Most teams have tried copilots and POCs. Brainforge is where that experimentation becomes a system: we score use cases, build the first one to production, and stand up the governance and operating model so AI keeps compounding after we leave.
In plain terms
Where teams get stuck
We have AI pilots everywhere and nothing in production that matters.
Leadership wants an AI roadmap, but vendors keep selling us another tool trial.
Our data isn't governed or shaped well enough for AI to be trustworthy.
We don't know how to evaluate, monitor, or safely operate agents after launch.
What we deliver
A scoped sprint that maps how AI should work in your business, scores use cases, and delivers an executable 90-day plan leadership can approve.
Production builds — copilots, agents, RAG foundations, and model selection — grounded in your governed data and workflows.
Evaluation criteria, human-in-the-loop controls, observability, and the operating model that keeps AI reliable at scale.
How deep it goes
The same delivery primitives (context, controls, and review) show up across every engagement.
Current-state mapping, use-case scoring, and an executable roadmap that survives contact with leadership.
Production LLM and agent builds wired into the tools your teams already use.
Evaluation criteria, guardrails, and observability so AI output is defensible, not vibes.
Role-shaped agents that run defined operational loops under human approval gates.
What changes
Proof in production
Common questions
Engagements start with a scoped discovery sprint or a single production use case, so you pay for a bounded outcome rather than an open-ended retainer. Most teams begin with a four-week discovery sprint and fund the first build from the roadmap it produces.
Both, but most of our work starts where teams already failed. If you have POC fatigue or isolated copilot experiments, we bring structure: current-state mapping, use-case scoring, and a plan that replaces the pile of pilots.
We are a specialist team that builds. You get senior practitioners shipping production agents and workflows on your stack, with governance and evaluation built in, instead of a large team producing slides and recommendations.
Yes. Every engagement includes guardrails, permissions, evaluation criteria, and human-in-the-loop controls, and we design for regulated environments with local or private model routing when needed.
A first production workflow typically ships in 6–12 weeks from a discovery sprint. The point is a working system with metrics your leadership trusts, not another demo.
How we work
We map current AI usage, constraints, and business value so the roadmap is funded by outcomes, not vendor hype.
We ship a working agent or workflow on governed data with evaluation criteria and a clear owner.
We hand over guardrails, monitoring, and enablement so the team runs and extends the system.
Our Trusted Partners
In one working session we'll name what's broken, what's possible, and the first system worth building.
