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Your Team Wastes 4 Hours a Week Tab-Switching for Answers You Already Own

If you're a Head of Ops watching your team context-switch between Stripe, HubSpot, Metabase, and Slack 40 times a day, you're funding a $80k/year coordination tax.

Your team uses Slack all day. Your data lives in 5 different tools. What if you could ask what is our MRR this month or which deals close this week directly in Slack and get a real answer from your actual data? We build Claude-powered Slack bots that connect to your database, CRM, analytics, and business tools. Your team asks questions in natural language. AI queries your systems and responds in the channel.

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Claude-Powered Slack Integration

A Slack AI integration is a custom bot that accepts natural language questions inside Slack and returns accurate, live answers pulled from your connected data sources such as Postgres, HubSpot, Stripe, or Metabase. It goes beyond read-only: the bot can draft records, trigger workflows, and surface alerts based on conditions you define. We build, test, and deploy the integration end to end, including authentication, permission scoping, and query guardrails.

What is holding your current website back?

Common gaps we find in nearly every audit.

Answering a question like 'what is churn this quarter' requires opening four tools, finding the right report, and hoping the numbers agree with each other.
Risk: That context-switching compounds across your team every day, degrading focus and producing inconsistent answers depending on who ran the query and when.
Non-technical operators cannot self-serve data without help from an analyst or engineer, creating a bottleneck that delays decisions by hours or days.
Risk: Your technical staff spends a significant portion of their week on ad hoc data requests instead of product or infrastructure work, directly slowing output.
Dashboards go stale, get ignored, or require a trained user to interpret correctly, so most of your team defaults to guessing or asking someone else.
Risk: Decisions made on outdated or misread data accumulate into strategic errors that are only visible in retrospect, when correcting course is more expensive.

How We Build This Right

Every safeguard, built in from Day 1.

Scoped Data Access

Every integration is built with explicit permission boundaries. The bot only queries tables and fields you approve, using read-only credentials where write access is not required, so sensitive records are never exposed accidentally.

Audit Logging

Every query, action, and response is logged with the requesting user, timestamp, and data source. You maintain a full record of what was asked and what the bot returned, which supports internal review and access audits.

Guardrails and Query Validation

Before any query reaches your database, it passes through a validation layer that blocks destructive operations, enforces row limits, and flags queries outside expected patterns, preventing accidental data exposure or unintended writes.

What We Build

Purpose-built features for your industry.

Plain English Data Queries

Operators type questions in Slack the way they would ask a colleague. The bot translates the question into a precise query against your actual database or CRM, returns a formatted answer, and can follow up with clarifying detail if asked. No SQL knowledge required on the user side.

Multi-Source Data Joins

A single question can draw from multiple connected sources simultaneously. Ask which deals are at risk this week and the bot can cross-reference HubSpot stage data with Stripe payment status and your internal product usage metrics in one response.

Action Execution

Beyond answering questions, the bot can take actions you define: creating a HubSpot contact, updating a deal stage, triggering a Zapier workflow, or posting a formatted summary to a channel. Actions require explicit confirmation steps to prevent unintended execution.

Proactive Alerts and Digests

Configure the bot to monitor conditions on a schedule and post to relevant Slack channels when thresholds are crossed, such as MRR dropping below a target, a trial nearing expiration without conversion, or a support queue exceeding a set volume.

Built on a Modern, Secure Stack

Claude APISlack APISupabaseVercelHubSpot APIStripe API

Our Development Process

From discovery to launch. Quality at every step.

01

Discovery and Data Mapping

1 week

We spend the first week documenting your data sources, the questions your team asks most often, and the actions that would save the most time. We map table structures, API availability, and permission requirements before writing a line of code.

02

Bot Architecture and Credentials

1 week

We set up the Slack app, configure Claude API access, and establish secure read connections to your approved data sources using least-privilege credentials. We define the query validation layer and establish logging infrastructure before any user-facing feature is built.

03

Query and Action Development

1-2 weeks

We build and test the core question-and-answer flows against your real data, covering the top query types identified in discovery. Each query is validated for accuracy, edge cases, and response formatting. Actions are implemented with confirmation flows and rollback handling where applicable.

04

Staged Rollout and Handoff

1 week

We deploy to a test channel with a small internal group first, gather feedback, and refine responses before opening to the full team. We deliver documentation for adding new query types, adjusting permissions, and monitoring usage, then remain available for a two-week support window post-launch.

Social Animal

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Frequently Asked Questions

If it has an API, we can connect to it. In practice, that means databases, Salesforce, HubSpot, Stripe, Google Analytics, Jira, and plenty of custom internal tools. The bot answers from your real data -- not generic responses, not hallucinated numbers.
The AI only surfaces what users are allowed to see. You configure which channels and users can access which systems, and sensitive data can be restricted to DMs. Every query is logged, so you've got a full audit trail.
A simple bot connected to one or two systems starts at $5,000. Multi-system bots with workflow actions -- the kind that can query five platforms and create Jira tickets -- run $15,000 to $30,000 depending on complexity.
Both, actually. Ask questions and get answers in seconds. Or go further and trigger actions -- create a ticket, update a record, send an email -- right from Slack. Sensitive actions can require approval before they execute, so you're never one accidental message away from something irreversible.
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