ai-solution
Generative AI .
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.
What We Build
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.
AI-POWEREDLearn how we work
Delivered faster with AI tooling
We don't just write code; we orchestrate intelligence. By integrating Copilot-assisted generation and AI-driven automated testing, we reduce time-to-market by up to 40%.
Copilot-Assisted Generation
LLM-backed development to eliminate boilerplate delays.
AI Predictive Testing
Anticipating edge cases before they enter production.
ENGINEERED FOR PERFORMANCE
The iSkylar Tech Stack
OpenAI API
Anthropic Claude API
LangChain
LlamaIndex
Pinecone
Weaviate
HuggingFace
Python
FastAPI
PostgreSQL + pgvector
AWS Bedrock
Ollama (local LLMs)
Pydantic
Docker
Sentry
Our Development Journey
A streamlined, transparent path from vision to launch.
01
Discovery & Use Case Definition
1–2 weeks
We assess your data readiness, define the generative AI use cases with the highest ROI, agree on accuracy and latency requirements, and produce an architecture plan before any development begins.
02
Data Preparation & Pipeline Design
1–3 weeks
Document chunking, embedding strategy, vector store selection, retrieval testing, and prompt engineering — the foundation that determines whether RAG outputs are accurate enough for production.
03
System Development
3–10 weeks
RAG pipeline, LLM integration, API layer, human review interfaces (if required), and integration into your existing product or workflow. Weekly demos throughout.
04
Evaluation & Red-Teaming
1–2 weeks
We test systematically for hallucinations, prompt injection, off-topic responses, and edge cases — and benchmark accuracy against your agreed success criteria before production launch.
05
Production Deployment
3–5 days
System deployed to your cloud environment with monitoring, token usage tracking, cost alerts, and latency dashboards live from day one.
06
Optimisation & Ongoing Support
Ongoing
Prompt iteration, retrieval quality improvements, model upgrades, token cost optimisation, and evaluation cycles as your data and use cases evolve.
Our Proven Track Record
IMPACTMETRICS
EFFICIENCY GAIN
78%
Reduction in manual document processing time after deploying an AI document intelligence system for a legal-tech client
RETRIEVAL ACCURACY
91%
Accuracy on RAG-powered knowledge assistant after retrieval optimisation and prompt engineering
COST REDUCTION
65%
Monthly OpenAI API cost reduction after migrating a client from GPT-4 to a fine-tuned Mistral model
SYSTEMS DEPLOYED
30+
Generative AI systems deployed to production across content, document processing, and workflow automation categories
Flexible Engagement
Partnership structures tailored to your scale and velocity.
Proof of Concept
A working generative AI prototype on your real data — RAG pipeline, LLM integration, and evaluation report — so you can validate accuracy and ROI before committing to a full production build.
- Defined Scope
- Fixed Budget
POPULAR
Production AI System
End-to-end generative AI system — data pipeline, LLM integration, API build, evaluation suite, production deployment, monitoring, and 3 months of post-launch support. Our most popular engagement.
- Full Integration
- Infinite Scalability
Dedicated Generative AI Team
ML engineers and prompt specialists embedded in your team on a monthly retainer — shipping new AI features, optimising existing systems, and keeping your models current as the underlying technology evolves.
- Defined Scope
- Fixed Budget
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"iSkylar transformed our legacy banking app into a modern, lightning-fast experience. Their AI-driven approach saved us months in engineering time."
JD
James Donovan
CTO, GlobalFin
"
"The level of technical precision and UI craftsmanship is unmatched. They don't just build apps; they build business advantages."
SL
Sarah Lin
Product Head, LuxeRetail
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?+
We use RAG to anchor responses to retrieved source documents, implement confidence thresholds that trigger human review for low-certainty queries, and run structured evaluation suites that test for hallucination rates before launch. We also build citation interfaces so users can verify the source of every AI answer.
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?+
A proof of concept is typically 4–6 weeks. A full production system with evaluation, deployment, and monitoring is usually 10–18 weeks depending on data complexity and integration scope.
What does generative AI development cost?+
Proof of concepts start from $6,000. Production systems from $20,000. Pricing depends on use case complexity, number of data sources, and integration requirements. Fixed-scope quote before signing.
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.
Ready to build your breakthrough?
Contact us today for a technical consultation and a detailed project estimate.
Get a free estimate