BergFlow
Annotation engineIngest and queue management, labeling and context, quality gates, and validation for multimodal data, including physical-world workflows.
About BergLabs
Two platforms. Six applications. One operating loop.
BergLabs is the data-operations layer behind production AI. Two platforms do the core work: BergFlow controls records, and BergAuto automates the steps that move them. Six applications run on top as one aligned system: Rosterr, Quinn, Charterr, Sirenn, Radarr, and Atlass.
The system
Each platform and application has a clear role.
Together they make AI programs operationally reliable: clean inputs, accountable review, governed automation, focused applications, and measurable execution.
Quinn
Specialist review podsDomain reviewers for human review, QA and adjudication, and exception handling when automation should not own the decision alone.
BergAuto
Enterprise ops automationThe overarching automation layer that runs work across workflows, industries, and functions while approvals, exceptions, and confidence checks stay on record.
Rosterr
Productivity dashboardProductivity, SLA, performance, audit trail, and reporting visibility across the full system, with feedback that sharpens operations over time.
Operating model
From data to decisions, without operational drift.
We do not treat annotation, review, automation, and reporting as separate projects. The system is designed so each layer strengthens the next.
BergFlow structures the work.
Data enters through governed queues, schema-backed tasks, and validation gates before it reaches a model or downstream workflow.
Quinn handles the exceptions.
Specialist reviewers take on the decisions that need domain judgment, QA adjudication, or accountable human oversight.
BergAuto runs the repeatable steps.
Clear rules, approvals, confidence checks, and exception routing make automation useful without turning it into a black box.
Rosterr closes the loop.
Throughput, SLA, quality, and audit visibility feed back into the system so the operation gets sharper instead of noisier.
$120M+Client revenue impact across CX, finance, and operations
300%YoY growth in managed-ops volume (2025–26)
1,250+Trained specialists across functions
96–98%Annotation accuracy across multimodal data
Why clients work with us
Because enterprise AI fails in the handoff layer.
The hard part is rarely the model alone. It is the queue, the review burden, the exception path, the SLA risk, and the lack of operational visibility. BergLabs builds the system around that reality.
Global payments network engagementIndia’s largest e-retailerA European robotics companyA mid-market BFSI firmGlobal payments network engagementIndia’s largest e-retailerA European robotics companyA mid-market BFSI firmGlobal payments network engagementIndia’s largest e-retailerA European robotics companyA mid-market BFSI firm