Governed AI that works in production

AI works when it has the right context, the right workflow, and the right controls. Brainforge helps teams move from scattered experiments to production agents and copilots that people trust.

Trusted by Fortune 1000 and High Growth Startups

In plain terms

What AI means in practice

Turn AI pressure into governed workflows, knowledge systems, copilots, and agents that work inside your existing operations. We help teams pick the right wedge, build the context layer, and ship production systems with human approval gates.

Where teams get stuck

Build workflow automation, knowledge engineering, copilots, and agents around company context.

We have AI pilots but no production operating model.

Our assistants give generic answers because they do not have the right context.

Teams are using AI tools inconsistently, without guardrails or review.

We need agents to work inside Slack, CRM, repo, docs, and data systems.

What we deliver

Core AI

Workflow Automation

We automate repeated work across Slack, CRM, tickets, and internal systems so teams stop copying context by hand.

Knowledge Engineering

We turn documents, transcripts, tickets, and operating rules into a searchable context layer that supports people and agents.

Copilots & Agents

We build assistants that retrieve approved context, cite sources, escalate edge cases, and run with human approval where it matters.

How deep it goes

Capabilities behind the work

The same delivery primitives (context, controls, and review) show up across every engagement.

Production agent systems

Agent runtimes with durable approvals, workflow persistence, and human-in-the-loop gates so automation is reliable, not a demo.

Durable approvalsWorkflow persistenceHuman-in-the-loop gates

Slack-native AI assistants

Operating intelligence inside Slack, cited answers, tool routing, knowledge search, and workflow approvals where work already happens.

Cited answersTool routingKnowledge search

AI engineering toolchain rollout

Stand up modern AI coding and agent tooling for your team with setup, usage patterns, and guardrails.

Team setupUsage patternsGuardrails

Controlled tool access for agents

Custom integrations that let agents act with scoped permissions, plus deployment and hosting.

Scoped permissionsCustom integrationsDeployment & hosting

Reusable AI workflows

Skills, orchestrators, and operating playbooks your team can extend after the first launch.

SkillsOrchestratorsPlaybooks

AI reliability and evals

Observability and improvement loops, traces, evals, and quality review so AI output stays trustworthy.

TracesEvalsQuality review

The stack we build with

Tooling our team runs in production

Frameworks, platforms, and infrastructure we implement, integrate, and operate end to end.

Models & providers

OpenAIAzure OpenAIAnthropic Claude

Agent & copilot runtimes

CopilotKitMastraLangGraphn8n

Context & retrieval

SupabasePostgreSQL & pgvectorTurbopuffer

Observability & evals

Langfuse

Identity & access

WorkOS / AuthKit

Where work happens

SlackHubSpotLinearGoogle Workspace

What changes

Outcomes you can point to

A prioritized AI workflow backlog tied to business value and readiness.
Working assistants and agents with source grounding, review gates, and measurable adoption.
An AI toolchain and skill/playbook system that makes your team more capable after we leave.
A trace and eval loop so failures become improvements, not dashboard archaeology.

Proof in production

See where this work shows up.

Our own stack

Brainforge Assistant is this service running inside our own company.

It searches transcripts, HubSpot, Linear, Google Workspace, web, repo context, and vault docs; drafts cited answers; and routes approved actions where work already happens.

See the Slack Assistant proof →