Data & Access Governance
Map approved sources, sensitive fields, retention rules, user roles, and tenant boundaries before data reaches the model.
Applied AI & Machine Learning Engineering
Empower Enterprise Software with Predictive AI Intelligence
We design, fine-tune, and deploy production-grade AI models — embedding Large Language Models (LLMs), RAG vector search, Computer Vision, and Predictive Analytics directly into your business applications.
50+ Models · 99.8% Precision · Enterprise RAG
50+
Custom AI Models Deployed
99.8%
Target Model Quality
10x
Potential Decision Velocity
Governed
Private AI & Access Controls
AI Engineering Pillars
Applied AI & Machine Learning Capabilities We Engineer
Six specialized AI engineering pillars for grounded automation, controlled data access, measurable model quality, and production-ready operations.
01
Enterprise RAG & Private LLM Fine-Tuning
Private Retrieval-Augmented Generation (RAG) pipelines that query approved ERP, CRM, and document sources within defined security boundaries.
Private Enterprise RAG Vector DB Llama 3 Fine-Tuning
02
Computer Vision & Visual Quality Inspection
Convolutional Neural Networks (CNNs) for real-time manufacturing defect inspection, video stream object detection, and spatial tracking.
Computer Vision Defect Detection YOLO / CNNs
03
Predictive Machine Learning & Demand Forecasting
Time-series forecasting models predicting customer churn risk, inventory demand fluctuations, equipment failure, and dynamic pricing rules.
Predictive ML Churn Reduction Demand Forecast
04
Autonomous Conversational AI & Voice Agents
Sub-second latency speech-to-speech AI agents handling level-1 customer support calls, scheduling appointments, and escalating complex inquiries.
AI Voice Agents Speech-to-Text Level-1 Support
05
Intelligent Document AI & Contract Analysis
Multimodal document understanding extracting key clauses, risk flags, financial liabilities, and signatures from legal contracts and medical files.
Document AI Contract Parsing Risk Flagging
06
Production MLOps & Continuous Model Deployment
Model versioning via MLflow, data drift monitoring, automated GPU retraining pipelines, and low-latency Triton inference server hosting.
MLOps Pipelines Drift Detection GPU Inference
Enterprise RAG & Private Vector Knowledge Engines
Data Isolation & Grounded Knowledge Retrieval
Off-the-shelf public AI models expose sensitive enterprise data to privacy risks and lack domain-specific context. We engineer private Retrieval-Augmented Generation (RAG) pipelines that securely query your internal ERP, CRM, and document repositories.
✓
Isolated Private Cloud Hosting
Host suitable open models in an isolated AWS VPC, Azure tenant, or on-premises environment with role-based access and auditable data flows.
✓
High-Speed Vector Databases
Index approved manuals, policy files, and structured records for fast vector retrieval, benchmarked against your data volume and latency goals.
✓
Citation Back-Linking & Source Verification
Verify every generated AI answer with clickable direct links to exact page numbers in internal source documents.
Private knowledge layer · Retrieval · Citations
Vision inference · Quality signals · Edge deployment
Computer Vision & Predictive Analytics
Real-Time Visual Inspection & Predictive Intelligence
Automate visual defect detection in manufacturing lines, video stream surveillance analytics, and complex document OCR parsing using custom-trained convolutional neural networks.
✓
High-Speed Conveyor Defect Inspection
Detect scratches, misaligned labels, and packaging defects in camera streams with throughput validated for your production line.
✓
Time-Series Demand & Churn Forecasting
Identify churn and demand signals early enough to support targeted retention, inventory, and planning workflows.
✓
Edge AI Model Quantization
Optimize models to run locally on low-power edge hardware devices (NVIDIA Jetson, Raspberry Pi) without cloud latency.
What Kind of AI Projects We Build
Real-World Applied AI Solutions & Machine Learning Platforms
From internal enterprise knowledge copilots and predictive sales forecast engines to automated vision inspection, AI voice agents, and document risk analyzers.
✓
Enterprise Knowledge Copilots & Internal Document RAG
Sub-second vector search over internal ERP/CRM files and PDF manuals with zero data leakage.
✓
Predictive Churn & Demand Forecast ML Engines
Real-time customer churn risk scoring, inventory demand forecasting, and automated price elasticity optimization.
