Built for change, engineered for clarity
Generative and agentic AI don’t stand still. Requirements shift mid-flight, tasks get more subjective, and security constraints vary by dataset. Sigma adapts in real time — custom teams, custom workflows, and tool-agnostic execution — so you keep momentum without compromising quality.
How we adapt (without the chaos)
- Tool-agnostic delivery: your proprietary stack, open-source, or Sigma tools — no lock-in.
- Guideline iteration loops: rapid pilots → calibrated rubrics → scaled production with living “gold” sets.
- Right experts, right moment: linguists + SMEs (medical, legal, finance, geo, etc.) when domain stakes rise.
- Anywhere security: remote, on-prem, clean-room, or air-gapped — data stays where it must.
- Human + ML assist: pre-labeling, spot checks, and drift alerts to speed throughput without trading away nuance.
Read more: How to scale data annotation with an effective strategy
Our problem-solving playbook
When ambiguity shows up, we don’t guess — we instrument it.
- Frame the problem: define failure modes (hallucination, tone drift, bias) and success metrics (factuality, IAA, parity).
- Design the workflow: choose reviewers, sampling rates, adjudication rules, and escalation paths.
- Pilot & calibrate: run side-by-side tests; tighten guidelines where annotators disagree.
- Scale with safeguards: productionize QA gates, add coverage checks, and monitor drift.
- Close the loop: convert findings into rubric updates and model retraining data.
Backgrounder: The intricacy of assessing data quality
When scope shifts, we scale
- Speech → conversation intelligence: move from verbatim ASR to diarization, event tagging, and intent attribution across speakers, at scale.
- Monolingual → global: add markets and dialects with native teams; preserve tone and politeness norms across cultures.
- Text-only → multimodal: align image, audio, and text events to teach agents causal relationships (who did what, when, and why).
- Open internet → high-security: transition to clean rooms with locked-down devices and audited access.
See it in action: - Case study: Transcripción de vídeo escalable en 24 dialectos
- Case study: Natural conversation in specific dialects
Quality without crowdsourcing
Crowd marketplaces can’t sustain subjective judgments at enterprise grade. Sigma curates teams, targets inter-annotator agreement, and uses expert adjudication where nuance matters — so “flexible” never becomes “inconsistent.”
- Live calibration: weekly quality huddles, error taxonomy, and guideline versioning.
- Measurable outcomes: factuality, citation validity, sentiment/politeness parity, bias gap reduction.
- Human context by design: annotators are trained to capture subtext, cultural cues, and narrative logic.
Dive deeper: Human annotators in AI: Adding context & meaning
Secure by default, adaptable by design
Flexibility only works if security does too. We operate under ISO 27001 and SOC 2 Type II controls, are fully GDPR-aligned, and support on-prem/clean-room workflows with audit trails and least-privilege access.
Learn more: Ensuring data privacy and security in data annotation
What you can expect, every time
- Clear SLAs: throughput, turnaround, and quality/IAA thresholds tailored to your use case.
- Transparent QA: gold-set checkpoints, spot audits, and data cards documenting coverage.
- Faster time-to-value: pilots that prove impact in weeks, then scale without rework.
Related reading: Your GenAI data roadmap: 5 strategies for success
Bottom line: Complexity isn’t a blocker — it’s where Sigma’s flexibility and problem-solving shine. Bring us the shifting scope, the subjective rubric, the security constraint, and the need to scale. We’ll turn ambiguity into reliable, repeatable workflows.