We don't just automate — we build autonomous AI systems. From Hermes-style reasoning engines to OpenClaw-like agent frameworks, we create LLM-powered agents that understand context, make decisions, and execute tasks without human intervention.
94%
Decision Accuracy
10x
Faster Processing
24/7
Autonomous Operation
Agent Response Time
<2s
From reasoning engines to autonomous agents — we architect AI systems that solve real business problems
Multi-step reasoning systems that break down complex problems, evaluate options, and arrive at optimal decisions — like Hermes-style chain-of-thought processing.
Context-aware chatbots and virtual assistants that maintain conversation state, access knowledge bases, and execute actions through natural language.
Retrieval-Augmented Generation that grounds AI responses in your documents, databases, and real-time data — eliminating hallucinations and ensuring accuracy.
Self-directed agents that monitor conditions, make decisions, and execute multi-step workflows without human oversight — from lead qualification to incident response.
Image and video analysis systems for quality control, document processing, and visual inspection — integrated with your existing workflows.
AI models that forecast outcomes, identify patterns, and recommend actions — from churn prediction to demand forecasting and risk assessment.
We work with the best AI models and frameworks — choosing the right tool for your specific use case
GPT-4, GPT-4o, Assistants API, function calling
Claude 3, Claude 3.5 Sonnet, computer use capabilities
Open-source models for on-premise, private deployment
Vector databases for RAG and semantic search
Real-world AI systems we've built for clients
Built a conversational AI agent integrated with the company's knowledge base, CRM, and ticketing system. The agent handles 85% of Tier-1 support inquiries autonomously, escalating complex issues to human agents with full context.
85%
Tickets Resolved
<2s
Response Time
4.8/5
CSAT Score
Implemented a RAG system that processes legal documents, extracts key terms, compares against precedent database, and flags risks — reducing contract review time from 4 hours to 20 minutes.
90%
Time Reduction
99.2%
Accuracy Rate
10,000+
Docs Processed
From concept to deployment — how we build reliable AI systems
Understand your use case, data sources, and success criteria. Define what "good" looks like for your AI system.
Design the system architecture — model selection, data pipeline, integration points, and safety guardrails.
Build the AI system with iterative testing, prompt engineering, and fine-tuning for your specific domain.
Production deployment with monitoring, logging, and continuous improvement based on real-world performance.
We combine deep technical expertise with business pragmatism
We don't build demos — we deploy systems that handle real load, with error handling, monitoring, and graceful degradation.
We can deploy on-premise or in your VPC, use private models, and ensure your data never leaves your control.
You work directly with the AI engineers building your system — no account managers, no lost context, no surprises.
Tell us what you're trying to solve. We'll design an AI system that actually works — not a prototype that never ships.
Common questions about AI agent development
Traditional automation follows fixed rules: "If X happens, do Y." An AI agent reasons: "Given the situation, what's the best action?" AI agents use LLMs to understand context, evaluate options, and make decisions — handling edge cases and exceptions that would break rule-based systems. For example, a rule-based system might route all high-value leads to sales. An AI agent reads the lead's message, researches the company, checks inventory, and decides whether to route to sales, schedule a demo, or send a nurture sequence — all autonomously.
AI agent projects typically range from $15,000 for a focused single-purpose agent (like a support chatbot) to $75,000+ for complex multi-agent systems with reasoning, memory, and tool use. Costs depend on: complexity of reasoning required, number of integrations, data volume and preprocessing needs, model choice (OpenAI API vs. open-source vs. fine-tuned), and deployment requirements (cloud vs. on-premise). We provide fixed-price quotes after the discovery phase so there are no surprises.
Absolutely — integration is our specialty. We build agents that connect to your CRM (Salesforce, HubSpot), ERP (NetSuite, SAP), databases, APIs, and internal tools through function calling and API integration. The agent can read from and write to your systems, making it a true team member rather than a standalone chatbot. We've integrated AI agents with NetSuite for inventory-aware sales recommendations, with HubSpot for intelligent lead scoring, and with custom internal tools for operations teams.
We use multiple techniques: RAG (Retrieval-Augmented Generation) grounds responses in your documents and data, not the model's training data. Source attribution shows where information came from. Confidence scoring flags uncertain responses for human review. Structured output (JSON mode) forces the model to respond in predictable formats. For critical decisions, we implement human-in-the-loop checkpoints where the agent proposes actions but waits for approval. Our systems typically achieve 94-99% accuracy, with full audit trails of every decision.
You have full flexibility. We work with OpenAI (GPT-4, GPT-4o), Anthropic (Claude 3), and open-source models (Llama 3, Mistral, Mixtral) that can run on your infrastructure. For sensitive data or regulatory requirements, we recommend open-source models deployed in your VPC or on-premise — your data never leaves your environment. We can also fine-tune open-source models on your proprietary data for domain-specific performance that matches or exceeds general-purpose APIs at lower cost.
A focused single-purpose agent (like a customer support bot with 50 FAQs) can be deployed in 2-3 weeks. A complex multi-agent system with reasoning, memory, and multiple integrations typically takes 8-12 weeks. The timeline depends on: data preparation and cleanup, integration complexity, testing and refinement cycles, and your review/feedback speed. We work in 2-week sprints with working demos at each milestone — you'll see progress from week one, not a black-box reveal at the end.