AI Agents & Chatbots, Engineered to Reach Production
We design, build, and govern custom AI agents and chatbots that don't stall in a demo. They go live, integrate with your systems, and stay accountable for the outcome.
Most AI agents never make it past the pilot. We build the ones that do, from "we should automate this" to a production agent your team and customers can rely on. One accountable partner, a dedicated AI engineering team. No outsourcing, no agent-washing.
An agent doesn't just answer, it reasons across your systems and closes the loop.
TRUSTED BY TEAMS THAT SHIP
Click any platform to read verified customer reviews.
01
The 2026 reality
The hard truth about AI agents: most don't survive production
The hype is real, and so is the failure rate. We built our practice around the opposite outcome.
McKinsey finds that while around 62% of organizations are experimenting with AI agents, fewer than 25% have scaled even one to production. Industry surveys report roughly 74% of enterprises have rolled back at least one agent after launch*, and Gartner projects that more than 40% of agentic-AI projects will be cancelled by the end of 2027, usually because the costs, risks, and integration work were underestimated.
The reasons are consistent, and they're fixable. The leading causes of rollback aren't exotic: exposed customer data and hallucination or brand risk, followed by shaky integrations and unclear ROI. Hallucination in particular is mostly a context-and-governance problem, not a model problem, agents invent answers when they lack governed access to your specific business data.
There's also a quieter problem: agent-washing. Gartner reviewed thousands of vendors marketing an "AI agent" and found only about 130 were verifiably agentic. A lot of what's sold as an "agent" is a search bar in a costume.
Every engagement we take is scoped to clear the bar that kills most projects, the right tool for the job, governed from day one, integrated for real, and measured against a success metric we agree before we write code.
From experiment to production
Experimenting with agents~62%
Scaled to production<25%
Leading causes of rollback *
Exposed PII / customer data~31%
Hallucination / brand risk~22%
Sources: McKinsey (adoption & scaling); industry surveys (rollback rates & causes); Gartner (agent-washing, project cancellations). *Figures from secondary reporting, see the trust section.
<0%of agents reach production (McKinsey)
~0%rolled back an agent post-launch *
~0of thousands of "agent" vendors are verifiably agentic (Gartner)
AI agents and chatbots that answer your customers and act in your systems
We build conversational chatbots, autonomous agents, and the channels and care around them, each grounded in your own content. Pick the capability that fits your goal to see how we build it.
AI agent monitoring and optimization: evals, guardrails, knowledge updates, and model migrations that keep a live agent accurate.
03
Clarity
Chatbot or AI agent? We build the right one, and we'll tell you which
The terms get used interchangeably; the cost and capability are not. A chatbot resolves the conversation. An agent resolves the problem, it reasons about context, acts across your systems, and closes the loop.
Resolves the problem. An agent reads, decides, and writes, completing a multi-step task across your systems without a human doing the copy-paste in the middle.
Resolves the conversation. A chatbot understands the question and answers from a knowledge base, mostly read-only, single-step. The smart spend when the work only needs an answer.
How a chatbot and an AI agent differ, at a glance.
AI Chatbot
AI Agent
What it does
Understands and answers
Understands, decides, and acts
Scope
Single-step, conversational
Multi-step, cross-system, goal-driven
Touches your systems
Read-only (knowledge base)
Reads and writes (CRM, ERP, APIs)
Best for
FAQs, lookups, low-risk linear tasks
Onboarding, refunds, triage, workflows
Typical build
~2–8 weeks
~8–16+ weeks
Run-cost & governance
Lower
Higher, needs real guardrails
If the work only needs an answer
A well-built RAG chatbot is the smart spend, cheaper to build and run, and faster to ship.
If it spans systems or needs follow-through
If the task depends on context, crosses two systems, or replaces copy-paste work, you need a true agent, governed accordingly.
Every buyer is one of two people: starting from zero, or fixing what already exists. We're experts at both, and we'll meet you wherever you are.
IMG 01 · BUILD · 16:10
Founder starting fresh at a clean desk, a blank screen ready for a new agent
Build new
Start from a blank page
You've identified the workflow but have nothing live yet. We start with discovery, score the use case honestly (build, buy, or partner), design the conversation and the architecture, and ship a governed agent.
