Stop paying people to do
what software can do automatically.
LLMs, RAG pipelines, and AI-powered workflows, grounded in your data, not generic outputs.
Intelligence that initiates.
iSkylar builds production generative AI systems, RAG-powered knowledge assistants, LLM-integrated workflows, content generation pipelines, document intelligence tools, and custom fine-tuned models, using OpenAI, Anthropic, open-source LLMs, and your proprietary data. We engineer systems that are accurate, auditable, and cost-efficient at production scale, not just impressive in a demo.
This is for you if...
RAG (Retrieval-Augmented Generation) Systems
We build knowledge bases and retrieval pipelines that ground LLM responses in your documents, databases, and internal content eliminating hallucinations and making AI answers accurate and auditable.
LLM-Powered Workflow Automation
We integrate LLMs into your business workflows document classification, data extraction, report generation, email drafting, and decision routing replacing manual steps with AI that operates at your quality bar.
AI Content Generation Pipelines
Automated pipelines that generate on-brand marketing copy, product descriptions, localised content, and structured reports at scale with human review gates and brand voice controls built in.
Document Intelligence
AI systems that read, extract, classify, and summarise contracts, invoices, forms, and unstructured documents turning document processing from a manual bottleneck into an automated, auditable pipeline.
Fine-Tuning & Custom Model Training
We fine-tune open-source LLMs (Llama, Mistral, Phi) on your domain data customer service transcripts, technical documentation, compliance materials to improve accuracy and reduce inference costs versus GPT-4.
AI Product Feature Integration
We embed generative AI capabilities into your existing product intelligent search, auto-complete, summarisation, Q&A interfaces, and AI-assisted forms using clean APIs that do not require your team to become ML engineers.
The Implementation Roadmap
Discovery & Use Case Definition
TIMELINE
1–2 weeks
Data Preparation & Pipeline Design
Document chunking, embedding strategy, vector store selection, retrieval testing, and prompt engineering the foundation that determines whether RAG outputs are accurate enough for production.
TIMELINE
1–3 weeks
System Development
TIMELINE
3–10 weeks
Evaluation & Red-Teaming
We test systematically for hallucinations, prompt injection, off-topic responses, and edge cases and benchmark accuracy against your agreed success criteria before production launch.
TIMELINE
1–2 weeks
Production Deployment
TIMELINE
3–5 days
Optimisation & Ongoing Support
TIMELINE
Ongoing
Frequently Asked Questions
What is RAG and why does it matter?
RAG (Retrieval-Augmented Generation) grounds LLM responses in your actual documents and data rather than relying on the model's training knowledge alone. This eliminates hallucinations on domain-specific questions, keeps answers current, and makes every response auditable you can trace exactly what source the AI drew from. For enterprise use cases, RAG is almost always the right architecture over fine-tuning alone.
How do you prevent the AI from hallucinating incorrect information?
Can you build generative AI on our private, proprietary data?
Yes. All data stays in your cloud environment. We never send your proprietary documents to third-party APIs without your explicit decision and for clients with strict data privacy requirements, we run entirely on self-hosted open-source models using Ollama or AWS Bedrock.
How long does generative AI development take?
What does generative AI development cost?
Should I fine-tune a model or use RAG?
For most business use cases, RAG is cheaper, faster to update, and more explainable than fine-tuning. Fine-tuning is the better choice when you need to change the model's style or behaviour rather than its knowledge for example, training a customer service tone or a domain-specific classification task. We advise on the right approach after understanding your requirements.
Who owns the AI system and our data?
You own everything pipeline code, vector stores, prompt configurations, evaluation suites, and all processed data. We build in your cloud account so you are never dependent on our infrastructure.
Do you provide ongoing optimisation after launch?
Yes. Generative AI systems improve significantly with post-launch iteration better retrieval, refined prompts, and updated source data. We offer optimisation retainers that run evaluation cycles monthly and ship improvements continuously.