Deploy AI applications and automate business processes
We deploy dedicated private models and Retrieval-Augmented Generation (RAG) architectures inside your isolated cloud perimeter, governed by strict zero-data-retention standards.
Deploying external consumer AI tools without architectural boundaries introduces unnecessary data exposure risks. We engineer dedicated private retrieval architectures within your isolated VPC perimeter: indexing internal corporate documentation, enforcing role-based access permissions, and providing fast, grounded answers without transmitting data outside your environment.
Deploy secure, enterprise-grade AI applications, private LLM inference, and Retrieval-Augmented Generation (RAG) within dedicated cloud perimeters.
Pasting confidential corporate data into public consumer AI models, unvectorized document dumps, lack of hallucination evaluation benchmarks, and unmonitored GPU costs.
Assess your organization's preparation before initiating services in AI Engineering, Machine Learning & Automation:
Pragmatic AI roadmaps. Separate transformative commercial reality from expensive hype.
Identify where artificial intelligence can solve a real business problem and determine what it would take to implement it. We assess feasibility, data needs, risks, costs, and measurable outcomes.
Build applications that use AI models to support a defined task or user experience. Proven engineering baselines with zero-downtime execution.
Build applications that use AI models to support a defined task or user experience. Work can include model integration, data flow, user interfaces, evaluation, and deployment.
Custom generative AI applications with strict guardrails, prompt caching, and evaluation harnesses.
Build systems that use generative models to create or transform text, images, code, or other supported content. We design the workflow, controls, evaluation, and integration around your use case.
Integrate proprietary and open-source LLMs into your product with sub-second response times.
Connect an application or business workflow to a large language model. We handle prompts, context, output validation, permissions, latency, and cost considerations.
Build autonomous multi-agent systems with tool calling, memory stores, and safety constraints.
Build AI-driven workflows that can select tools and perform bounded tasks toward a goal. We define permissions, guardrails, approval points, and evaluation so actions remain appropriately controlled.
Use AI within a defined business process to reduce repetitive manual work. Proven engineering baselines with zero-downtime execution.
Use AI within a defined business process to reduce repetitive manual work. We connect models with existing systems and include validation and human review where the task requires it.
Ground AI responses in your verified company data using hybrid vector search and semantic chunking.
Connect an AI model to selected reference material so answers can use relevant information from your documents or data. We build retrieval, permissions, source handling, and evaluation into the system.
Deploy and scale Pinecone, Qdrant, Milvus, and pgvector for sub-50ms similarity search.
Set up storage and search for vector representations used in semantic search and AI retrieval. We help choose a suitable system and configure indexing, access, and integration.
End-to-end ML architectures from feature stores to automated model training and evaluation.
Build the data, training, evaluation, and deployment workflows needed to use machine learning in production. We focus on making models reproducible, testable, and maintainable.
Serve production models on autoscaling GPU and CPU endpoints with continuous batching.
Put a trained machine learning model into a usable environment, such as an API or batch process. We address packaging, scaling, monitoring, versioning, and rollback.
Automated model deployment pipelines with versioning, drift detection, and automated rollback.
Build tools and workflows for managing machine learning data, experiments, models, and deployments. MLOps improves repeatability and coordination between data science and engineering.
Track token throughput, latency percentiles, model drift, and response quality metrics in real time.
Monitor and manage language-model applications in production. This can include latency, cost, output quality, errors, model versions, and safety-related evaluation.
Autoscaling GPU clusters configured for intensive machine learning and 3D rendering jobs.
Configure cloud computing resources with GPUs for workloads such as AI inference, machine learning, or accelerated processing. We help match capacity and configuration to the workload.
Improve the speed, cost, or resource efficiency of running AI models to produce outputs. Proven engineering baselines with zero-downtime execution.
Improve the speed, cost, or resource efficiency of running AI models to produce outputs. Techniques depend on the model and may include batching, caching, quantization, or hardware selection.
Connect an application to an external AI service through its API. Proven engineering baselines with zero-downtime execution.
Connect an application to an external AI service through its API. We manage request handling, credentials, output processing, error paths, and usage controls.
Extract structured data from PDFs, invoices, and scans using multimodal AI pipelines.
Use automation and AI to extract, classify, summarize, or validate information in documents. We design review and exception handling for cases that need human attention.
Automate data pipeline execution with Apache Airflow, dbt, and automated data quality tests.
Automate recurring data collection, transformation, validation, and delivery tasks. This reduces manual work and makes data processing more consistent.
Automate repetitive steps across business applications and workflows. Proven engineering baselines with zero-downtime execution.
Automate repetitive steps across business applications and workflows. We map the process, connect the required systems, and add checks for exceptions and approvals.
Address risks related to AI data access, model use, outputs, and automated actions. Proven engineering baselines with zero-downtime execution.
Address risks related to AI data access, model use, outputs, and automated actions. We help define controls, evaluation practices, monitoring, and accountability appropriate to the application.
Speak directly with a Principal Cloud Architect. We will review your infrastructure requirements and deliver a written Statement of Work.
