Twenty-eight years shipping production software. Now applied to the systems that think for themselves. We build multi-agent AI that runs in production — observable, maintainable, and worth the investment.
From architecture to deployment — we deliver working systems, not slide decks.
Design and build production multi-agent systems using the frameworks your team will actually maintain — LangGraph, AutoGen, CrewAI, OpenAI Agents SDK, or custom orchestrators built for your constraints. We architect the agent graph, memory layer, tool surfaces, and handoff logic, then deliver it running in your cloud.
Turn your documents, databases, and internal communications into something your AI can reason over. We design retrieval pipelines, select and tune embedding models, stand up vector stores, and implement re-ranking strategies that make the difference between a prototype and a system your team trusts.
Replace rigid automation with agents that handle exceptions. Email, calendar, CRM, ERP, voice, SMS, document workflows — we connect AI to your existing systems and build the guardrails that make autonomous operation safe enough to ship.
Already building something? We review your architecture, identify where the complexity will hit, and recommend the shortest path to production. Useful before you're six months into the wrong framework, or when you need a second set of eyes before a critical deployment.
Deep expertise across the full AI stack — models, frameworks, infrastructure, and integration.
Anthropic Claude, OpenAI GPT-4o / o1, Google Gemini, Meta Llama, DeepSeek, Mistral — routed by task, cost tier, and latency requirements.
LangChain / LangGraph, AutoGen, CrewAI, OpenAI Agents SDK, Semantic Kernel, Claude Code, and custom orchestrators built for production constraints.
End-to-end retrieval pipelines: Pinecone, pgvector, ChromaDB, Weaviate, and custom stores. Embedding strategy, chunking, hybrid search, and re-ranking.
MCP server development and integration — exposing internal systems, databases, and APIs as AI-consumable tool surfaces.
Session continuity, hot memory stacks, knowledge extraction from conversation transcripts, and long-term agent state management.
PII redaction pipelines, structured output validation, cost observability, audit trails, and prompt injection defense — production-safe AI.
Extract, classify, and act on contracts, invoices, compliance documents, and forms — at scale, with structured output you can rely on.
Replace brittle RPA with agents that reason about exceptions, not just execute scripts. Built with escalation paths, audit logs, and human-in-the-loop gates.
Custom AI assistants grounded in your internal knowledge base, systems, and communications — not generic chatbots trained on public data.
Production-grade conversational agents with guardrails, escalation paths, and the audit logs your compliance team requires.
Cron-driven and event-triggered agents that monitor, report, and act autonomously — across email, SMS, calendar, voice, and external APIs.
Financial data, market signals, regulatory feeds, and enterprise data sources — structured, validated, and delivered on schedule.
CDK, Lambda, DynamoDB, API Gateway, CloudFront, S3, Secrets Manager, EventBridge, SNS, SES, Route53 — infrastructure-as-code from day one.
Gmail, Google Calendar, Drive, Slack, Microsoft 365, SignalWire / Twilio, Salesforce, ServiceNow, and custom MCP server surfaces.
Secret hygiene, pre-commit gates, HMAC token derivation, IAM least-privilege, redaction pipelines, and audit-ready logging.
Python, TypeScript / JavaScript, Perl, Bash — frontend through infrastructure. We write the code and operate what we ship.
Cost attribution by model and task, latency percentiles, token tracking, failure-rate alerting, and provider failover — AI systems you can actually monitor.
AWS primary, with integrations to GCP services (Vertex AI, Gemini), Azure OpenAI, and on-premises Ollama / open-weight model deployments.
The problems clients bring us most often.
Extract structured data from PDFs, classify documents, flag clauses, and route for approval — without manual review queues.
Legacy automation that breaks on exceptions, replaced by agents that read context and decide — with a human escalation path when needed.
RAG systems over internal wikis, Slack history, email, and databases — answers grounded in your actual institutional knowledge.
Agents that monitor filings, flag changes, draft responses, and maintain audit trails — in fintech, healthcare, and legal domains.
Autonomous agents that watch your systems, diagnose anomalies, and deliver plain-English assessments — not raw log dumps.
Agents that read deal context, draft follow-ups, summarize calls, and update records — reducing the admin burden on revenue-generating staff.
Order Amid Chaos LLC has been building production software since 1998. We design, deploy, and operate agentic AI systems — multi-agent architectures with specialized agents across legal, financial, systems, and operational domains, persistent memory, autonomous workflows, and integrations spanning email, calendar, voice, SMS, and external APIs.
That practice is grounded in a long record of shipping: cloud-native architectures on AWS, Linux systems at scale, data pipelines in daily production use, enterprise applications across fintech, public-sector, and infrastructure domains. We know what makes systems fail at 2am, and we build so they don't.
The result is agentic AI that actually runs in production — observable, maintainable, and worth the investment.
We build what we use. Our own operations run on the same infrastructure we bring to clients.
Most AI projects fail in the requirements phase. We spend time mapping the actual workflow — the exceptions, the edge cases, the human decisions that matter — before proposing a solution.
Security, observability, cost controls, and failure modes are architecture decisions, not afterthoughts. We build systems that survive the real world.
We do not hand off frameworks or architectural documents. We hand off systems that are deployed, instrumented, and verified — with the knowledge transfer to keep them running.
We work in short cycles with visible progress at every step. No six-month discovery phases. If a design assumption is wrong, we find out in week two, not month six.