Hydrating View...
Turn Raw Algorithms into High-Impact Autonomous Digital Capabilities
At aaisol, we design and deploy reliable AI solutions for businesses. We engineer practical Retrieval-Augmented Generation (RAG) systems, smart workflow automation, predictive models, and computer vision tools that integrate smoothly into your everyday software. Our focus is on practical value, fast response times, data privacy, and measurable business results.
We bridge the divide between theoretical computer science and stable production software.
Deep expertise across both classical statistical ML and state-of-the-art LLM architectures
Production-ready deployment pipelines using Docker, Kubernetes, and optimized inference servers (Triton/vLLM)
Strict data privacy standards with self-hosted models, VPC isolation, and zero third-party data leakage
Observable AI with prompt evaluations, guardrails, drift monitoring, and fallbacks
Every facet of this domain engineered with best-practice standards.
Custom fine-tuning, domain-specific instruction tuning, prompt engineering, and synthetic data pipelines.
Hybrid vector search, semantic chunking, reciprocal rank fusion, and verifiable citations for enterprise knowledge bases.
Multi-agent orchestration, tool-use execution, memory management, and deterministic guardrail layers.
Intent recognition, sentiment analysis, entity extraction, document summarization, and multilingual translation.
Object detection, visual quality inspection, OCR, facial analysis, and real-time edge video stream inference.
Customer churn prediction, dynamic pricing models, demand forecasting, and predictive maintenance algorithms.
Collaborative filtering, deep neural ranking models, and session-based personalized discovery systems.
Omnichannel contextual assistants equipped with role-based access control and live CRM integrations.
We leverage modern, resilient technologies without adding unnecessary dependencies.
From initial architectural audits to production monitoring, our methodology is designed for transparency and zero downtime.
We evaluate existing datasets, establish quantitative benchmark metrics (F1, BLEU, latency targets), and choose the optimal architectural approach.
Cleaning, structuring, tokenizing, embedding, and setting up reproducible training/validation splits.
Iterative experimentation with foundational models, hyperparameter tuning, RAG indexing, and agent workflow definition.
Exposing models via high-throughput FastAPI endpoints, streaming WebSockets, and asynchronous task queues.
Deploying on scalable GPU/CPU clusters with active latency tracking, hallucination checks, and automated drift alerts.
How organizations leverage our engineering in real enterprise contexts.
Automated analysis of 100+ page supplier agreements to extract non-standard indemnities and liability clauses with precise citations.
High-speed camera vision model classifying PCB surface imperfections at 60 frames per second on factory edge hardware.
Session-based neural recommendation engine matching visitor behavior to inventory variants in real time.
Our engineering systems are designed to comply with rigorous regulatory environments and high-availability demands across:
Evaluation-driven workflows verify model responses against rigorous ground-truth test suites.
Streamed responses, model quantization (AWQ/GGUF), and semantic caching for responsive user experiences.
All custom weights, training datasets, and orchestration code belong 100% to your organization.
Clear answers to common questions about architecture, privacy, timeline, and deliverables.
We deploy open-source models (such as Llama 3, Mistral, or Qwen) within your own private cloud VPC or on-premise infrastructure. When third-party APIs like OpenAI or Anthropic are required, we utilize enterprise zero-retention agreements where client data is never stored or used for model training.
Prompt engineering sends static text to an LLM, which is limited by the context window and cut-off knowledge. A RAG (Retrieval-Augmented Generation) pipeline dynamically queries your company's proprietary databases, documentation, and vector stores to inject live, verified contextual data into the generation process with exact source references.
Yes. We frequently conduct architectural audits to implement semantic caching, prompt compression, smaller fine-tuned specialist models (e.g. 8B parameters instead of 70B+), and high-throughput inference engines like vLLM, frequently cutting API costs by 60-80%.
Schedule a technical consultation with our senior engineering leads. We provide candid architectural advice and transparent sprint estimates.
Start a Project in AI & ML