✓
Computer Vision Automated Quality Scanners
High-speed conveyor camera inspection detecting manufacturing anomalies with sub-millisecond latency.
✓
Autonomous AI Voice & Customer Support Agents
Speech-to-speech AI support agents resolving level-1 tickets and booking appointments via phone or web widget.
Copilots · Forecasting · Vision · Voice
ENGINEERING BLUEPRINT
The Sibiri Responsible AI Operating Architecture
Successful AI products need more than model selection. We design the controls, evidence, and ownership needed to operate them responsibly.
Evaluation & Guardrails
Create domain test sets and measure groundedness, retrieval quality, failure modes, safety rules, and task success.
Human Oversight
Route high-impact or low-confidence decisions to the right reviewer with context, escalation paths, and an audit trail.
Lifecycle Monitoring
Observe cost, latency, quality, drift, and feedback by model version so teams can improve or roll back with confidence.
AI Engineering Lifecycle
Our 6-Step Applied AI & ML Development Process
Our disciplined AI engineering methodology ensures model accuracy, data privacy, and seamless production integration.
01
AI Opportunity Audit
Auditing business workflows, training data quality, privacy constraints, and ROI feasibility targets.
02
Data Pipeline Setup
Cleaning, tokenizing, and indexing proprietary enterprise datasets into high-dimensional vector embeddings.
03
Model Training & Fine-Tuning
Fine-tuning open-source LLMs (Llama 3, Mistral) or training custom CNNs / XGBoost models on GPU infrastructure.
04
Vector RAG Integration
Building sub-second vector search pipelines, prompt engineering templates, and source citation back-links.
05
MLOps Deployment
Deploying models on GPU inference clusters (Triton / Ray) with RESTful API endpoints and role-based access.
06
Continuous Learning
Monitoring data drift, user feedback, cost, and quality thresholds to guide controlled retraining and version updates.
AI Technology Stack
Frameworks, Vector DBs & Model Architectures We Utilize
We build production-grade AI applications using top-tier deep learning frameworks and vector engines.
PyTorch & TensorFlow
Enterprise Deep Learning & CNN Models
LangChain & LlamaIndex
LLM Orchestration & RAG Pipelines
Qdrant, Pinecone & Milvus
Sub-Second Vector Search Databases
OpenAI, Llama 3 & Claude APIs
State-of-the-Art Language & Vision APIs
Quantifiable ROI
Choose an AI Architecture That Fits the Risk
Compare unmanaged public-tool usage with a governed enterprise AI implementation designed around your data and workflows.
Generic Public AI Chatbots
- ✕ Risk of uploading confidential corporate IP and client data to third-party public models.
- ✕ Frequent hallucinations generating incorrect information without source citations.
- ✕ Generic responses lacking specific knowledge of your internal ERP/CRM data.
- ✕ High recurring per-token API costs scaling uncontrollably with enterprise usage.
Sibiri Custom Private Enterprise AI
- ✓ Private-cloud or on-premises deployment options with defined data boundaries and access controls.
- ✓ Source-linked responses and evaluation workflows that make important outputs easier to verify.
- ✓ Domain adaptation and workflow integration focused on measurable task-level outcomes.
- ✓ Transparent infrastructure and inference-cost modelling matched to expected usage.
FAQ
Frequently Asked Questions
Everything you need to know about custom AI & Machine Learning solutions with Sibiri Innovation.
Is our proprietary business data safe when training or querying private AI models?
Yes. We deploy open-source models (such as Llama 3 or Mistral) inside your isolated private cloud environment (AWS VPC or Azure tenant) or on-premises servers. Your data never leaves your infrastructure or gets shared with third parties.
What is Retrieval-Augmented Generation (RAG) and why is it better than plain ChatGPT?
RAG connects a language model to approved databases and documents. It retrieves relevant context before generating a response and can attach source links, making answers more grounded and easier to verify. We still evaluate accuracy for your domain and keep human review where the risk requires it.
Can admins update headings, section text, images, and numbers from a control panel?
Yes. Every enterprise service page includes an integrated admin control panel allowing authorized team members to update headings, paragraphs, section images, key metrics, and SEO metadata dynamically.
How long does a custom AI Solutions project take to deploy?
A focused proof of value may take several weeks, while production delivery depends on data readiness, integrations, security review, and evaluation requirements. Discovery produces a phased scope with clear milestones before implementation begins.