Engineer reviving a stalled chatbot, screen showing a fixed, working conversation
Rescue & re-automate
Fix the bot you already have
You have a chatbot that's stale, a script that breaks, or an agent that quietly got rolled back because it hallucinated, leaked data, or never integrated properly. We audit what you've got and rebuild it to production standard.
Each door implies its own starting audit, a workflow-and-data audit for a new build, a diagnostic audit of the live system for a rescue, but it's one expertise expressed two ways, not two separate services.
05
How we build
How we build agents that actually ship
Our process is engineered against the exact reasons agents fail. Every stage has a gate; nothing reaches production that can't be measured, governed, and rolled back.
IMG 03 · PROCESS · 3:2
Engineering team at a whiteboard mapping an agent's decision flow, sticky notes and diagrams
1
Free audit & discovery
~1–2 weeks
We map the target workflow, define the single success metric, score build/buy/partner, check your data readiness, and decide, on evidence, whether you need a chatbot, an agent, or a multi-agent system.
Gate: go / no-go on evidence
2
Architecture & scope
~1–2 weeks
Model selection (Claude, GPT, or Gemini, chosen for the task, not the brochure), framework, RAG/knowledge design, the integration map for your CRM/ERP/helpdesk, and a governance tier matched to the agent's autonomy.
Gate: signed-off architecture
3
Design & build
~3–10+ weeks
Conversation and persona design, the retrieval pipeline, tool and system integrations (built on open standards like MCP so connections are portable), guardrails, and an evaluation harness, all in a sandbox before anything touches live data.
Gate: sandbox before live data
4
Evaluate & harden
ongoing through build
Adversarial and accuracy testing, hallucination and prompt-injection defenses, least-privilege access per agent, full audit trails, human-in-the-loop gates for high-stakes actions, and circuit breakers that halt the agent on threshold violations.
Gate: must clear accuracy thresholds
5
Phased launch
~1–3 weeks
We launch to a pilot, watch real conversations, then expand on measured results, never a big-bang switch-on. Observability and monitoring ship with the agent, not after the first incident.
Gate: expand only on measured results
6
Optimize & support
ongoing
Continuous tuning, token-and-infrastructure cost optimization, and scaling as volume grows. Most of an agent's lifetime cost lives here, so we plan for it on day one.
Gate: tuned to a live run-rate
You see a live pilot early rather than waiting for a big-bang launch, and you can switch the agent off cleanly at any gate.
Our stack, chosen for your problem, not our preferences
We're deliberately model-agnostic and framework-fluent. The right architecture depends on your latency, accuracy, compliance, and budget, so we choose from the full modern toolkit instead of forcing every problem onto one vendor. Hover or tap a layer for detail.
We build on Anthropic's models as a partner, not a dependency. We work across Claude, GPT, and Gemini, each chosen per use case for reasoning quality, safety behavior, latency, and cost. We use what wins for you.
LangGraph for stateful, regulated workflows; CrewAI for role-based teams; the OpenAI and Claude Agent SDKs, Microsoft's Agent Framework, and Google's ADK where each fits. Four orchestration patterns that actually ship: graph, role-based, hand-off, and supervisor.
Retrieval-augmented generation grounded in your documents and data, with governed memory so the agent answers from your truth, and cites it, instead of guessing.
We connect agents to your CRM, ERP, helpdesk, and data warehouse using the Model Context Protocol (MCP), the open, now-industry-standard tool-connection layer, so integrations are portable and don't lock you to one vendor.
Low-latency speech-to-text and text-to-speech for natural inbound and outbound voice agents, with the same governance as text.
Eval harnesses, red-teaming, and monitoring anchored to recognized frameworks (NIST AI RMF, OWASP GenAI Security) so quality is measured, not assumed.
Orchestration, a supervisor coordinating specialist agents
We never claim a certification, partner tier, or capability we don't hold. Specific tiers are marked with an asterisk until confirmed by 10Turtle.
A support team calm at their desks while an AI agent quietly clears the queue on screen, a real production moment
In production
This is what it looks like when an agent actually works
Live, integrated, and accountable. Not a demo that wows in a meeting and breaks in week two. We build for the quiet Tuesday, when the agent just handles it.
