Custom AI Solutions
Bespoke AI built around your private knowledge — RAG, document intelligence, extraction, and guardrails that generic tools can't match.
// Overview
When off-the-shelf AI isn't enough, we design systems around your documents, workflows, and data. Retrieval-augmented generation, classification, extraction, evaluation, guardrails, and cost controls.
// How This Helps You
- Pull insights from internal docs and unstructured data
- Get accuracy that generic AI can't match
- Build defensible IP and competitive advantages
- Control costs through caching and smart routing
// What's inside
Everything that powers this service
RAG pipeline
Grounds answers in your own documents so the AI stops hallucinating and starts citing.
Document intelligence
OCR and layout parsing turn PDFs, contracts, and forms into structured data.
Classification & extraction
Auto-tags documents and pulls named fields — invoice totals, clauses, IDs — cleanly.
Embeddings & vector search
Converts knowledge into embeddings for fast, semantic retrieval.
Guardrails & safety
Enforces business rules and blocks prompt injection and PII leakage.
Data pipelines
Automated ingestion and sync keep the knowledge base current as your data changes.
Domain adaptation
Tunes models — including Arabic-native — to your terminology and tone.
Evaluation & observability
Tracks retrieval accuracy and answer faithfulness with eval harnesses and logging.
For your company
- Unlock insight trapped in internal docs and unstructured data
- Get accuracy generic AI can't reach, grounded in your own sources
- Cut processing cost and manual handling of documents and data
- Build defensible IP instead of renting someone else's model
- Deploy privately and in-region for finance, healthcare, legal, and government
For your customers
- Faster outcomes — claims, approvals, and cases resolved in minutes
- Answers grounded in real policy, not generic AI guesses
- Sensitive data kept protected and on-policy
- Staff with instant context, so questions aren't repeated
// Compared
Generic AI tool vs. a custom RAG system
| Generic AI tool | Raanzlr custom AI | |
|---|---|---|
| Knowledge | Public data only | Your private documents |
| Accuracy | Plausible but unverified | Grounded and cited |
| Safety | Open-ended outputs | Guardrails, PII protection |
| Data location | Public API, off-shore | Private, in-region deploy |
| Ownership | Rented capability | Defensible, owned IP |
// How we deliver
From kickoff to launch
Discovery & knowledge audit
Map data sources, document types, business rules, and compliance constraints.
Pipeline & ingestion
Connect sources, parse and OCR documents, then chunk and embed into the vector store.
RAG & guardrails
Build retrieval, orchestration, and safety/PII guardrails to your business rules.
Evaluate & tune
Measure retrieval accuracy and faithfulness; tune chunking and prompts.
Deploy & maintain
Launch with observability, monitoring, and scheduled re-indexing.
// Tools & stack
// Start Your Project
Ready to build Custom AI Solutions?
Tell us about your project and a senior engineer will be in touch within one business day.
// Other Services
All servicesFrequently asked questions
- What is RAG and do we actually need it?
- Retrieval-augmented generation means the model answers from your documents rather than from memory. You need it when answers must be grounded in your own contracts, policies or product data, and must cite where they came from. If your questions are general knowledge, you do not need it.
- Where does our data go?
- That is a design decision we make with you before building. Options range from a fully self-hosted pipeline where nothing leaves your infrastructure, to a managed model provider under a data processing agreement. We document exactly what is sent where, and residency requirements are a constraint we design around rather than discover late.
- Do you fine-tune models or use off-the-shelf ones?
- Usually off-the-shelf with good retrieval, because it is cheaper, faster to ship, and easier to change. Fine-tuning earns its cost when you need a specific output format or domain vocabulary that prompting cannot reach reliably. We will say which case you are in.
- How do you stop it making things up?
- Grounding answers in retrieved source passages, returning citations so a reader can check, and setting the system to say it does not know rather than guess. We also build an evaluation set from your real questions so accuracy is measured against your domain before launch, not assumed.
- How long does a custom AI project take?
- Typically 6–12 weeks depending on how clean the source data is and how many systems it has to reach. Data preparation is usually the long pole, not the model work. You get a dated plan with milestones after the technical audit.