AI & Data Solutions

Better models start with better labels.

Every AI or ML system is only as reliable as the labelled data behind it. Lakspire is a data-annotation partner — structured labelling, human-in-the-loop review and versioned training sets, built for real models in real deployments.

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Where we help

Three annotation deliverables that plug straight into modern ML pipelines.

Everything below is annotation work Lakspire genuinely delivers — from prototype-grade labelling to production-scale evaluation gold sets. Structured guidelines, calibrated reviewers and quality controls, engagement after engagement.

Training & Evaluation Datasets

Assembled, versioned training splits, evaluation gold sets and preference-labelled datasets — prepared for real ML pipelines with provenance you can audit.

Document Annotation

Field, clause and entity annotation on documents — invoices, forms, contracts, records — where accuracy under variation is the only useful measure.

Domain-adapted datasets

Datasets shaped to your domain vocabulary and edge cases — retail, healthcare, mobility, finance — so models learn from the specifics of your world, not a generic pool.

The operations bench

Eight operations behind every dataset we deliver.

Scroll to walk the bench card by card, or open all eight at once. The disciplined operations that surround every labelling engagement.

Operation 01

Raw data collection

Gathering text, images, audio and video from the sources you specify — with consent, provenance and coverage tracked end to end before a single label is applied.

SourcingConsent

Operation 02

Schema-driven labelling

Your labelling schema applied consistently at scale — every sample carries the exact tags your model was designed to consume, even as edge cases pile up.

TaxonomyScale

Operation 03

Pre- & post-labelling QA

Deduplication, redaction, normalisation and quality checks either side of labelling — no dirty data reaches your annotators, and no bad labels reach your model.

QAPII

Operation 04

Annotation quality scoring

Precision, recall, F1 and inter-annotator agreement measured against annotated gold sets — the regression signal ship-blockers care about.

MetricsGold set

Operation 05

Human-in-the-loop review

Reviewers correcting live model predictions in the loop — the safety net that turns a promising model into a production one, and produces correction sets for retraining.

HITLCorrection

Operation 06

Annotation validation

An independent second pass on annotation quality — guideline adherence, agreement scoring, and the sample-level defects that quietly ruin evaluation metrics.

AuditGuideline

Operation 07

Model-assisted pre-labelling

Model-drafted labels that human reviewers polish — throughput without giving up the accuracy that matters at review time, tuned pipeline by pipeline.

ThroughputAssisted

Operation 08

Guideline authoring & calibration

Written labelling guidelines, reviewer onboarding and calibration rounds — so the whole bench agrees on the same edge cases before they hit production data.

DocsCalibration

01/08Raw data collection

01 / 08

Raw data collection

Gathering text, images, audio and video from the sources you specify — with consent, provenance and coverage tracked end to end before a single label is applied.

02 / 08

Schema-driven labelling

Your labelling schema applied consistently at scale — every sample carries the exact tags your model was designed to consume, even as edge cases pile up.

03 / 08

Pre- & post-labelling QA

Deduplication, redaction, normalisation and quality checks either side of labelling — no dirty data reaches your annotators, and no bad labels reach your model.

04 / 08

Annotation quality scoring

Precision, recall, F1 and inter-annotator agreement measured against annotated gold sets — the regression signal ship-blockers care about.

05 / 08

Human-in-the-loop review

Reviewers correcting live model predictions in the loop — the safety net that turns a promising model into a production one, and produces correction sets for retraining.

06 / 08

Annotation validation

An independent second pass on annotation quality — guideline adherence, agreement scoring, and the sample-level defects that quietly ruin evaluation metrics.

07 / 08

Model-assisted pre-labelling

Model-drafted labels that human reviewers polish — throughput without giving up the accuracy that matters at review time, tuned pipeline by pipeline.

08 / 08

Guideline authoring & calibration

Written labelling guidelines, reviewer onboarding and calibration rounds — so the whole bench agrees on the same edge cases before they hit production data.

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Have annotation, evaluation or document-labelling work in flight?

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