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AI sales infrastructure for service businesses

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September 13, 2025

Flutch

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Agents API MCP Open Source

AI sales infrastructure for service businesses

A/B Testing Acceptance Testing Access Control Agent Analytics Agent Governance Agent OperationsFree + from $49/mo

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Overview Discussion

Overview

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Flutch deploys AI infrastructure for service businesses — sales agents that capture inbound across every channel, qualify leads against your rules, follow up automatically, and sync to your CRM.

Our team builds a configured industry pack for your vertical (roofing, HVAC, law, med-spa, home services), launches it on your real leads in 7–10 business days, and stays on as your AI-ops team.

Supported features

Key Features

Product Analytics: Dau/mau, Retention, Funnels, Conversions
Observability (per Agent + Org-level): Latency, Tokens, Error Rate, Failures
Agent Configuration Versioning: Multiple Versions, Compare, Promote Best
Usage Limits: Per User Group Limits By Messages/tokens/budget
Alerts: Notify When Budgets/limits Are Near Threshold
Channel Integrations: Whatsapp, Telegram, Slack, Discord, Messenger, Instagram
A/b Experiments: Compare Agent Versions On Selected Metrics
Pre-release Acceptance Tests: Run Scenario Suites Before Rollout
Conversation Audit Log (human-readable): Full Dialog + Step-by-step Processing Trace
Copilot: Helps Operate The Platform Without Deep Platform Knowledge
Agent Creation + Templates: Built-in Templates For Fast Setup
Shareable Chat Ui: Create Agent → Share Link → Others Can Chat
Knowledge Management: Articles/files; Reuse Kb Across Agents
Sdk For Langgraph: Devs Build The Graph; Sdk Wraps Endpoints + Memory Mgmt
Model Gateway: Use Built-in Provider Keys; One Billing Surface For Models
User Management & Access Control: Private / Company / Public; Roles/permissions

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Initial release

September 13, 2025

Initial release of Flutch.

Author

Artem Ignatev

@artem-ignat-ev

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Joined: September 2025

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Pros and Cons

Pros

Central place to operate multiple conversational agents across a company

Works for both engineering and operators: technical metrics plus readable conversation logs

Supports safe iteration: versioning, A/B comparisons, and pre-release scenario tests

Helps keep usage predictable with budgets/limits and threshold alerts

Deploys the same agent to multiple messaging channels without rebuilding integrations each time

Lets teams share and reuse knowledge bases across multiple agents

Allows quick internal trials via templates and a shareable chat UI

Integrates well with LangGraph-based workflows via SDK (endpoints + memory handled for you)

Can connect existing LangChain-based agents (e.g., n8n) for analytics

Simplifies model access and billing via a single model gateway

Supports access control (private / company / public) and basic role-based permissions

View 6 more pros

Cons

Outcome/ROI analytics depends on clean event tracking and reliable CRM/support/booking data

Initial setup requires integrations, event mapping, and agreed “success” definitions

Attribution to deals can be uncertain in multi-touch or offline sales processes

Not a replacement for full BI; complex reporting will still live in Looker/PowerBI/SQL

May be too heavy for small pilots, low-volume bots, or a single internal assistant

Some enterprises require BYOK / strict vendor allowlists; a shared model gateway may not fit

Coverage of external frameworks is incomplete (no native CrewAI or LlamaIndex yet)

Messaging channels can add policy/API constraints that affect what’s possible to automate

Deep customization of workflows or data models may still require engineering work

Storing conversation logs can trigger security/compliance reviews and retention requirements

View 5 more cons

Q&A

What is Flutch?

wrote:

Flutch is an analytics and governance layer for conversational AI agents. It helps teams operate multiple agents in one place: track performance, manage versions and rollouts, control usage and budgets, and review conversations with an audit trail.

Who is it for?

wrote:

Teams running conversational agents in production: engineering/platform teams, AI teams, and operators (RevOps, Support Ops, CX, product owners) who need visibility and control across agents, channels, and versions.

How is this different from LLM observability tools?

wrote:

LLM observability focuses on technical signals (latency, errors, tokens, traces). Flutch is built to run agents as a business system: versioning, rollouts, experiments, audits, limits, and analytics that operators can use.

Do you build agents or only monitor them?

wrote:

Both are possible. You can create agents using templates inside Flutch, or connect existing agents built with other stacks (e.g., LangGraph / LangChain-based systems like n8n).

Can I connect my existing agent framework?

wrote:

Yes for LangGraph and LangChain-based setups (including n8n). Native support for CrewAI and LlamaIndex is not available yet.

What channels can agents run on?

wrote:

Common messaging channels including WhatsApp, Telegram, Slack, Discord, Messenger, Instagram (availability may depend on each platform’s API and policies).

+ Show 10 more

What does “agent versioning” mean in practice?

wrote:

Each agent has multiple configurations/versions. You can test versions, compare performance, and control rollouts so changes are tracked and reversible.

How do A/B tests work?

wrote:

You can split traffic between agent versions and compare results using selected metrics (usage, conversions/funnel steps, quality scores, etc.), then promote the better-performing version.

What are acceptance tests?

wrote:

Scenario-based tests you run before releasing an update. They help catch regressions and risky behavior using a predefined set of conversations and edge cases.

Does Flutch offer on-premise deployment?

Yes, Flutch offers assistance for on-premise deployment in any infrastructure, or provides Helm charts for self-deployment.

What is the conversation audit log?

wrote:

A human-readable log of the full dialogue plus a trace of how the agent processed the message and produced the response, designed for non-developers as well.

How do cost controls work?

wrote:

You can set limits per user group (messages, tokens, budget) and receive alerts when thresholds are approaching.

Do users need to bring their own model keys?

wrote:

You can use the built-in model gateway (one billing surface for multiple providers). If you require your own keys, that’s typically a configuration/enterprise requirement.

How is knowledge managed?

wrote:

You can create knowledge articles or upload files, reuse knowledge bases across agents, and keep information consistent across workflows.

Is Flutch a full BI tool?

wrote:

No. Flutch provides agent-specific analytics and operational controls. For complex company-wide reporting, teams often still use their BI stack (Looker/PowerBI/SQL).

What’s the typical setup effort?

wrote:

It depends on how many channels and systems you connect. The main work is integrating the data sources, mapping events, and defining what “success” means for each agent/use case.

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