment.
Production-grade implementation with error handling, logging, cost controls, and safety layers.
Deploy with observability — trace every LLM call, monitor accuracy, and iterate fast.
Contract analysis, clause extraction, case research summarisation, and legal document drafting assistants.
Financial report parsing, fraud pattern detection, customer support bots, and automated compliance checks.
Medical record summarisation, clinical note extraction, patient intake automation, and diagnostic support tools.
Product recommendation engines, AI customer support, catalogue enrichment, and personalised search.
AI-powered features inside your existing SaaS — chat interfaces, smart summaries, auto-tagging, and predictions.
Process automation, document routing, supplier communication bots, and internal knowledge assistants.
Simple LLM integrations (chat feature, document Q&A) take 1–2 weeks. Full RAG pipelines or autonomous agents take 3–6 weeks depending on data complexity and scope.
Yes. We can integrate AI features into any existing stack — React, Next.js, Django, Rails, or whatever you're using. We don't require a full rewrite.
We use local embeddings or private deployments when data sensitivity requires it. We can run models on your own infrastructure using Ollama, vLLM, or AWS Bedrock to keep data in your control.
API costs vary by usage. We build cost controls, caching layers, and model-routing strategies to keep costs predictable. We'll give you a realistic estimate before we build.
Absolutely. We regularly help teams whose AI features underperform — improving prompts, adding retrieval, switching models, or rebuilding the pipeline architecture.
Tell us what you want to build. We'll scope it, prototype it, and ship it.
