Computer vision
Images and video, from bounding boxes and keypoints to dense instance and panoptic segmentation.
Data labeling infrastructure for frontier AI teams
Automation-first data labeling.
SatyaHQ combines engineering and human review to deliver labeling pipelines that are faster, cheaper, and more auditable than conventional vendors.
Every batch of work makes the next one more automated — so your cost per label drops as your dataset grows.
What we label
Coverage across every modality your models need.
From pixel-dense segmentation to preference pairs and agent trajectories, we run the pipeline end to end.
LLM evaluation and RLHF
Preference pairs, rubric scoring, and red-teaming, run by reviewers who understand model behavior.
Document and OCR
Structured extraction from complex, multi-page layouts — forms, contracts, tables, handwriting.
Audio and speech
Transcription, speaker diarization, and intent tagging across accents, languages, and noisy conditions.
Multimodal
Image-text, video-text, and agent trajectory datasets for frontier multimodal and agentic systems.
Custom workflows
Domain-specific taxonomies, review hierarchies, and tooling built to your spec on request.
The SatyaHQ difference
Engineering runs the pipeline. Humans review the edges.
Most vendors scale labeling by adding people. We scale it by writing software. LLM-in-the-loop pre-labeling, active learning, and programmatic QA handle the predictable work, so our reviewers spend time only on the cases where human judgment actually shifts the label.
That shift is the entire product. It means your cost per label falls as your dataset grows, your turnaround tightens batch over batch, and every decision stays traceable to the model version and reviewer that produced it.
01
Automation compounds
Every batch trains the next round of pre-labels. Unit economics improve week over week, not month over month.
02
Human judgment where it matters
Senior in-house reviewers, not a crowd. Calibrated, retained, and measured on agreement with gold.
03
Transparent by default
Every label is auditable to the annotator and the model version that proposed it. No black boxes.
04
Engineers, not a body shop
We ship tooling, not just hours. You keep the pipeline artifacts when the engagement ends.
Maninder Singh Mann
- Founder and Managing Director
- IIM Faculty
- IIM Alumni
- Ex- Pepsico, Mondelez (Cadbury), AkzoNobel
- Authority on AI Automations
Ashutosh
- Co-Founder | Technology & AI
- AI Automation Architect
- AI Agents & Workflow Systems
- Business Process Optimization
- Scalable Automation for Startups & SMEs
How we work
Five steps from kickoff to steady-state delivery.
The first two weeks are the whole game. Get the taxonomy right, automate the baseline, and the pipeline compounds.
1
Discover
We map your data, taxonomy, and acceptance criteria.
2
Design the pipeline
We scope tooling, reviewer tiers, and QA loops.
3
Automate the baseline
LLM pre-labels and heuristics do the predictable work first.
4
Human review in the loop
Senior reviewers resolve the edge cases that move the model.
5
Deliver and iterate
We ship batches, surface errors, and retrain the automation.
FAQ
Questions buyers ask us.
Answers are written for ML leaders who've been burned by a labeling vendor before.
Most engagements start labeling within five to seven business days. The first week goes into aligning on taxonomy, acceptance criteria, and pilot volume. Larger programs with custom tooling requirements typically kick off within two to three weeks.
We work across computer vision, LLM evaluation and preference data, document extraction, audio, and multimodal agent trajectories. If your domain needs a specialized taxonomy or workflow, we design and automate it for you. We decline work only when we cannot meet your accuracy bar.
Every project runs in a segregated environment with role-based access, device-level controls, and network restrictions. Annotators and engineers sign NDAs and IP assignments before touching data, and all handling is DPDPA-aligned. For sensitive workloads, we can operate inside your VPC or on-premise environment so data never leaves your perimeter.
All three, depending on what makes your cost model predictable. Per-label pricing works for well-defined tasks at scale; per-hour suits exploratory or low-volume work; fixed-scope project pricing covers pilots and milestone-based programs. We quote before you commit, and we share throughput data so pricing gets tighter over time.
Yes. We regularly operate inside customer-owned Labelbox, Scale, CVAT, and bespoke internal tools — we bring the pipeline engineering and the reviewer workforce; you keep the platform. For teams without a platform preference, we can stand up and operate tooling end to end.
Yes, and we encourage it. Most Fortune 100 engagements begin with a two- to four-week paid pilot scoped to a defined batch, with measurable accuracy and throughput targets. If we don't hit them, you don't scale.