Services
AI that works in production. The whole programme, or just the piece you need.
Most of our largest customers buy one service. Some buy the full programme. The four below are modular — pick the entry-point that matches where you are now, sequence them when it makes sense.
Define what “good” looks like — before a single label is placed.
Most annotation programmes fail upstream of annotation. The taxonomy was ambiguous. Edge cases were never written down. The acceptance test was “the model passed UAT” — but UAT was three engineers eyeballing 50 frames. The team that wrote the schema is not the team that has to defend it to regulators eighteen months later.
Our Data Strategy service is the work that should happen before the labelling brief is signed. Schema, taxonomy, QA framework, gold-set construction, acceptance criteria — set by people who have labelled 500M+ datapoints across robotics, medical imaging, satellite, financial documents and product content. The deliverable is a written specification your engineering team and our annotation team can both work to, with the test that proves the model meets it.
A label set that survives five model versions.
Class definitions, hierarchy, overlap rules, attribute fields, ID conventions. Designed so the same data is reusable when the model architecture changes, the regulator changes, or the product team pivots.
What data you need, what you have, what you have to acquire.
Audit of the existing corpus against your performance target. Gap analysis by class, by geography, by seasonality, by demographic. A buy-or-collect plan for the missing slices.
Multi-pass review, gold sets, model-in-the-loop validation.
Reviewer ratios, inter-annotator agreement targets, gold-set composition, escalation rules, drift thresholds. Designed to your accuracy target — not a generic 95%.
The number the model has to hit, and the test that proves it.
Per-class precision, recall, F1 and confidence thresholds. Stress cases. The held-out set that proves production-readiness. Signed off before annotation starts so there is no argument at acceptance.
Best entry-point if you have raw data but no labelling plan, or if your current programme is hitting an accuracy ceiling and you suspect the schema is the blocker.
Next: Data Enrichment ↓Production-grade annotation. The same accuracy at 10K rows or 10M.
This is the core. 1,000+ trained annotators across 11 rural community offices, 16 years of practice, 96% annual retention. The team that learned your domain in month one is the team that scales you in year three. Most of our customers buy this service first.
We support every common modality and every common annotation type. We work on your platform or ours. We handle domain expertise that gig platforms cannot — farmers labelling agricultural imagery, clinically-trained annotators reviewing medical pathology, multilingual reviewers handling moderation in dialects most teams cannot source. The output is fit for production training, every cycle.
Image, video, LiDAR, 3D, text, audio, multimodal.
Satellite and drone imagery. Surgical video. Autonomous-vehicle point clouds. Voice transcription. Financial documents. Multilingual content moderation. Sensor fusion. We have shipped each at production scale.
Bounding box, polygon, semantic + instance segmentation, keypoint, classification, transcription, entity, sentiment, intent.
Pricing scales by annotation complexity and the expertise tier of the annotator — not by hourly labour rate. The price reflects the cost of the recommendation being correct.
CVAT, Prodigy, Labelbox, Dataloop, QGIS, custom APIs.
Bring your platform or use ours. We integrate with your S3 buckets, your DICOM stores, your GIS pipelines, your data lake. Outputs in COCO, YOLO, Pascal VOC, GeoJSON, shapefile, custom JSON — whatever your model expects.
Multi-tier review, gold sets, model-in-the-loop validation, drift monitoring.
98.7% accuracy across 500M+ company-wide datapoints. 99.4% sustained on autonomous-robotics workloads. 18 months zero-drift on Machani Group’s own robotics programme. Multi-pass review is standard, not premium.
- AgTech: 50M+ acres analysed annually. 4.5M+ Taranis images. 460+ weed species.
- eCommerce: 40M+ products processed annually. 814K+ UPCs for Syndigo. 53M+ SKUs for a major delivery platform.
- Healthcare: 631K+ diagnostic interpretations for Audere at 98% accuracy.
- Finance: 2.5M+ documents annually. 100K+ records validated for ITA Group at 98%+ accuracy.
- Robotics: 99.4% sustained accuracy on Machani Group production stack. 18 months zero-drift.
