Customers expose gaps no spec can predict
The first version will miss things. Real chats reveal confusing wording, unexpected intents, missing context, and places where the agent needs a better answer.
Built • Monitored • Improved
Managed AI agents for customer conversations
Built and managed by the Peach team
We set up AI agents for your high-volume customer conversations, connect them to your systems, watch how they perform, and keep improving them every week. Your team gets fewer repetitive conversations without owning prompt fixes, failed handoffs, or AI ops.
Why?
AI agents do not stay good on their own
Real customers keep teaching you what the agent needs to handle next. New objections, edge cases, phrasing, languages, and failure patterns show up every week.
Every chat carries too much baggage
Costs and quality suffer when agents drag bloated prompts, too many tools, oversized knowledge bases, and irrelevant context into every answer.
Upgrades can break what already works
A new prompt, model, tool, or knowledge-base update can improve one flow and quietly damage another. You need versioning and checks before changes go live.
Managed AI Agents
Peach builds the first version fast, then keeps improving it
We help you launch without turning AI into a six-month internal project. Then we keep the agent improving with micro-agents, custom evals, audit tooling, versioned upgrades, and weekly fixes.
We build the first useful version
We map the highest-volume conversations, design the first agent behavior, connect the basics, and get you to a working launch quickly.
We use micro-agents instead of one giant bot
Smaller agents handle focused jobs like lead qualification, order status, refunds, or handoffs, so each part is easier to test, tune, and upgrade.
We build custom evals and audit tools
We define what good looks like for your use case, run checks against real conversations, and use internal monitoring tools to catch regressions before they become support issues.
We version and upgrade agents safely
When customers ask new questions, use different phrasing, or trigger weak responses, we update prompts, knowledge, tools, and handoff rules without blindly breaking flows that already work.
30-day rollout
Start small, prove the agent works, then increase volume
We do not throw AI at every conversation on day one. We launch on repeatable use cases, review real chats, and scale only when quality is holding up.
MapDays 1-7
01
Map and learn from what already exists
We review existing chats, help docs, policies, scripts, and escalation patterns to understand what customers actually ask and where automation is safe.
BuildDays 8-21
02
Build the first version and deploy
We design the first micro-agents, connect the required tools and knowledge, set handoff rules, run initial evals, and launch on a controlled slice of volume.
ScaleWeek 4 onwards
03
Monitor, improve, and scale
We audit real conversations, run evals before upgrades, fix failure patterns, add new use cases, and increase automation only when quality holds.
Safety
Guardrails for the conversations you cannot afford to get wrong
The agent should know when to answer, when to check a system, and when to hand the conversation to a human.
SOC-2 Type II Certified
Enterprise-grade data protection, privacy controls, and encryption for customer conversation logs.
Zero-context-loss escalations
Human reps receive full conversation history and intent summaries, so customers never repeat themselves.
Hallucination shields
Guardrails prevent agents from guessing prices, promising unapproved policies, or misrepresenting your brand.
Versioned upgrades
Misses and edge cases feed into prompt updates, knowledge-base cleanup, eval runs, and controlled releases.
Frequently Asked Questions
What makes this a conversational AI agent?
It can handle back-and-forth customer chats, ask follow-up questions, check connected systems, and hand off when it is not confident. It is built for live customer conversations, not just internal task automation.
Is this a software platform or a managed service?
Both. You get Peach's AI agent platform plus our team managing setup, integrations, transcript reviews, custom evals, versioned upgrades, and ongoing improvements.
Which customer channels do you support?
We are WhatsApp-first because that is where most high-intent customer conversations happen for our customers. We also support agents on any channel you want to automate, including voice, webchat, email, Instagram DMs, or other customer messaging surfaces.
How do we prevent mistakes during live conversations?
We start with repeatable use cases, add guardrails for risky answers, and hand off to human reps whenever confidence is low. Your team can also flag any bad response so we can tune it.
Audit
Show us your customer conversations and we will map what AI can safely handle
Share a few details and we will review your customer conversation workflow, identify the repeatable work, and suggest the first managed agent worth building.
- Best starting use cases for automation
- Channels, tools, and systems to connect first
- Where HITL, evals, and upgrade checks should sit