Governed from day one, because that's what separates the 25% that ship
The enterprises scaling agents successfully now spend more on trust, security, and governance than on the AI build itself. We bake that in rather than bolt it on.
We don't apply one blunt policy to every agent, Gartner is explicit that uniform governance across agents of different autonomy levels is itself a cause of failure. A read-only FAQ bot and an agent that can issue refunds get very different controls.
Per-agent identity & least-privilege access, every agent gets its own scoped credentials, never a shared API key.
Real-time audit trails & decision traces, what the agent did, and why, captured with timestamp and context.
Hallucination & accuracy evals, measured against your data, with pre-production thresholds it must clear.
Prompt-injection & data-poisoning defenses, including protection of the agent's memory and knowledge sources.
Human-in-the-loop gates, explicit approval for high-stakes actions, designed so review stays meaningful.
Circuit breakers & rapid rollback, the agent halts on threshold violations; you can switch it off cleanly.
Data-residency & privacy controls, built to your regulatory scope.
Accessibility and compliance are both expertise and trust signal: we build agents and conversational interfaces that meet ADA / WCAG 2.1–2.2 AA standards and handle data under GDPR, CCPA, and PCI DSS where relevant, and for regulated buyers, with the additional controls those sectors require.
Governance tiered to autonomy, base to top
Identity & least-privilege accessScoped credentials per agent
Audit trail & decision tracesEvery action logged with context
Human-in-the-loop gatesApproval on high-stakes steps
Circuit breaker & rollbackHalts on threshold violations
What it costs, how long it takes, and the bill nobody warns you about
Pricing depends on one thing: complexity. Here are the honest industry ranges so you can sanity-check any quote, yours included. We scope your exact number in the free audit, against a defined ROI metric.
Representative 2026 industry ranges, not 10Turtle's quoted prices.*
The number most quotes hide: a mid-sized support agent can carry a three-year TCO well above 2× its build quote. We put the full run-rate on the table on day one, not when the first invoice lands.
"Just build it in-house" is riskyFrom-scratch builds routinely run 12–24 months to real value and are commonly under-budgeted by 40–60%.*
Most pilots stallMIT's GenAI Divide study found ~95% of enterprise GenAI pilots delivered no measurable business impact, and externally built tools succeeded roughly twice as often as internal builds.*
The sweet spot is a partnerProduction-grade where it matters, integrated into your stack, governed, and fully owned by you, without the multi-year detour.
The upside is real, too. Gartner projects conversational AI will cut global contact-center labor costs by roughly $80 billion in 2026, with AI handling contacts at a fraction of the per-conversation cost of human agents.
A senior engineer and a client side by side at one screen, calm and in control
One roofFrom the first audit to a live agent, one accountable team. No outsourcing, no finger-pointing.
Why 10Turtle
Why teams choose 10Turtle for AI agents
One accountable partner, a dedicated AI team, and a process built against the exact failure modes that roll agents back.
01
You get a dedicated AI engineering team, not a generalist juggling your agent between web tickets, backed by one accountable partner under one roof. No outsourcing, no white-label markup, no finger-pointing.
02
Our process is designed against the exact failure modes that roll agents back. We measure success before we build, and we don't call it done until it's live and holding.
03
We'll talk you out of an agent you don't need and into a chatbot that's cheaper and better. No agent-washing, ever.
04
The agent, the prompts, the integrations, and the data are yours. We build your capability, not a dependency on us.
05
Real third-party reviews, recognized certifications, and named technology partnerships, the signals both buyers and AI assistants check before they recommend a partner.
Representative engagements across support, sales, voice, knowledge, and rescue. Real client names and verified results publish with each live case study.
In their words, image, audio, and video. Real, permissioned testimonials replace these before launch.
Platforms we build on
Peer partnerships across the platforms we build on
WordPressWooCommerceShopifyWebflowWix
We build on Anthropic’s models as a partner, not a dependency — never under or through Anthropic.
