Most AI automation services are retrofit jobs. GPT gets wired into Zapier, Make.com flows gain a prompt node, and the stack breaks under real branching logic. Codeora Vision builds the opposite: agent-native, not retrofitted. Every system runs on LangGraph, MCP, and Claude, answering inbound calls, qualifying leads, and syncing to a CRM, then improving through evals. The result is production-grade from the first deployment, serving teams across the US, UK, EU, Canada, and Australia.
Trusted by a top-5 US dental DSO, a Fortune 500 healthcare system, and a national multi-state property management firm.
Updated June 4, 2026
INTEGRATIONS WE SHIP WITH
WHAT WE DO
AI automation services are done-for-you systems that run business workflows autonomously, not DIY tools a team wires together. Per McKinsey's January 2025 Superagency in the Workplace report, 92% of companies are scaling AI investment, yet only 1% have reached maturity. Codeora Vision closes that gap with agent-native systems built on LangGraph, MCP, and Claude.
1%
of companies reach AI maturity
McKinsey, 2025
$80B
projected contact center savings from AI
Gartner, 2024
80%+
autonomous resolution
Klarna, 2024
21×
conversion at a sub-5-minute response
NAR, 2024
AGENT-NATIVE ARCHITECTURE
Most platforms sell connectors and leave you to babysit them. We build agent-native systems on LangGraph, MCP, and Claude, where an orchestration layer runs the work. LangGraph treats decision branching as the primary job, so our voice and SDR builds survive past the fourth conditional. Evals and LangSmith observability catch regressions in testing, not in production.
MODELS — Claude (Sonnet, Opus) · GPT-4o · Gemini · Llama · Mistral
FRAMEWORKS — LangGraph · LangChain · LlamaIndex · MCP · LangSmith · n8n
NEXT STEP
Pick the process that costs you the most right now, an unanswered phone, a slow inbox, a stalled pipeline. We map it in a 30-minute architecture review under NDA, then show you the agent-native rebuild path on LangGraph, MCP, and Claude. You leave with a plan you can act on, with us or without us.
WHAT WE BUILD
Every capability is its own system, never grouped into vague buckets. Pick the workflow that is costing you the most.
A 24/7 receptionist that answers inbound calls, books appointments, and routes overflow, so no after-hours call hits voicemail.
Vapi · Retell AI · Twilio
AI ReceptionistInbound and outbound voice agents for reminders, qualification, and follow-up, natural on the line and on-script under load.
Vapi · Retell · ElevenLabs
AI Voice AgentsWeb, WhatsApp, and SMS chatbots for storefront and support that resolve tickets instead of deflecting them into a queue.
Gorgias · Zendesk · Shopify
AI Chatbot DevelopmentCustom-built agentic AI solutions for workflows off-the-shelf tools cannot touch, engineered on LangGraph, MCP, and Claude.
LangGraph · MCP · Claude
Custom AI SolutionsAgentic workflow automation that orchestrates multi-step processes across your tools, holding state and memory a flow cannot.
n8n · LangGraph · Temporal
Workflow AutomationRetrieval-augmented generation that grounds answers in your own documents, so the system cites your knowledge instead of guessing.
Pinecone · Weaviate · pgvector
RAG & Knowledge SystemsAutonomous SDR agents that research, reach out, qualify, and book meetings, then sync everything to your pipeline.
HubSpot · Salesforce · Apollo
AI SDR & Lead GenerationSTILL DECIDING
Start with the diagnostic, not the sales pitch. In a 30-minute architecture review we map your stack, your integrations, and where automation pays back fastest, then point you to the right system. NDA from the first minute, no obligation, no pressure to choose today.
WHERE WE DEPLOY
We ship the same agent-native AI automation services into six verticals, each with its own integrations and its own compliance line. Depth lives on the industry pages.
NOT A FIT?
The architecture is the same whether you run a clinic, a brokerage, or a SaaS support desk. If your workflow involves calls, intake, retrieval, or outreach, we can build for it. Bring the process, we will map the system.
HOW WE BUILD
Every Codeora Vision engagement runs through The ORACLE Build Method™, a four-phase process: Observe, Reason, Architect, and Calibrate and Evolve. It opens with a 30-minute architecture review under NDA and closes with a 90-day calibration window. Architecture on LangGraph, MCP, and Claude is designed before a single line ships.
Current-state diagnostic under NDA: stack, integrations, and the workflow with the fastest payback.
Architecture design on LangGraph, MCP, and Claude, with the eval plan defined up front.
Build with automated test suites and LangSmith observability, then deploy to production.
90-day calibration window, continuous monitoring, and a retraining cadence.
FAQ
An AI automation agency designs, builds, and maintains AI systems that run your workflows, instead of selling DIY tools. Codeora Vision builds agent-native AI automation services on LangGraph, MCP, and Claude, including receptionists, voice agents, chatbots, and SDR pipelines. Per McKinsey's January 2025 report, only 1% of companies reach AI maturity, so the work is production engineering.
Traditional automation follows fixed if-this-then-that rules and breaks when reality does not match the rule. AI automation uses agents that reason, read context, and adapt. Codeora Vision builds on LangGraph, which treats decision branching as the primary job, so the system handles edge cases instead of failing on them.
An AI automation platform is software you operate yourself, such as Zapier or Make.com. AI automation services are done-for-you systems built and maintained for you. Codeora Vision ships custom systems on LangGraph, MCP, and Claude, with evals and LangSmith observability, and you own the result instead of renting per-task runs.
Zapier and Make.com handle linear tasks well, like moving a form entry into a spreadsheet. They struggle once a workflow needs judgment, memory, or many conditional branches. If your process involves answering calls or qualifying leads, an agent-native system handles reasoning a flow cannot. Klarna's 2024 benchmark reported 80%+ autonomous resolution.
Every engagement runs through The ORACLE Build Method™: Observe, Reason, Architect, and Calibrate and Evolve. It starts with a 30-minute architecture review under NDA, where we map your stack and find the fastest payback. We then design on LangGraph, MCP, and Claude, build with evals, and run a 90-day calibration window.
Timelines depend on scope. A single-channel system such as an AI receptionist or chatbot typically ships in two to four weeks. Multi-agent voice or SDR builds with deep CRM, ERP, or EHR integration usually run six to ten weeks. Every project includes a 90-day calibration window plus continuous monitoring.
Pricing is published per service, never hidden behind a quote. Productized systems run $2,500 to $5,000 setup plus $599 to $999 per month flat. Higher-complexity work such as custom AI solutions, RAG systems, and AI SDR runs $25K to $80K per project plus an optional $2K to $5K monthly retainer. Each service page lists its range.
Return shows up as recovered revenue and reclaimed hours. Gartner forecasts $80B in annual contact center labor savings from AI. In one anonymized deployment, a litigation firm cut weekly document retrieval from 12 hours to under 2 hours using a Codeora Vision RAG system on Pinecone. We model the math during the architecture review.
DALLAS, TEXAS — RESPONDING IN < 4 HOURS
Pick the workflow that costs you the most and let us map the rebuild. The first step is a 30-minute architecture review under NDA, where you see exactly how an agent-native system would run the work. No retrofit, no guesswork. You leave with a build path you can act on.
Codeora Vision is headquartered in Dallas, Texas, building AI automation services for teams across the US, UK, EU, Canada, and Australia. Updated June 4, 2026