Chatbots fail when they guess. Brainforge builds assistants grounded in your docs, CRM, tickets, and transcripts — in Slack, on your site, or inside your product — with citations and human approval on actions. Our own Slack assistant runs on this stack every day.
In plain terms
Where teams get stuck
Our chatbot gives generic answers because it doesn't have our context.
Teams re-ask the same questions in Slack because answers live in silos.
Customer support answers are inconsistent and untracked.
We don't want a bot writing to our systems without approval.
What we deliver
Role-aware assistants inside Slack that search transcripts, CRM, tickets, and docs, draft cited answers, and route approved actions where the team already works.
Site and product chatbots grounded in your knowledge base, with human escalation and conversation logging so quality is measurable.
Evals, conversation monitoring, and guardrails so answers stay accurate as your context, tools, and policies change.
How deep it goes
The same delivery primitives (context, controls, and review) show up across every engagement.
Assistants inside Slack built on the Slack API and Vercel AI SDK, with channel-aware routing, citations, and daily briefs.
Retrieval and context engineering over docs, transcripts, tickets, and CRM so answers carry source trails.
Draft follow-ups, tickets, CRM notes, and briefs with human approval on sensitive writes.
Site and product chatbots with intake, routing, escalation to humans, and conversation logs.
Evals, monitoring, and policy controls so chatbot quality is measured and defensible.
Hosting, observability, and iteration from real usage with your team.
What changes
Proof in production
Our assistant searches meeting transcripts, HubSpot, Linear, Google Workspace, web, repo context, and vault docs; drafts cited answers; and sends role-aware daily briefs.
See the Slack Assistant proof →See how AI copilots used unified campaign context to flag underperforming assets and budget misallocations.
Read the copilot case study →Related ways to engage
Common questions
Engagements start with a scoped build sprint, so you pay for a bounded piece of work rather than an open-ended retainer. Most teams begin with one assistant on a named surface — Slack, support inbox, or site chat — then expand once they see grounded answers in use.
A first assistant with retrieval, citations, and approval flows typically ships in 3–6 weeks of sprint work. Larger rollouts across multiple channels phase in after the first surface is stable.
Yes. Slack-native assistants are our flagship — the Brainforge Assistant that runs inside our own company is built on the same stack, searching transcripts, CRM, tickets, and docs with cited answers and approved actions.
Every assistant ships with retrieval over approved context, source citations, conversation logs, and eval criteria. We monitor answer quality and turn failures into eval cases so accuracy improves over time.
No. Sensitive actions stay approval-gated. The assistant drafts follow-ups, tickets, and CRM notes, and a human approves before anything is written.
How we work
We find the repeated questions and stale answers in Slack, support, and docs that cost your team time.
We wire retrieval, citations, approval flows, and escalation into the surface where the questions happen.
We set evaluation criteria, monitor conversations, and improve the assistant from real usage.
Our Trusted Partners
In one working session we'll name what's broken, what's possible, and the first system worth building.