14
One roof
One roof for the whole experience
An agent rarely lives alone. When your AI agent needs a home on a fast, accessible website, our Web team builds and integrates it. When it needs a voice, name, and on-brand personality, our Branding team designs it. And when the automation reaches deeper into your operations, our wider AI practice scopes the workflows behind it, all under the same accountable roof.
AI agent & chatbot development: your questions, answered
A chatbot understands a question and answers it from a knowledge base, so it resolves the conversation. An AI agent reasons about context, takes actions across your systems (like your CRM or helpdesk), and completes a multi-step task, so it resolves the problem. Chatbots mostly read; agents read, decide, and write. If your task only needs an answer, a chatbot is the smarter spend; if it spans systems, depends on context, or replaces copy-paste work, you need an agent.
Industry ranges in 2026 run roughly $8K to $25K for a RAG chatbot, $25K to $150K for a custom agent with integrations, and $150K to $450K+ for enterprise or multi-agent systems; compliance-heavy sectors add 25 to 35%. Just as important is the run-rate: development is typically only 30 to 40% of three-year total cost of ownership, with LLM usage, hosting, monitoring, and maintenance making up the rest. 10Turtle scopes your exact number (build and run-rate) in a free audit, against a defined ROI metric.
A focused RAG chatbot is typically 2 to 8 weeks; a custom agent with real integrations is usually 8 to 16 weeks; enterprise and multi-agent systems run 4 to 9+ months. We work in phases so you see a live pilot early rather than waiting for a big-bang launch.
The common causes are exposed data, hallucination and brand risk, fragile integrations, and unclear ROI, not bad luck. Fewer than 25% of agents are scaled to production and many enterprises have rolled at least one back. We design against those failure modes specifically: governed context to curb hallucination, least-privilege access and audit trails for data safety, real integrations, and a success metric agreed before we build.
Hallucination is mostly a context problem, not a model problem. Agents invent answers when they lack governed access to your real data. We ground agents in your information with retrieval and citations, test against accuracy thresholds before launch, and constrain actions with human-in-the-loop approval for high-stakes steps and circuit breakers that halt the agent on threshold violations.
Yes, integration is the most important factor in whether an agent succeeds, and the one most vendors gloss over. We connect agents to your CRM, ERP, helpdesk, and data warehouse using open standards like the Model Context Protocol (MCP), so connections are portable and maintainable, and we plan for what happens when an integration changes in production.
In-house gives maximum control but routinely takes 12 to 24 months and is commonly under-budgeted by 40 to 60%. MIT's GenAI Divide study found ~95% of enterprise GenAI pilots delivered no measurable business impact, and that externally built tools succeeded roughly twice as often as internal builds. SaaS is fast but limits customization and ties you to a vendor. Partnering hits the middle: production-grade and customized where it matters, integrated into your stack, governed, and fully owned by you. We'll give you an honest read on which fits your situation in the audit.
We're model-agnostic, Claude, GPT, and Gemini, chosen per task for reasoning, safety, latency, and cost, and framework-fluent across LangGraph, CrewAI, the OpenAI and Claude Agent SDKs, Microsoft's Agent Framework, and Google's ADK. We build on Anthropic's models as a partner, not a dependency.
Yes. The agent, its prompts, integrations, and your data belong to you. We build your capability, not a lock-in.
Absolutely, it's one of our core services. We audit your existing bot or agent, diagnose why it stalled, and rebuild it to production standard. We're experts in re-automation and agent rescue.
It can be, with the right controls: data-residency and privacy safeguards, audit trails, human-in-the-loop on sensitive decisions, and governance tiered to the agent's autonomy. We build to ADA/WCAG accessibility standards and GDPR/CCPA/PCI DSS data requirements, with the additional controls regulated sectors require.
Bring us a workflow, a stalled bot, or just a hunch. In a free AI agent audit, we'll tell you whether you need a chatbot or an agent, what it would take to build, the real all-in cost, and whether it's worth doing at all. No obligation, no agent-washing.
Founder starting fresh at a clean desk, a blank screen ready for a new agent
Engineer reviving a stalled chatbot, screen showing a fixed, working conversation
A support team calm at their desks while an AI agent quietly clears the queue on screen, a real production moment
FINAL-CTA-BAND · 21:9