AI Agents That Actually Work in Production
We build LangGraph multi-agent pipelines, RAG knowledge bases, custom chatbots, and fine-tuned language models that deploy to production and keep running. No demos. No prototypes handed over in a Jupyter notebook.
Example Agent Flow
Industries and Use Cases We Have Deployed
Insurance Claims Processing
80% faster processing
Legal Document Research
2 hrs to 4 min per query
Sales Lead Qualification
31 hrs to 4 min response
Customer Support Automation
70% ticket deflection
Financial Report Generation
15 hrs per week saved
Medical Records Summarization
95% accuracy vs manual
E-Commerce Product Recommendations
45% higher order value
Contract Review and Redlining
10x faster review cycle
Six AI System Types We Deploy
Each one purpose-built for production, not proof-of-concept.
Multi-Agent Pipelines
LangGraph orchestration where each agent owns one job, passes clean structured output to the next, and the whole chain runs without a human touching it.
- Parallel and sequential agent coordination
- Shared memory and state between agents
- Automatic retry and fallback logic
- Works with OpenAI, Anthropic, or open-source
RAG Systems and Knowledge Bases
Your AI stops making things up. It searches your actual documents, cites its sources, and stays accurate as your data changes.
- Ingest PDFs, docs, databases, or web pages
- Semantic search with metadata filtering
- Source citations on every answer
- Auto-sync when documents update
Customer-Facing Chatbots
Not a simple FAQ bot. A chatbot that qualifies leads, books appointments, handles tier-1 support, and knows when to escalate to a human.
- Conversational lead qualification
- Calendar and CRM integration
- Handoff to human with full context
- Embeds on any website in minutes
LLM and SLM Fine-Tuning
Off-the-shelf models do not know your domain. We fine-tune models on your data so they speak your language, follow your format, and hallucinate far less.
- Supervised fine-tuning on proprietary data
- RLHF-style preference alignment
- Domain vocabulary adaptation
- Your model, your infrastructure
Vector Database Architecture
Choosing the wrong vector DB at the start costs you months later. We design the right schema, ingestion pipeline, and query strategy for your scale.
- Pinecone, Chroma, Milvus, or pgvector
- Embedding strategy per content type
- Hybrid search (semantic + keyword)
- Incremental indexing pipelines
Human-in-the-Loop Dashboards
When the AI is not confident enough, it pauses and asks a human. The state is saved so it picks up exactly where it left off after the human decides.
- Confidence threshold routing
- Persistent state across pause/resume
- Full audit trail of every decision
- Next.js dashboard, customized to your workflow
From Discovery to Production
Discovery and Workflow Mapping
We interview your team, map the exact process the AI will replace or augment, identify edge cases, and define success metrics before writing a single line of code.
Architecture and Model Selection
We design the agent topology, select the right LLM or SLM for each role, define the vector schema, and present an infrastructure diagram for your sign-off.
Pipeline Development and Integration
We build the agents, connect your data sources, integrate with your existing tools via API, and set up the evaluation harness with golden-set test cases.
Evaluation and Safety Testing
DeepEval or custom evaluation frameworks run against real data. We measure accuracy, hallucination rate, latency, and cost per inference before touching production.
Production Deployment
Containerized deployment with horizontal autoscaling, health checks, secret management, and GitOps CI/CD. You get a deployed system, not a Jupyter notebook.
Monitoring and Continuous Improvement
Grafana dashboards track model performance, cost per call, queue depth, and error rates. We alert before users notice problems and iterate based on real production data.
Our AI Stack
Tools we use daily in production AI systems.
Frequently Asked Questions
What is the difference between an AI chatbot and an AI agent?+
Do I need my own OpenAI or Anthropic API keys?+
How do you prevent AI hallucinations in business-critical systems?+
Can you fine-tune a model on our proprietary data?+
How long does it take to build and deploy an AI agent?+
What happens after the AI system is deployed?+
Let Us Audit Your Biggest Manual Workflow
In a free 30-minute call we will identify the one AI integration that would have the largest impact on your team and give you a rough implementation roadmap.