AI & Machine Learning Engineering Services
Move beyond fragile API wrappers and toy demos. We engineer production-grade applied AI systems — combining custom fine-tuned models, semantic retrieval pipelines, and autonomous agent workflows with strict data privacy and low-latency infrastructure inside your own cloud boundary.
Production-grade AI systems engineered for enterprise reliability
We architect and deploy applied AI systems that integrate directly into your transactional software, operational databases, and customer-facing workflows with verifiable outputs.
Enterprise LLMs & Hybrid RAG Pipelines
Verifiable knowledge retrieval grounded in your proprietary documents
Most LLM initiatives fail due to factual hallucinations and weak document indexing. We engineer production RAG pipelines combining dense vector embeddings with sparse keyword search and cross-encoder reranking, ensuring every answer directly cites verified source documentation.
Autonomous Multi-Agent Workflows
Coordinated agent systems that execute multi-step business logic
Move beyond conversational chats to autonomous operational pipelines. Using state machine orchestration, we engineer agent squads that break down complex tasks, query internal databases, parse unstructured data, and trigger validated transactions with human-in-the-loop approvals.
Computer Vision & Multimodal Intelligence
Domain-specific visual models for OCR, document parsing & quality control
Extract actionable structured intelligence from images, video streams, and complex scanned documents. We train and fine-tune specialized computer vision models for technical blueprint parsing, multi-language OCR, automated defect detection, and inventory verification.
Private Model Deployment & Optimization
Self-hosted open-weights models inside your private cloud boundary
Protect proprietary business intelligence and eliminate unpredictable third-party API bills. We deploy open-weights foundational models (such as Llama, DeepSeek, and Mistral) directly inside your private AWS/GCP VPC or on-premise GPU clusters with optimized quantization.
Production AI engineering vs. fragile wrappers and generic tools
Compare the trade-offs between custom applied AI engineering, generic public API wrappers, and off-the-shelf SaaS chatbots.
| Evaluation Criteria | Nymph AI Engineering | Generic API Wrappers | Closed SaaS AI Tools |
|---|---|---|---|
| Data Privacy & Governance | Zero data retention; private VPC or on-premise inference inside your security boundary | Unrestricted API calls to third-party public endpoints risking data exposure | Proprietary vendor servers with opaque data retention and logging policies |
| Model & Asset Ownership | 100% legal ownership of fine-tuned weights, vector schemas, datasets, and code | Proprietary vendor frameworks with high platform switching costs | Zero ownership; total reliance on closed-source platform subscriptions |
| Hallucination Control & Accuracy | Hybrid semantic search (dense + BM25), rerankers, and deterministic citation guardrails | Naive vector retrieval without cross-encoders, prone to factual drift | Generic prompt templates with minimal citation verifiability |
| Inference Latency & Cost Scale | Model quantization, context caching, and optimized private GPU hosting | Unoptimized public API calls with compounding per-token billing spikes | Rigid per-seat licensing tiers with arbitrary usage quotas |
| Workflow & System Integration | Custom API microservices, autonomous agent pipelines, and direct database sync | Isolated chatbot widgets that cannot interact with internal transactional data | Siloed web interfaces requiring manual copy-pasting of enterprise data |
How we engineer and deploy your AI infrastructure
A milestone-governed agile engineering lifecycle with zero surprises. You inspect live software on private staging environments at regular milestones and steer priorities with real-time feedback.
Feasibility Audit & Data Evaluation
We analyze your target use case, evaluate your training and document data, assess compliance requirements, and establish quantitative accuracy and latency baselines.
Model Architecture & Pipeline Design
Our architects select optimal foundational models, model vector schemas, design chunking strategies, and define deterministic guardrail policies.
Prototype Engineering & Staging
We configure private sandbox infrastructure, build the retrieval and agent pipelines, and deploy an initial functional prototype to a private staging URL.
Accuracy, Recall & Guardrail Testing
Our engineers benchmark semantic retrieval precision, evaluate hallucination rates against ground-truth datasets, and stress-test latency under peak loads.
Production Cut & Full Handover
We deploy to production within your private cloud environment, configure telemetry monitoring, and legally transfer 100% of code, weights, and documentation.
Complete engineering deliverables handed over to you
You receive full code repositories, private endpoints, model weights, and telemetry suites so your internal team can operate or extend your AI systems at any time.
Private Inference Architecture
Isolated LLM and vector search endpoints deployed directly within your AWS, Google Cloud VPC, or on-premise infrastructure via Infrastructure as Code.
Fine-Tuned Model Weights & Checkpoints
Custom open-weight model weights and LoRA adapters trained on your domain terminology, complete with training scripts and reproducible datasets.
Guardrails & Policy Middleware
Validation layers and security middleware that prevent prompt injections, enforce strict source citations, and eliminate hallucinations.
Automated Evaluation & Benchmark Suite
Testing harnesses that continuously benchmark response latency, token economics, and semantic retrieval accuracy against ground-truth datasets.
OpenAPI Documentation & Client SDKs
Comprehensive Swagger specifications and pre-configured API endpoints for seamless integration into your existing web, mobile, and backend apps.
30-Day Post-Launch Warranty
Dedicated post-launch warranty covering any stability optimizations, model monitoring, and engineering onboarding for your internal team.
Modern, battle-tested AI frameworks chosen for enterprise stability
We avoid fragile wrapper abstractions. We build with established open-source machine learning ecosystems, high-performance vector databases, and scalable inference runtimes.
Frameworks & Orchestration
- PyTorch
- LangChain
- LlamaIndex
- LangGraph
- Hugging Face
- FastAPI
Vector DBs & Embeddings
- Qdrant
- Pinecone
- pgvector (Postgres)
- Milvus
- ChromaDB
- Weaviate
Inference & Optimization
- vLLM
- TensorRT-LLM
- Ollama
- ONNX Runtime
- Triton Server
- CUDA
Foundational Models
- Llama 3.3
- DeepSeek
- Mistral
- OpenAI GPT-4o
- Claude 3.5 Sonnet
- Gemini 2.0
Computer Vision & OCR
- OpenCV
- YOLOv11
- Tesseract
- PaddleOCR
- Florence-2
- TorchVision
Frequently Asked Questions About AI & Machine Learning Services
Transparent answers regarding data privacy, RAG hallucination control, infrastructure deployment, and model ownership.
Never. We build with zero-data-retention enterprise API endpoints or deploy open-weights models (such as Llama, DeepSeek, or Mistral) entirely within your private cloud VPC or on-premise infrastructure. Your proprietary data never leaves your security perimeter and is never used for external model training.
Ready to Engineer Governed AI Systems for Your Business?
Tell us your target use case or data landscape. Our principal AI architects will evaluate feasibility, outline technical architecture options, and provide a clear milestone roadmap within 24 hours.