Enterprise AI Solutions Built for Real Business Value
We build production-grade, secure, and cost-optimized AI applications. From custom LLM fine-tuning and secure database RAG setups to autonomous agentic teams, we turn generative AI into measurable ROI.
Get StartedMoving Beyond AI Prototypes
The era of simple chat wrappers is over. Modern enterprise applications require robust security guardrails, strict compliance with data laws, optimized inference cost structures, and precise answer verification. We provide the mathematical and software engineering depth required to deploy robust AI solutions that scale.
Our Specialized Capabilities
Enterprise Retrieval-Augmented Generation (RAG)
Connect Large Language Models securely to your proprietary corporate databases, wikis, and document stores. We implement hybrid keyword-semantic search, re-ranking systems, and chunk optimization using vector databases like pgvector, Qdrant, and Pinecone to deliver high-precision answers with zero hallucinations.
Key Technologies & Methods
- Hybrid Search (BM25 + Dense Vectors)
- Context Re-ranking & Compression
- Metadata & Document Access Security
- Evaluation and Guardrails (Ragas)
Autonomous Agentic Workflows
Go beyond standard chat forms. We build custom multi-agent teams that coordinate, plan, execute APIs, self-evaluate, and execute complex workflows (e.g. automated customer support, automated data reconciliations) using frameworks like LangGraph, CrewAI, and LangChain.
Key Technologies & Methods
- Multi-Agent Planning Systems
- Human-in-the-Loop Safeguards
- Stateful Workflow Orchestration
- Dynamic Function Calling / Tools
Custom LLM Fine-Tuning & Quantization
For scenarios requiring custom formatting, domain-specific behavior, or localized edge deployments. We fine-tune open-source foundations (like LLaMA 3, Mistral, Phi-3) using PEFT/LoRA techniques and quantize them for cost-efficient hosting on private GPU infrastructure.
Key Technologies & Methods
- LoRA / QLoRA Parameter Training
- Structured JSON Schema Generation
- quantized edge model deployments
- Inference optimization (vLLM, TensorRT)
Computer Vision & Visual Intelligence
Automate quality assurance, spatial analytics, and object classification. We design and train deep learning models for custom industrial and commercial environments, deploying models on cloud platforms or optimized edge processors.
Key Technologies & Methods
- Real-time Object Detection (YOLO)
- Instance Segmentation (SAM)
- Edge hardware compilation (TensorRT)
- Automated QA inspection systems
AI Impact in Production
Customer Support Speed
Deployed autonomous agentic workflows to analyze order anomalies, decreasing logistics ticket resolution times from hours to minutes.
LLM Cost Savings
Implemented semantic prompt caching, model routing, and token compression to support 150k monthly active queries on a limited budget.
Inspection Precision
Designed a custom edge-based YOLOv8 vision system to run QA checks on high-speed hardware assembly lines.
Our AI Stack
Core AI Frameworks
- LangChain
- LangGraph
- LlamaIndex
- CrewAI
- PyTorch
- Hugging Face
Foundation Models
- GPT-4o / GPT-4o-mini
- Claude 3.5 Sonnet / Haiku
- Gemini 1.5 Pro / Flash
- LLaMA 3
- Mistral / Mixtral
Vector Databases
- pgvector (Postgres)
- Qdrant
- Pinecone
- Milvus
- ChromaDB
Deployments & MLOps
- vLLM
- Docker / Kubernetes
- AWS Bedrock / SageMaker
- Azure AI Services
- Triton Inference Server