Accurate, Data-Grounded
AI Systems.
We bridge the gap between creative AI and enterprise reliability. From custom RAG pipelines to specialized model fine-tuning, we build systems that don't just "talk"—they perform.
RAG: The Reliability Standard
Retrieval-Augmented Generation (RAG) is how we ensure your AI never guesses. By grounding every response in your actual documentation, we eliminate the primary risk of LLMs: hallucinations.
Accurate Citations
Every claim linked back to your source data.
Real-Time Knowledge
Sync with dynamic databases and live document flows.
Private Context
Keep proprietary data isolated from base model training.
Generic AI Problem
- • Hallucinates facts when it lacks data
- • Knowledge frozen at training cut-off
- • Zero access to private company docs
- • Cannot verify source of information
- • Data processed on public servers
Scaleopal RAG Solution
- Strict grounding in your documentation
- Always live, dynamic knowledge base
- Full integration with private datasets
- Automatic source-cited attribution
- Private, sovereign infrastructure options
Our RAG Pipeline Architecture
We build modular, high-performance pipelines designed for enterprise scale and pinpoint accuracy.
Data Ingestion
Connect to PDFs, Notion, SQL, and APIs. We handle the heavy lifting of parsing unstructured data into clean context.
- Dynamic Syncing
- Multi-Source Connectors
- OCR & Pre-processing
Chunking & Embeddings
Smart semantic segmentation that preserves context. We turn your data into searchable high-dimensional vectors.
- Semantic Chunking
- Custom Embeddings
- Context Window Optimization
Retrieval Strategy
Beyond keyword search. We use hybrid semantic matching and re-ranking to find the exact needle in the haystack.
- Hybrid Search
- Cross-Encoder Re-ranking
- Query Expansion
Response Layer
Final generation with strict guardrails. Every answer is double-checked against the retrieved facts.
- Fact Verification
- Source Attribution
- Confidence Scoring
Real-World Impact
RAG systems are the foundation of modern AI ROI. Here is how we deploy them for high-stakes business environments.
Knowledge Assistants
Empower your team with an AI that knows your entire internal wiki, HR policies, and training manuals.
Enterprise Search+
Search across Slack, OneDrive, and specialized databases with natural language queries.
Support Automation
Automate technical customer support with an AI that reads your latest docs and past resolved tickets.
Legal & Research
Automate document review and summary generation for complex legal or academic datasets.
Specialized Fine-Tuning
When retrieval isn't enough, we modify the core model. Fine-tuning is for agencies needing specific brand voices, niche domain expertise, or cost-optimized high-volume models.
Why Fine-Tune?
- Total Voice Control: Embed your brand tone so every output feels human-written from your agency.
- Domain Mastery: Specialized models for legal, medical, or niche tech that base models often get wrong.
- Performance Efficiency: Creating smaller, faster models that do one task better than any general model.
Our Training Process
Data Curation
Cleaning and formatting historical data and style guides.
SFT / LoRA
Efficient training using state-of-the-art PEFT techniques.
Validation
Benchmarking against target results with human-in-the-loop.
Quantization
Optimizing weights for fast, low-cost inference.
Sovereign Infrastructure
Total data autonomy. We host models on your private cloud or on-premise hardware. No third-party API monitoring. No training on your data. Zero leakage.
Private Runtime
Models run isolated on your private cloud (AWS/Azure/GCP) or local servers.
Deterministic Costs
Eliminate unpredictable per-token API fees. Fixed infrastructure pricing for high volume.
Total Control
You own the weights, the data, and the hardware usage. Complete audit trails.
Supported Enterprise Stack
White-Label
AI Engineering Partnership
Deliver cutting-edge RAG & Fine-Tuning solutions as your own proprietary service line. We provide the engineering DNA; you keep the brand equity and client relationship.
Custom enterprise AI setups, industry-specific knowledge tools, and private GPTs.
Infrastructure hosting, model training, technical pre-sales, and 24/7 technical monitoring.
Our Commitment to You
Invisible Partnership
We never interact with your clients. We sign strict NDAs and act as your 'Product Development Team'.
100% IP Transfer
Upon completion, your clients own their specialized models and vector databases. No lock-in.
Scalable Margins
Our fixed implementation pricing is designed for you to add significant margin for your agency.
RAG & Fine-Tuning Questions
Common questions about RAG pipelines, model fine-tuning, and sovereign AI deployment.
RAG lets AI search and cite your documents without retraining the model. Fine-tuning trains the model itself on your data to change its behavior and style. RAG is better for knowledge retrieval, fine-tuning is better for consistent voice and format. Often you use both together.
When properly designed, RAG systems are highly accurate because they only answer based on retrieved documents and provide source citations. Accuracy depends on document quality, chunking strategy, and retrieval precision. We benchmark and optimize each system during development.
Yes. We've fine-tuned models for legal, medical, real estate, and SaaS industries. We collect industry-specific training data, fine-tune using LoRA techniques, and validate outputs against expert review. Results are typically measurably better than base models for domain tasks.
Sovereign AI is necessary when: 1) You have strict compliance requirements (HIPAA, GDPR), 2) Training data is proprietary/competitive advantage, 3) High volume where API costs become prohibitive, or 4) Legal/contractual restrictions prevent third-party processing. For most businesses, cloud APIs with proper security are sufficient.
RAG has setup costs (pipeline development, vector database) plus ongoing API/hosting costs. Fine-tuning has higher upfront costs (data prep, training) but lower per-use costs. For low-medium volume, RAG is cheaper. For high volume with consistent tasks, fine-tuning pays off. We help you model both scenarios.
Simple RAG systems (chat with PDFs) can be ready in 2-3 weeks. Complex enterprise systems (multiple data sources, hybrid search, custom UI) typically take 4-8 weeks. Fine-tuning adds 2-4 weeks depending on data availability and quality.
Yes. We design incremental update pipelines that refresh the knowledge base on schedules (hourly, daily) or triggered by events. For truly real-time needs, we implement streaming ingestion. The retrieval layer always searches the latest available data.
Properly designed RAG systems say 'I don't have information about that' rather than hallucinating. We implement confidence thresholds and fallback responses. You can optionally route unknowns to human support or log them for knowledge base expansion.
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