Computer Vision & Multimodal AI
Systems that read documents, inspect products, understand video and generate visual assets — combining classical vision, modern detection models and multimodal LLMs where each one actually fits.
- Document intelligence
- Detection & tracking
- Video analytics
- Generative visuals
The right model is often not the biggest one
A multimodal LLM can read an invoice, but a tuned detection model does it a hundred times cheaper and faster at volume. We combine classical vision, purpose-trained models and multimodal LLMs by task, which is how vision systems stay affordable once they leave the pilot.
Capabilities in this practice
Each of these ships as a working system integrated with what you already run — not a slide deck or a proof of concept that stalls at the pilot.
Document Intelligence & IDP
Invoices, contracts, forms, KYC packs and shipping documents parsed into structured fields with confidence scores and a review queue for anything uncertain.
OCR at Scale
High-throughput extraction across scans, handwriting and multi-language documents, with layout and table structure preserved.
Detection, Segmentation & Tracking
Object detection and tracking for counting, safety monitoring, quality inspection and process analytics on live or recorded video.
Video Understanding
Event detection, scene summarisation and searchable video indexes, so hours of footage become a queryable dataset.
Generative Visual Pipelines
Text-to-image, sketch-to-image and image-to-3D generation with quality presets and standardised output formats — the stack behind MeshVista.
Edge & On-Device Deployment
Quantised models running on edge hardware where bandwidth, latency or privacy rules out sending frames to a server.
A sequence built to de-risk, not to impress
We measure before we optimise and ship in slices, so you can stop, redirect or scale at any step with evidence rather than instinct.
- 1
Data audit
Assess sample quality, capture conditions and label availability — the honest constraint on what is achievable.
- 2
Labelling strategy
Define the schema, set up annotation, and use synthetic data where real examples are scarce.
- 3
Model selection
Trial classical, purpose-trained and multimodal approaches against accuracy, latency and cost per item.
- 4
Human-in-the-loop
Confidence thresholds and review queues so low-certainty items reach a person instead of silently failing.
- 5
Deploy
Batch, real-time or edge serving, integrated with the systems that consume the output.
- 6
Retrain
Feed reviewer corrections back into training so accuracy improves with use.
Vision work we have shipped
We build our own products on this stack. When we recommend an approach, it is one we already run in production and pay the bills for.
The things clients ask before signing
Less than most teams expect. Fine-tuning a pretrained detector often works from a few hundred well-chosen examples per class, and multimodal LLMs can handle some document tasks with none at all. We start with what you have and quantify the gap rather than assuming a large annotation project.
Yes. Open-weight vision models deploy inside your VPC or on edge devices, which is typically required in manufacturing, healthcare and defence contexts where frames cannot leave the site.
On structured and semi-structured documents, high enough to automate the majority of items straight through, with the remainder routed to human review by confidence score. We report per-field accuracy against a held-out set rather than a single headline number, because the fields that matter differ by process.
Ready to put this into production?
Tell us the problem you are trying to solve. We will tell you honestly whether AI is the right tool for it, and what it would take to ship.