A clear breakdown of what Brainforge fixes, how fast, and what it actually delivers.
Trace and monitoring setup
End-to-end traces on LLM calls, tool use, and agent decisions, with alerts on drift, latency, and cost.
Know what your AI is doing, and whether it is getting better
Agents make decisions you can't see and drift you can't feel. Brainforge wires traces, evals, and monitoring into LLM apps and agents — on Langfuse and OpenTelemetry — so every decision is traceable and failures become improvements.
In plain terms
What AI Observability means in practice
Where teams get stuck
Traces, evals, and monitoring for LLM apps and agents, so AI output stays trustworthy and improves.
We can't see what our agents actually do step by step.
Answer quality is unmeasured, so failures surface as complaints.
AI cost and latency creep with no visibility into why.
There is no loop that turns failures into improvements.
What we deliver
Core AI Observability
Evaluation programs
Eval suites for retrieval, answers, and agent behavior that catch regressions before users do.
Review operations
Review loops and scorecards so quality is owned, measured, and improved over time.
How deep it goes
Capabilities behind the work
The same delivery primitives (context, controls, and review) show up across every engagement.
Tracing and instrumentation
End-to-end traces on LLM calls, tool use, retrieval, and agent steps, on Langfuse and OpenTelemetry.
LangfuseOpenTelemetryTrace export
Monitoring and alerts
Dashboards and alerts for drift, latency, cost, and error rates so problems are caught early.
Drift alertsLatency monitoringCost tracking
Evaluation suites
Eval cases for retrieval, answers, and agent behavior that gate releases and catch regressions.
Retrieval evalsAnswer evalsRelease gates
Review operations
Scorecards and review loops so quality is owned and improved continuously.
ScorecardsReview boardsImprovement loops
What changes
Outcomes you can point to
- Full traces on every LLM call and agent decision.
- An eval suite that measures and gates quality.
- Alerts on drift, latency, and cost before they bite.
- A review loop your team runs after we leave.
Related ways to engage
A clear front door into this work
Common questions
AI Observability, straight answers
What does AI observability implementation cost?
Engagements start with a scoped instrumentation sprint, so you pay for a bounded piece of work rather than an open-ended retainer. Most teams begin with traces on one AI system, then add evals and monitoring as the loop proves out.
How long does an observability implementation take?
A first trace-and-eval setup typically ships in 2–4 weeks of sprint work, depending on how many AI systems and how much legacy instrumentation exists.
Which observability tools do you use?
We standardize on Langfuse for LLM traces and evals and OpenTelemetry for system instrumentation, and adapt to your existing monitoring stack as needed.
What do you measure?
Traces on LLM calls, tool use, retrieval, and agent steps, plus evals on answer quality, and alerts on drift, latency, and cost. The scorecard is built around what your business cares about, not generic dashboards.
Can you observability an AI system we already built?
Yes. We retrofit tracing and evals onto existing LLM apps and agents, which is most of our work — systems built fast without instrumentation are where the value is.
How we work
A path from pressure to a working system
01
Instrument the system
We wire tracing and monitoring into your LLM calls, tools, and agents so behavior is visible.
02
Define what good looks like
We build eval cases from real failures and business criteria so quality is measurable.
03
Run the review loop
We set up scorecards and review cadence, then hand over the loop to your team.
Our Trusted Partners
We only bring the best of the best
READY TO PUT
AI Observability to work?
AI Observability to work?
In one working session we'll name what's broken, what's possible, and the first system worth building.
