Results that speak
for themselves.
Real problems. Measurable outcomes.
A selection of engagements across industries — each one a distinct AI challenge solved with custom data strategy, domain-specific models, and production deployments that delivered real business impact.
Built for
High-Stakes AI
We operate where data quality is mission-critical and the cost of failure is measured in lives, money, or market position.
Real results
from the field
From chaotic archives to searchable assets — and a new revenue stream
The network had accumulated 50+ years of untagged video and audio content — dramas, news archives, documentaries — with no structured metadata. Finding any piece of content required hours of manual searching, and the vast majority of the library was effectively invisible to both staff and potential licensing buyers.
Challenge: 50+ years of untagged video and audio content made content search and management time-consuming and inefficient. Valuable IP sat locked in unusable archives.
Label 50,000 videos to train and fine-tune a content understanding model
We designed a labelling schema covering scene types, speaker identification, sentiment, and topic classification, then worked with the client's archivists to produce a high-quality training dataset at scale.
Deploy scene detection, facial recognition, and keyword tagging at scale
The trained model was applied across the full archive, automatically generating rich metadata — enabling any clip to be surfaced by keyword, face, topic, or mood in seconds rather than hours.
Identify and capture an additional revenue opportunity
By analysing viewer trend data, we identified high-value "clip-worthy" moments — viral scenes and iconic dialogues — and built an automated pipeline to extract and reformat them for social media distribution.
Content search improved by 90%+, reducing manual archive retrieval from hours to seconds. The automated clipping pipeline turned dormant archive content into an active social media engine generating over 10 million views per month — a new revenue stream that didn't exist before the engagement.
archive man-hours
annually
per month
any archive clip
Predictive crop ripeness detection replaces fixed harvest schedules across thousands of hectares
Spanning thousands of hectares, plantation harvest management relied on fixed rotational schedules — every 10 to 15 days per block — regardless of actual fruit ripeness. A significant proportion of fruit was consistently harvested either too early or too late, compounding yield losses across every cycle. Manual monitoring at this scale was not operationally feasible.
Challenge: Fixed harvest schedules misaligned with actual ripeness caused approximately 28% yield loss. Labour-intensive manual monitoring could not scale to cover thousands of hectares consistently.
Top-view aerial mapping to identify high-yield harvest areas
Drone imagery was used to map ripeness across entire plantation blocks, flagging areas with the highest density of harvestable fruit — allowing harvesters to prioritise zones and plan routes in real time.
Detailed tree-level monitoring for fruit, leaf, and bark health
Ground-level computer vision models analysed individual tree samples, identifying health signals and yield potential with high precision — enabling early intervention before problems affect the harvest.
Ground-level weed and soil condition detection
Additional models flagged weed encroachment and wet/dry soil conditions, enabling proactive ground management that protects future yield cycles and reduces reactive labour costs.
Combining predictive ripeness detection with weed and disease monitoring delivered a 34%+ increase in effective yield rate. Harvester routes were optimised in real time based on live ripeness data, saving over 1,000 man-hours annually and eliminating the guesswork from one of agriculture's most economically critical decisions.
increase
eliminated
annually
optimisation
From generic translation to cultural intelligence: AI that thinks locally
As the company expanded internationally, surface-level translation was not sufficient. Markets expected content that felt genuinely native — reflecting local idioms, cultural references, and market context. Off-the-shelf translation APIs produced technically accurate but culturally hollow output that failed to engage local audiences.
Challenge: No quality data existed that accurately captured cultural nuance, subtlety, and market context. This made it impossible to localise content at the quality and scale required for meaningful market growth.
Develop split evaluation criteria to reduce subjectivity
We created structured rubrics across key cultural evaluation domains — formality level, regional idiom, emotional tone, and brand voice — transforming "does this feel local?" from a subjective judgement into a measurable, repeatable scoring system.
Implement a feedback loop to regenerate and validate localised content
A closed-loop evaluation pipeline was built: content was generated, scored against the rubric, and regenerated iteratively until it cleared the quality threshold — ensuring consistency and cultural accuracy at scale.
Evaluate all LLM outputs and build a Golden Dataset
Every approved output was added to a curated Golden Dataset used to continuously fine-tune the localisation model — so the system became measurably more accurate with every piece of content processed.
Quality-localised content drove over 50% sales growth through increased customer engagement and premium pricing power. Subscriber numbers grew 33% as audiences responded positively to content that felt genuinely native — not translated. The Golden Dataset created a lasting proprietary data asset that continues to improve model performance post-engagement.
localised content
growth
continuous improvement
deployed
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