Most AI agent projects fail before they reach users — wrong architecture, hallucinating agents, or vendors who hand off a demo and disappear. We build production-grade AI agent systems from the ground up. Senior engineers only. Fixed scope. You own the code on delivery.
What we actually build
Custom AI agent systems — not off-the-shelf tools, not prompt wrappers. We build the full stack: multi-agent orchestration that handles complex workflows, retrieval pipelines that surface the right information, and the backend infrastructure that keeps everything running under real load. Every engagement is fixed scope with senior engineers, and you own all the code on delivery. No retainer. No dependency on us after handoff.
01 — What we build
We don’t build proof-of-concepts. Every system ships with error handling, monitoring, guardrails, and a plan for when things break.
Multiple AI agents with clear scopes, delegation logic, tool use, and human-in-the-loop checkpoints. For workflows a single prompt can’t handle.
Hybrid vector + keyword pipelines with proper chunking, re-ranking, and eval. Tuned until the right document surfaces — not the most similar embedding.
Multi-turn chatbots and voice agents with memory, guardrails, and hard failure boundaries. Built for regulated industries where “close enough” isn’t good enough.
Event-driven pipelines powered by LLMs. Document processing, lead enrichment, data extraction — replacing manual work with systems that don’t call in sick.
Auth, data pipelines, real-time processing, and APIs engineered for ML workloads. The backend your AI agent needs to not fall over at scale.
Full-stack mobile with embedded AI — voice interfaces, real-time inference, and on-device intelligence. Shipped to app stores, not left in staging.
02 — Proof of work
Not concepts. Not demos. Production AI agent systems with real clients, real metrics, and real scale.
Step 1
You explain the problem. We tell you what we’d build, what the architecture looks like, and what it would cost. No pitch, no obligation. Most people leave with a clearer picture of what they actually need even if they don’t hire us.
Step 2
We spec every deliverable before a line of code is written. You know exactly what you’re getting, the timeline, and the total cost before we start. No scope creep. No surprise invoices.
Step 3
Senior engineers only. Every system ships with documentation, monitoring, and error handling built in. Most AI agent systems are in production within 4–8 weeks. You own the code outright on delivery.
We turn down projects regularly. If your data is a mess and you haven’t cleaned it, an AI agent will confidently return wrong answers at scale — that’s worse than no agent. If you need something built in two weeks, we’re not the right call. If you want a chatbot that answers FAQs, use Intercom.
Custom AI agent development makes sense when the workflow is genuinely complex, the accuracy bar is high, and the system needs to run in production without someone babysitting it. If that’s not your situation yet, we’ll tell you on the scoping call — and point you toward what actually makes sense.
What does an AI agent development service actually deliver?
A production-ready AI agent system — not a demo or a prototype. This includes the agent architecture, orchestration logic, integrations with your existing systems, error handling, monitoring, and documentation. You receive the full codebase and own it outright. We don’t retain access or lock you into ongoing contracts.
How is this different from hiring an AI developer or using an agency?
Most agencies staff projects with junior developers supervised by a senior. We don’t. Every engagement is senior engineers only — the people scoping your project are the people building it. Fixed scope means the price you’re quoted is the price you pay. And unlike a staffing arrangement, we’re accountable for the outcome, not just the hours.
How long does it take to build a custom AI agent system?
Most production AI agent systems we build are live within 4–8 weeks from scope sign-off. Simpler single-agent systems can be faster. Complex multi-agent workflows with deep integrations take longer. We give you a realistic timeline during the scoping call — before any commitment.
What if the AI agent doesn’t perform as expected after launch?
Every system we ship includes a post-launch period where we monitor performance and fix issues that emerge from real usage. We build in guardrails, logging, and fallback logic specifically because production AI behaves differently than test environments. You’re not left alone on day one.
How much does it cost to develop a custom AI agent?
Custom AI agent development typically ranges from $15,000 to $150,000+ depending on complexity, number of agents, integrations required, and whether you need ongoing infrastructure. A single-agent system with limited integrations sits at the lower end. Multi-agent orchestration with RAG pipelines, custom tooling, and production infrastructure is toward the upper end. At CodeMint, every engagement is fixed scope — you know the total cost before we write a line of code.
What does the AI agent development process look like from first call to launch?
The process starts with a scoping call where we define the problem, the architecture, and the deliverables. From there we produce a fixed-scope proposal — specific outputs, timeline, and price. Build phase is senior engineers only, with regular check-ins. Most production AI agent systems are live within 4–8 weeks of scope sign-off. You receive the full codebase, documentation, and monitoring setup on delivery.
Who are the main types of AI agents and which one do I need?
The four main types are single-task agents (handle one defined job), multi-agent systems (multiple specialised agents coordinating on complex workflows), RAG-powered agents (retrieve and reason over your documents or data), and autonomous workflow agents (end-to-end process ownership with minimal human intervention). Most production use cases for technical founders involve multi-agent systems or RAG pipelines — or both. We’ll tell you which architecture fits your problem on the scoping call.
Can I use an AI agent development service if I already have an internal engineering team?
Yes — and it’s often the better model. Internal teams are stretched on core product. Bringing in a specialist agency for the AI layer means your engineers stay focused on what they own, and the AI system is built by people who’ve shipped production agents before. We hand over full code ownership on delivery, so your team can maintain and extend it without any dependency on us.
How do I know if my use case actually needs a custom AI agent versus an off-the-shelf tool?
If your workflow is standard — customer support, simple scheduling, basic Q&A — an off-the-shelf tool will get you 80% of the way there faster and cheaper. Custom development makes sense when your process has branching logic a generic tool can’t handle, when you need deep integration with proprietary systems, when accuracy requirements are high enough that hallucinations have real consequences, or when the competitive advantage is in the AI itself. If you’re not sure, that’s exactly what the scoping call is for — we’ll tell you honestly if you don’t need us.
Book a free 20-minute call. We’ll scope your system, give you an architecture outline, and tell you what it would cost — before any commitment.
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