We design, build and verify custom RL environments with realistic tasks, reliable reward functions and hard-to-game graders, so your models get better at the skills you care about.
Also need labeled images, video or text? We still do that too: see vision & annotation.
env: invoice_reconcile domain: finance-ops sandbox: docker://fl/erp-sim:1.4 tools: [browser, sql, python] instruction: > Match March invoices to POs and flag any variance above 2%. reward: - check: ledger_matches_ground_truth weight: 0.7 - check: flags_correct # precision + recall weight: 0.3 guards: [no_db_writes_outside_scope, no_answer_lookup] max_steps: 60
Pick a single piece or have us deliver the whole pipeline. Every environment ships with the code, data and documentation to run it on your own infrastructure.
Sandboxed, reproducible worlds where agents act with real tools and get real feedback.
The signal is the product. We make rewards that are accurate, dense where it helps, and hard to game.
Human data to warm-start a policy or shape its behaviour before and alongside RL.
Held-out task sets that tell you whether training is working, and keep telling you as models change.
Short loops, shared visibility, and nothing ships until it has been tried against real models.
We start from the behaviour you want, not a task count, and agree on the domain, difficulty target and how success is measured.
Our engineers and domain experts write the tasks, sandboxes, tools and graders, with a pilot batch in your hands early.
Every task is checked for solvability, run against baseline models, and attacked for reward hacks before it reaches you.
You get versioned, documented environments. We study your training runs and adjust difficulty and coverage as your model improves.
For perception models and multimodal training, we provide annotation for images, video and text, with model-assisted tooling and human quality review on every batch.
Single- and multi-label tags for images and video frames, against your taxonomy.
Tight, consistent boxes for object detection, including small and occluded objects.
Semantic and instance masks with refined, pixel-accurate boundaries.
Consistent object IDs across video frames for tracking and re-identification.
Named entities, classification, sentiment and span labeling for NLP datasets.
Keypoints, polygons, 3D or something unusual? Tell us what your model needs.
Model-assisted, human-verified. Our in-house pipeline uses segmentation models such as SAM-HQ with mask refinement to draft labels, and trained annotators correct and approve every one. You get faster turnaround without giving up accuracy. No special folder structure is needed: send the data the way you have it.
A smaller set of correct, verified tasks beats a large set of noisy ones. We never trade accuracy for count.
Early pilot batches let you test the signal on your own models before committing to scale.
Start with one environment or a few thousand labels and grow from there.
Clear per-task or per-project quotes, agreed up front.
Interested? Email us with the capability, domain and timeline you have in mind. We'll come back with a scoped proposal and a pilot plan.
FasterLabeling