Best entry-point if you have a labelling plan but need volume, or if your current vendor cannot hold accuracy as you scale.
Next: Algorithm Development ↓Build the model. On the same stack we run our own robots on.
We do not just label data and hand it over. We build the model that consumes it, train it against the acceptance criteria your team signed off in Strategy, validate it against the production scenarios your team cannot reproduce in QA, and hand over weights, training pipeline and evaluation harness so your team owns it from day one.
The differentiator is that the same engineering organisation runs Machani Group’s own robotics programme — CeCe and RIA are in production for the care sector. We have lived the problems of drift, edge-case generalisation and production observability ourselves. When we say a model is ready, it is ready against the standards we apply to robots that operate around vulnerable adults.
The right model for the problem — not the trendy one.
Computer vision: YOLO variants, transformer-based detectors, segmentation backbones, fine-tuned VLMs. NLP: transformer fine-tunes, retrieval-augmented architectures. 3D/LiDAR: PointNet derivatives, voxel grids, sensor-fusion stacks. Multimodal where the problem warrants it.
The Machani Group production stack.
GPU training capacity, distributed training pipelines, experiment tracking, hyperparameter sweeps, reproducibility controls. The same infrastructure we use for our own robotics models, available to your programme.
Gold-standard datasets, custom test harnesses, human-in-the-loop review.
Stress-testing against real-world data and hidden edge cases. Bias and fairness testing through subgroup analysis. Compliance-aligned validation for HIPAA, GDPR, SOC 2 and industry-specific standards. The deliverable is a written validation report your regulators can read.
Your team owns the model.
Trained weights. Training pipeline. Evaluation harness. Inference scaffolding. Documentation written for engineers, not vendors. No black boxes, no proprietary runtime lock-in. You can fork it, fine-tune it or rebuild it without us.
Best entry-point if you have labelled data but no model, or if your in-house team needs an extension to ship a vertical model on a quarter-scale timeline.
Next: Deployment & Maintenance ↓Run it in production. The world the model meets is not the world it was trained on.
A model that scored 99% in evaluation can degrade to 84% in production within a quarter — and nobody notices until a customer complaint surfaces it. Distribution shift is silent. Drift is silent. Edge cases that never appeared in the training set arrive at production, one at a time, and the model fails them one at a time. The cost is not the failure. The cost is the time it takes to detect, diagnose and retrain.
Our Deployment & Maintenance service runs the production pipeline. Drift monitoring, retraining cycles, SLA-governed accuracy, edge-case escalation. The same operational discipline we apply to Machani Group’s own robotics programme — 18 months of zero-drift sustained QA on autonomous-robotics workloads — applied to yours.
Ingest → clean → label → validate → deploy → monitor.
Cycle times compressed from weeks to hours. Integration with your CI/CD, your MLOps platform, your data lake. Encrypted data channels for compliance-critical workloads. Custom connectors where the off-the-shelf integration falls short.
Continuous observability on inputs, outputs and per-class performance.
Distribution shift detection on input features. Per-class precision and recall monitored on a rolling window. Alert thresholds tuned to the cost of failure in your domain. The dashboard your on-call team actually opens.
Triggered by drift, not the calendar.
When monitored drift crosses the threshold set in Strategy, the retraining pipeline runs against new annotated data — produced by the same team that holds the original taxonomy. No knowledge loss between cycles. Accuracy holds.
Accuracy under contract. Edge cases to humans, not the bot.
Accuracy SLA written into the agreement, with the same gold-set test that signed off the original model. Compliance-critical edge cases routed to trained human reviewers — never silently down-graded to the automated path.
Best entry-point if your model works in evaluation but is drifting in production, or if you need an operations partner to run the pipeline so your engineers can ship the next model.
See this in action →All work delivered from secure, in-house facilities under GDPR, HIPAA and SOC 2-aligned practices. No subcontracting. No third-party annotation. UK data-residency arrangements available on request. B Corp certified — your supplier code-of-conduct compliance is already in place.
Next steps.
The 100-frame audit is a free 48-hour benchmark. You upload samples from your current pipeline; we return an accuracy, coverage and bias report — plus a service recommendation if useful. No sales conversation required.