Boostnetic is an independent AI research and advisory practice. We work upstream — designing the data architecture, evaluation frameworks, and technical strategy that AI labs and research teams build on.
End-to-end design and implementation of multi-modal data collection, processing, and annotation pipelines for frontier AI training. From sensor specs to Label Studio deployments at production scale.
Rubric design, SME calibration, and quality-gate engineering for reinforcement learning from human feedback. Domain-expert sourcing across medicine, law, linguistics, and scientific disciplines.
Strategic advisory for AI research programmes — scoping complex data initiatives, designing delivery architectures, and providing independent technical oversight across EU, US, Gulf, and MENA project contexts.
Technical advisory on AI data strategy, ontology design, and compute infrastructure. Academic-grade research output from a team with active PhD-level research at University of Greifswald and published work on AI thermodynamics.
A leading AI research lab needed to scale their egocentric video annotation pipeline from prototype to production. Existing tooling couldn't handle the volume, multi-modal complexity, or the quality requirements of their RLHF training data.
Designed and deployed a Label Studio-based pipeline with automated pre-annotation using SAM, custom quality gates with multi-stage reviewer calibration, and a real-time monitoring dashboard. Built annotator onboarding flows that reduced calibration time from weeks to days.
from boostnetic import Pipeline, QualityGate
# Multi-modal annotation pipeline
pipeline = Pipeline(
source="egocentric_video",
annotators=24,
quality_threshold=0.985
)
pipeline.add_stage("pre_annotate", model="SAM-2")
pipeline.add_stage("human_review", calibration=True)
pipeline.add_stage(QualityGate(iaa=0.92))
pipeline.run() # → 50k annotations, 98.5% quality
Scope your data problem, map constraints, define quality targets and delivery requirements. We start every engagement with deep technical discovery.
Design the full pipeline stack — tooling selection, annotation schema, quality gates, reviewer flows, and integration architecture.
Run the pipeline with continuous QA, real-time monitoring dashboards, and iterative calibration loops to maintain quality at scale.
Deliver structured datasets with full documentation, reproducibility specs, and transfer support so your team can own the pipeline.
Most RLHF deployments fail not because the model can't learn from feedback — but because the feedback pipeline itself is under-engineered. Teams treat human evaluation as a labelling task when it's actually an inference task with compounding uncertainty.
The pattern we've seen work at scale: decouple collection from calibration. Build a pre-annotation layer (SAM, auto-segmentation, or LLM-draft) to reduce cold-start friction. Route to domain-calibrated reviewers — not general annotators. Gate every batch through inter-annotator agreement thresholds before it touches training. The pipeline isn't a conveyor belt. It's a feedback loop with multiple resonance frequencies, and the architecture needs to account for drift at every stage.
The teams that get this right tend to invest 3x more in evaluation infrastructure than in model architecture. That ratio is not accidental.
There's a growing gap between how models are evaluated in academic benchmarks and how they perform in production. Benchmarks test capability in isolation. Production tests capability under composition — where errors chain, edge cases multiply, and the distribution shifts daily.
A research-grade evaluation framework treats rubric design as ontology work. Every evaluation dimension needs a formal definition, boundary cases, and calibration examples. Evaluators aren't interchangeable — they need to be profiled for domain expertise, calibrated against gold standards, and monitored for drift over time. Without this, you're measuring noise and calling it signal.
The most robust frameworks we've built separate quality measurement from quality gating. Measure everything. Gate selectively. The measurement infrastructure becomes the foundation for continuous model improvement, while the gates protect production from regressions.
Building annotation pipelines for multi-modal data — egocentric video, kinematics, sensor fusion — is fundamentally different from text or image labelling. The temporal dimension changes everything. You're not annotating frames; you're annotating sequences of intent across modalities that don't always align.
Three lessons from deploying these pipelines across research labs in four regions: First, sensor calibration specs should be part of the annotation schema, not a separate document — annotators need to understand what the data physically represents. Second, pre-annotation with SAM-class models cuts throughput time by 60%, but only if the review interface surfaces model confidence alongside the prediction. Third, quality in multi-modal pipelines is not a single score — it's a vector. Spatial accuracy, temporal consistency, cross-modal alignment, and semantic correctness each need independent measurement.
The tooling gap here is real. Label Studio gets you 70% of the way. The remaining 30% is custom engineering that most teams underestimate by an order of magnitude.
We work selectively with research labs, AI teams, and technical founders. Engagements are advisory-first.