A clear breakdown of what Brainforge fixes, how fast, and what it actually delivers.
Support agent builds
Chat and voice agents that handle intake, triage, and routine resolution grounded in your docs, tickets, and product data.
Support AI that resolves more and escalates with context
Customers don't want to repeat themselves, and your team doesn't want to answer the same question twice. Brainforge builds support agents that handle intake, triage, and routine answers with your knowledge base and history, escalate to humans with full context, and log everything for evals.
In plain terms
What AI Customer Support means in practice
Where teams get stuck
Support agents that handle intake, triage, and answers with your context, with human escalation and conversation evals.
Support answers are inconsistent and nobody tracks quality.
Customers repeat themselves because history lives in silos.
Agents resolve the same routine questions that a bot could handle.
We can't measure whether support AI is helping or hurting.
What we deliver
Core AI Customer Support
Escalation with context
Humans get full conversation history when an agent hands off, so customers never repeat themselves and agents learn from real resolutions.
Support operations
Conversation logs, evals, and quality review so deflection, resolution, and satisfaction are measured and improve.
How deep it goes
Capabilities behind the work
The same delivery primitives (context, controls, and review) show up across every engagement.
Support agent development
Chat and voice agents for intake, triage, and routine resolution, on the channel your customers use.
Chat agentsVoice agentsIntake and triage
Grounded answers
Retrieval over your docs, tickets, and product data so answers are consistent and correct.
Knowledge retrievalConsistent answersSource grounding
Human escalation
Handoff with full conversation history so customers never repeat themselves.
Context handoffAgent routingCRM sync
Evals and quality
Conversation logs and quality evals that drive improvement.
Conversation loggingQuality evalsDeflection tracking
Operations and iteration
Monitoring and review loops your team runs after launch.
MonitoringReview cadenceIteration
What changes
Outcomes you can point to
- Routine questions resolved without a human, with consistent answers.
- Escalation that carries full context instead of starting over.
- Evals that measure deflection, resolution, and satisfaction.
- A support loop that improves from every conversation.
Proof in production
See where this work shows up.
Brainforge Assistant is this service running inside our own company
Our internal assistant searches transcripts, docs, and systems, drafts cited answers, and routes work with approval gates.
See the Slack Assistant proof →AI receptionist for healthcare
See a grounded example of AI handling intake and scheduling while preserving human context.
Read the healthcare AI receptionist guide →Related ways to engage
A clear front door into this work
Common questions
AI Customer Support, straight answers
What does AI customer support implementation cost?
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 channel — chat, email, or phone — and one question cluster, then expand as evals show quality.
How long does a support AI implementation take?
A first support agent with retrieval, escalation, and conversation evals typically ships in 3–6 weeks of sprint work, then tunes on real conversations.
Do your support agents replace our team?
No. They handle routine intake, triage, and answers so your team spends time on the cases that need judgment. Escalation with full context is built in, and evals show where humans still add the most value.
How do you keep support answers accurate?
Retrieval over your approved knowledge, conversation logs, and quality evals. When an answer is wrong, it becomes an eval case and the agent improves.
Can this work across chat, email, and phone?
Yes. We build on the channels your customers actually use — site chat, Slack, email intake, and voice agents — with one shared context layer and eval loop.
How we work
A path from pressure to a working system
01
Map the support flow
We map the questions customers ask, the answers that resolve them, and where humans must step in.
02
Build and ground the agent
We wire the agent to your knowledge base and history, tune escalation, and add conversation evals.
03
Launch and improve
We monitor deflection and quality, then iterate from real conversations with your team.
Our Trusted Partners
We only bring the best of the best
READY TO PUT
AI Customer Support to work?
AI Customer Support to work?
In one working session we'll name what's broken, what's possible, and the first system worth building.
