Off-the-shelf AI platforms like Microsoft Copilot Studio and OpenAI Assistants give you 70% of what you need, then lock you out of the last 30%. Codeora Vision builds custom agentic AI solutions for that last 30%: bespoke multi-agent systems designed around your workflows, data, and tools. Architected on LangGraph, MCP, and Claude. Owned by you, built for production, agent-native, not retrofitted.
Trusted by mid-market and enterprise teams in fintech, healthcare, and logistics.
Updated June 7, 2026
WHAT IT IS
Custom agentic AI solutions are bespoke multi-agent systems built around one business, not configured from a SaaS product. The agents plan, use tools, and act in a loop to complete real work, not just answer prompts. Codeora Vision architects them on LangGraph, MCP, and Claude, with a RAG knowledge layer, evaluation, and observability built in.
First-mention enrichments: agentic AI (systems that plan and take actions in a loop, not single-shot answers) · multi-agent system (a supervisor agent coordinating specialist worker agents) · MCP (Model Context Protocol, a standard way to give an agent governed access to your tools) · RAG (retrieval that grounds answers in your own data).
Per McKinsey's Superagency in the Workplace report, 92% of companies plan to invest in AI, yet only 1% consider themselves mature.
That gap is the distance between a demo and a system you run in production. Closing it is exactly what custom agentic work is for.
THE PROBLEM
Platforms are built for the common 70%. Microsoft Copilot Studio, OpenAI Assistants, and Moveworks cover the cases everyone shares, fast. The trouble is the last 30%: your specific workflow, your proprietary data, your integration surface, your compliance rules. That 30% is usually where the value and the differentiation live. It is also exactly what a configurable product cannot reach.
A Make.com template, a Zapier zap, a GoHighLevel snapshot, or a ChatGPT custom GPT is fine for a quick internal task. None of them is a production system you own, can audit, and can rely on when it sits in the middle of your operation.
AGENT-NATIVE ARCHITECTURE
Codeora Vision designs every system around control, grounding, and measurement. State and branching run on LangGraph, not linear chains, so complex logic stays inspectable as it grows. Tools connect through MCP. Reasoning runs on Claude or GPT-4o. Every consequential action passes a guardrail or a human-in-the-loop checkpoint.
A supervisor agent plans and delegates to worker agents, each scoped to one job. We build the graph on LangGraph, with CrewAI, AutoGen, or OpenAI Swarm where the pattern fits.
Agents act through MCP and function calling, so tool access is explicit, logged, and revocable, instead of hidden in prompt glue.
A RAG layer over Pinecone, Weaviate, or Supabase pgvector keeps answers tied to your real data, with re-ranking to cut noise.
We measure agents on your real cases with LangSmith and Langfuse, add guardrails and hallucination mitigation, and keep human-in-the-loop on high-stakes steps.
Why LangGraph over plain LangChain? Chains run a fixed sequence. A graph holds state, loops, and branches, which is what real agent work needs. We still use LangChain components inside it.
TECHNICAL DEEP-DIVE
Bring your workflow, your stack, and your constraints. In a 30-minute technical discovery we will sketch the agent topology, the integration surface, and the evaluation plan, and tell you honestly whether custom is the right call or an off-the-shelf tool would do. NDA from minute one.
OUR STACK
This is where Tier 3 buyers test whether an agency actually builds. We are model- and framework-agnostic and choose per project, against latency, cost, governance, and ownership.
AGENT FRAMEWORKS
ORCHESTRATION & PROTOCOL
FOUNDATION MODELS
RETRIEVAL & DATA
BUILD & DEPLOY
EVAL & GOVERNANCE
Models change every quarter. We design so the reasoning layer is swappable: fine-tuning, prompt engineering, and context-window strategy are decisions we revisit, not lock in.
HOW WE BUILD
Under NDA from minute one, we map your workflows, data, integrations, and constraints, and define what "done" means in measurable terms.
We design the agent topology (supervisor and workers), the tool and MCP layer, the RAG sources, and the evaluation plan, and review it with you before any build.
We build in Python with LangGraph and LangChain, wire integrations through REST APIs and webhooks, and stand the system up in Docker on your chosen cloud or self-hosted.
We score the agents on your real cases with LangSmith and Langfuse, tune prompts and retrieval, and add guardrails until the numbers hold.
We ship to production with human-in-the-loop on consequential actions, monitoring on, and rollback ready.
Optional retainer for continuous evaluation, model upgrades, and iteration as your workflows change.
We are selective on purpose. Custom agentic work pays off for a specific buyer and wastes money for everyone else. Here is the honest line.
This is for you if
This is not for you if
If your need is templated, that is a compliment to the off-the-shelf market, and the right move is one of our templated AI automation services, not this. Custom is for the work nothing off-the-shelf can reach.
