Frontier system · Operations · 6 stages · updated 2026-10-23

Company brain: docs, data and tickets in Slack

A Slack agent that answers from docs and past threads with sources, answers data questions with SQL, files tickets from conversations, and reports gaps in your docs.

1Is this my problem?

The same questions get asked in Slack every week and answers are spread across Notion, Drive and old threads.

You'll recognise this if…

Senior people answer the same internal question repeatedly.
New hires take weeks to find things.
Simple data questions wait days for an analyst.

Cost of doing nothingExpert time is spent as a search engine and decisions wait on answers.

Companies affectedCompanies of 30-500 people with knowledge in several tools and a busy Slack, often with a data warehouse.

2Is this solution worth it?

Your ROI, with the assumptions shown

Prefilled with our illustrative assumptions for a typical team (20 h/week of manual work at $70/h, 30% automated). Change them to your numbers.

Build cost (estimate)$10k–$40k

Time to first version (estimate)6-12 weeks

AI agent stages3 of 6

Human checkpoints0

Limits and when not to build it

Not worth it if your docs are thin or out of date; fix the docs first.

Guardrails it needs

Respect document permissions.
Read-only database role.
Always cite sources.

3How does it work?

The agency builds you one internal tool, Company Brain, that runs the whole process. Watch it work on a sample case:

Company BrainIllustrative demo with sample data

Pick a sample on the left and press Run example to watch Company Brain work through a case step by step.

Your tool connects to

Slack / TeamsHelp deskData warehouseAI model

Most of it is ordinary automation. AI agents handle 3 of 6 steps, and people approve at no checkpoints.

Under the hood: stages, vendors and the learning loop

AI agent Automation Human checkpoint

1Ingest
Index Notion, Drive, help centre and chosen channels with permissions.
Vector DB
Automation
2Retrieve
Contextual retrieval and BM25 for fewer misses.
Embeddings
Automation
3Answer
Answer with links; say "don't know" when unsure.
AI SDKClaudeGPT
AI agentDecides: Answer or say "don't know"
4Query data
Text-to-SQL against a read-only warehouse role.
BigQuerySnowflake
AI agentDecides: Write the SQL
5Act
Turn a thread into a Jira or Linear ticket on request.
JiraLinear
AI agent
6Gap report
Weekly list of unanswered questions to doc owners.
Slack
Automation

Learning loop

Thumbs-down answers and unanswered questions become doc tasks and eval cases, so the bot is measured on real misses.

Example run

Illustrative sample data showing what moves between stages. Not taken from the sources.

1
Ingest · inNotion + Drive
out12,400 chunks indexed
2
Retrieve · in"What is our refund policy for annual plans?"
out3 passages
3
Answer · inPassages
outAnswer with 2 links
4
Query data · in"How many trials converted last month?"
outSELECT ... -> 212 (read-only)
5
Act · inThread
outLinear ticket ENG-881 created
6
Gap report · inWeek
out7 unanswered questions sent to owners

Vendors commonly used

Vector DBEmbeddingsAI SDKClaudeGPTBigQuerySnowflakeJiraLinearSlack

Where AI decides and where people do

AI agents decide

Answer: Answer or say "don't know"
Query data: Write the SQL
Act: Turn a thread into a Jira or Linear ticket on request.

Human checkpoints

Maturity ladder

What each level adds. Most teams start at level 1 or 2 and climb. Highlighted: where this build sits.

Level 1Basic automation
A wiki and a search box.
Level 2AI-assisted
An LLM answers questions from pasted docs.
Level 3Autonomous agent
A Slack agent retrieves with permissions, cites sources, runs read-only SQL and files tickets.
Level 4Self-learning system
Thumbs-down and unanswered questions become doc tasks and eval cases.

Building blocks

Start with one of these smaller workflows and add stages.

Slack assistant over your internal docs

A Slack bot answers team questions (lead stages, processes, policies) from internal documentation, with links, so people stop interrupting the one person who knows..

OperationsAdvanced~3–8 h/week$4k–$15k Ask-your-data analyst agent

Team members ask questions in plain English; an agent writes SQL against your warehouse, runs it, checks the result and explains it, in several reasoning phases..

OperationsFrontier~3–10 h/week$6k–$20k Emoji-triggered support sidekick in Slack

When a support person reacts to an internal question with a set emoji, AI generates a search term, fetches matching help docs, reviews related ticket history, and replies in the thread with linked steps..

Customer success & supportStarter~2–5 h/week$800–$2.5k Slack threads to sprint tickets

A Slack conversation is summarised into a properly written ticket and added to the next planned sprint..

OperationsStarter~1–3 h/week$500–$1.5k

Seen in the wild

Links checked 2026-10-23. The pipeline combines patterns from these public builds; we are not affiliated with these companies.

4Who can build it?

Matched on the services, platforms and industries agencies list on their own sites, and on their published projects.