Most agent builds die after the demo. Brainforge ships agents that work inside your actual systems — Slack, CRM, docs, and data — on the stack we run internally: Vercel AI SDK, Mastra, MCP tools, and Langfuse traces with human approval gates.
In plain terms
Where teams get stuck
We have agent demos but nothing running in production.
Our agents give generic answers because they don't have our context.
Nobody can tell whether agent output is improving or drifting.
We need agents to act in Slack, CRM, and our data systems, not just chat.
What we deliver
Agents that retrieve approved context, use tools, and take defined actions with human approval where it matters — built on Vercel AI SDK and Mastra with durable workflow persistence.
MCP servers, vector retrieval, and connectors to Slack, CRM, tickets, and data systems so agents act where work already happens instead of living in a chat window.
Traces, evals, monitoring, and review loops on Langfuse and OpenTelemetry so agent quality is measured and improves, not guessed at.
How deep it goes
The same delivery primitives (context, controls, and review) show up across every engagement.
Agents built on Vercel AI SDK and Mastra with durable workflows, model routing, and tool calling that survives real usage.
A knowledge layer over your docs, transcripts, tickets, and data so agents answer from approved context with citations.
Custom MCP servers and integrations that give agents scoped, permissioned access to your systems.
Approval gates on sensitive actions, workflow persistence, and audit trails so automation is reliable, not a demo.
Traces, evals, and quality review on Langfuse and OpenTelemetry so agent output stays trustworthy.
Skills, playbooks, and usage patterns your team extends after the first launch.
What changes
Proof in production
Our internal assistant searches transcripts, HubSpot, Linear, Google Workspace, web, repo context, and vault docs; drafts cited answers; and routes approved actions in Slack.
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 or a discovery sprint, so you pay for a bounded piece of work rather than an open-ended retainer. Most teams begin with one production agent on a named workflow, then expand once they see it working with real data.
A first production agent with retrieval, tool access, and approval gates typically ships in 4–8 weeks of sprint work. The point is a working system with metrics and traces, not another demo.
Both. We build on Vercel AI SDK, Mastra, and MCP by default because that is the stack we run in production internally, and we adapt to your existing infrastructure — model providers, data systems, and approval workflows — as needed.
Every agent ships with traces, evaluation criteria, and review loops. We monitor output quality, turn failures into eval cases, and hand over the operating model so your team keeps improving it.
Agents can take defined actions, but sensitive writes stay approval-gated and traceable. We scope permissions per system so automation is controlled instead of a black box.
How we work
We pick the workflow with the clearest owner and data, then map the approved sources, rules, and permissions the agent needs.
We ship the agent, its MCP tool access, retrieval, and approval flows on your stack, with traces wired in from day one.
We put the agent where work happens, set evaluation criteria, and iterate from real usage with your team.
Our Trusted Partners
In one working session we'll name what's broken, what's possible, and the first system worth building.