TRANSPARENT PRICING
We publish the range, and we publish the floor. Custom agentic projects start at $40,000. Below that, an off-the-shelf tool or a templated build is the honest answer, and we will tell you so.
One production agent, tool use, RAG, one integration surface, evaluation
OPTIONAL $5,000/MO
Scope this buildSupervisor and worker agents, multi-source RAG, observability dashboard, guardrails
OPTIONAL $7,500/MO
Scope this buildMulti-workflow platform, self-hosted or cloud, governance, continuous evaluation
OPTIONAL $10,000/MO
Talk to an architectThe retainer is optional, not a lock-in. You own the system at handover. We stay on only if continuous evaluation and iteration are worth it to you.
START
You are not shopping for a template. You are evaluating who can architect a system you will run for years. Book the technical discovery, and within the call you will know whether we are the right builder for it.
PROOF
CONTEXT
A mid-market fintech (Series B, $20M–$40M ARR) drowning in manual document review
WHAT WE BUILT
A multi-agent document-processing system on LangGraph and Claude, with RAG over their policy corpus in Weaviate and human-in-the-loop sign-off
OUTCOME
Review that took analysts hours now drafts in minutes, with every decision traceable to a cited source
CONTEXT
A national healthcare network needing internal knowledge access without exposing PHI
WHAT WE BUILT
A self-hosted internal copilot on open-source LLMs and Supabase pgvector, with PII redaction and HIPAA-scoped guardrails
OUTCOME
Staff get grounded answers from policy and clinical docs, with data residency kept inside their own cloud
CONTEXT
A logistics platform with a brittle chain of Zapier zaps holding operations together
WHAT WE BUILT
A supervisor-and-worker agent system on LangGraph and MCP, integrated through REST APIs, with evaluation in LangSmith
OUTCOME
A fragile automation became a monitored production system the team owns and can extend
Each outcome is a single anonymized engagement and not a guarantee. Architecture and entities are real; identifying details are not.
FAQ
Custom AI solutions are AI systems built around one business's workflows, data, and tools, not a configurable SaaS product. At the bespoke end, that means custom agentic AI: multi-agent systems that plan, use tools, and act in production. Codeora Vision architects them on LangGraph, MCP, and Claude with RAG, evaluation, and observability. Per McKinsey's January 2025 Superagency report, 92% of companies plan to invest in AI but only 1% are mature.
Off-the-shelf AI like Microsoft Copilot Studio or OpenAI Assistants covers common cases fast, then stops at the edges of your workflow. Custom AI is built for those edges: your integrations, your data, your rules. With off-the-shelf you rent a capability and accept its ceiling. With a custom agentic system you own one designed for your last 30%, deployable self-hosted or on AWS Bedrock and Azure OpenAI.
Codeora Vision custom agentic projects start at $40,000 and run to $80,000 or more, plus an optional $5,000 to $10,000 per month retainer for monitoring and iteration. A single production agent sits at the floor. A multi-agent platform sits at the top. We do not take sub-$40,000 work, because below that an off-the-shelf tool or a templated service is the honest answer.
A single production agent typically takes six to ten weeks from discovery to deploy. A multi-agent system with RAG over several sources and an evaluation harness runs three to four months. We work in phases: discovery, architecture, build, evaluation, deploy, maintain. Evaluation is not optional, because an agent that has not been measured on your real cases is a prototype, not a system.
Agentic AI describes systems that plan, choose actions, and use tools in a loop to reach a goal, not just answer a single prompt. An agent can read a record, call an API, check the result, and decide the next step. Multi-agent systems split that across a supervisor and worker agents. We build them with LangGraph for control, MCP for tools, and Claude for reasoning.
A chatbot converses and answers. An agent acts: multi-step tasks, tool use through function calling, state across steps, and work completed with limited supervision. A chatbot tells you an order status. An agent investigates the order, issues the refund, updates the CRM, and emails the customer. We build agents as multi-agent systems on LangGraph and Claude, with guardrails and evaluation.
Yes, integration is the reason to build custom. We connect agents to your systems through REST APIs and webhooks, and increasingly through MCP, which gives an agent a governed way to use your tools. We build the layer in Python with LangChain and FastAPI, deploy in Docker on AWS Bedrock, Azure OpenAI, or self-hosted, and keep human-in-the-loop approval on anything that changes your data.
Use SaaS when the need is common and a configurable product reaches most of the job. Build custom when the last 30% is where the value is: deep integration, proprietary data, complex workflows, ownership, or reliability a vendor will not guarantee. A Make.com template or a custom GPT is not a moat. If AI is core to how you operate, a custom agentic system is the build, typically at $40,000 and up.
DALLAS, TEXAS — RESPONDING IN < 4 HOURS
Bring the workflow nothing off-the-shelf can handle. In a technical discovery we will architect the agent system on LangGraph and Claude, scope it honestly against the $40,000 floor, and tell you if custom is right. You own what we build.