AI built to scale and be flexible.
RAG pipelines, AI agents, and LLM integrations built for production — with retrieval quality, guardrails, and monitoring from takeoff.
RAG Systems
Retrieval-augmented generation pipelines that connect LLMs to your data — document ingestion, semantic chunking, embedding pipelines, and hybrid search for accurate, grounded responses.
AI Agents
Autonomous AI workflows that reason, plan, and act — tool orchestration, multi-step execution, and human-in-the-loop checkpoints for tasks that require judgment.
LLM Integration
Azure OpenAI, Azure AI Foundry, and open-source model deployment integrated into your existing applications — API design, prompt engineering, and response handling built for production traffic.
Model Orchestration
Multi-model routing, fallback chains, and cost optimization using LangChain, LangGraph, and Semantic Kernel. Route queries to the right model based on complexity, cost, and latency requirements.
Fine-Tuning & Optimization
Custom model training on your domain data when off-the-shelf models fall short. LoRA fine-tuning, evaluation benchmarks, and performance optimization for latency and cost targets.
Evaluation & Guardrails
Hallucination detection, content safety filters, response quality scoring, and citation verification built into the pipeline — not bolted on after launch.
Pipeline Architecture
Every layer a production AI system needs
Not an LLM API call wrapped in a web app. A complete pipeline from your data sources through retrieval, orchestration, and generation — with guardrails and monitoring at every stage.
Click any stage to explore its components
Data Flow
Guardrails
From Prototype to Production
AI systems built to ship to real users — with infrastructure, monitoring, and runbooks designed for production operations, not a demo that lives in a notebook.
Retrieval You Can Trust
RAG architectures designed for retrieval precision — semantic chunking strategies, relevance scoring, citation tracing, and hybrid search so your AI gives grounded answers, not hallucinated ones.
No Vendor Lock-In
Multi-model orchestration so you can route between GPT-4, Claude, Llama, or your own fine-tuned model. Switch providers or add new models without re-architecting.
Guardrails From Day One
Content safety, hallucination detection, and output validation built into the pipeline from the first sprint — not retrofitted after a production incident.
Observable AI
Prompt traces, latency metrics, token cost tracking, and quality scores — your AI system is as observable as your production API. No black-box inference calls.
Key Capabilities
- RAG pipeline architecture (ingestion, embedding, retrieval, generation)
- Agentic AI workflow design and tool orchestration
- Azure OpenAI Service and Azure AI Foundry deployment
- Multi-model orchestration (LangChain, LangGraph, Semantic Kernel)
- Vector database implementation and optimization
- Custom model fine-tuning and evaluation
- Prompt engineering and optimization frameworks
- Production monitoring and observability for AI systems
- Content safety and guardrail implementation
- ML pipeline design and CI/CD for AI models
Technologies
Engagement Models
AI Discovery Sprint
- Use case validation and feasibility assessment
- Architecture design and technology selection
- Working prototype with your real data
- Go/no-go recommendation with production estimate
Production AI Build
- Full RAG pipeline or AI agent system
- Production infrastructure (Azure or AWS)
- Evaluation framework and guardrails
- Monitoring, logging, and observability
- Handover documentation and runbook
Enterprise AI Platform
- Everything in Production Build
- Multi-model orchestration and routing
- Custom fine-tuning pipeline
- Advanced evaluation and testing suite
- Team training and knowledge transfer
- Ongoing support retainer options
Frequently Asked Questions
How do you handle hallucinations in RAG systems?
Hallucination control is built into the pipeline at multiple layers. We use semantic chunking strategies optimized for retrieval precision, hybrid search combining dense and sparse retrieval, relevance scoring with configurable thresholds, and citation tracing that links every generated statement back to source documents. The evaluation framework continuously measures retrieval quality and flags responses that lack grounding. No RAG system eliminates hallucination entirely, but a well-built pipeline reduces it to a manageable, measurable rate.
Can you work with our existing data sources and infrastructure?
Yes. The ingestion layer is designed to connect to whatever you already have — databases, document stores, APIs, SharePoint, Confluence, S3 buckets, or real-time event streams. We build connectors specific to your data landscape and handle the extraction, transformation, and embedding pipeline. The AI system deploys on your cloud infrastructure (Azure or AWS) or alongside it.
What models do you support — are we locked into one provider?
We build multi-model architectures by default. The orchestration layer can route between Azure OpenAI (GPT-4, GPT-4o), Anthropic Claude, Meta Llama, Mistral, and other open-source models — choosing the right model per query based on complexity, cost, and latency. You can switch providers or add new models without re-architecting the system. This also means you control your cost profile as model pricing evolves.
How long does it take to go from a working prototype to production?
The AI Discovery Sprint produces a working prototype in 2 weeks. From there, a production build typically takes 8–12 weeks depending on the complexity of your data sources, the number of integration points, and whether fine-tuning is involved. The production build includes infrastructure, evaluation, guardrails, monitoring, and a handover package — not just the model layer.
Do you handle model selection or do we need to decide first?
We handle model selection as part of the architecture design. During the Discovery Sprint, we evaluate candidate models against your use case — accuracy, latency, cost, and data sensitivity constraints. The production system is built with multi-model support so you are not locked into the initial choice. If a better model launches next quarter, swapping it in is a configuration change, not a rebuild.
What happens when new models are released — does our system become obsolete?
The orchestration and evaluation layers are model-agnostic by design. When a new model is released, we add it to the routing layer and run it through the evaluation suite against your existing benchmarks. If it performs better, it gets promoted. If not, nothing changes. Your data pipeline, guardrails, monitoring, and application integration remain stable regardless of which model sits underneath.
Ready to build AI that actually ships?
Book a 30-minute call. We will discuss your use case, assess feasibility, and outline what a production AI system looks like for your organization.