Engineering
Applied AI and product engineering.
Nine capabilities. Most engagements combine two or three. We scope the shape together in the first call.
01
Computer Vision
Production CV, from data to deployment.
Classification, detection, segmentation, image enhancement, OCR, ego-motion, and multi-camera pipelines. We own the full loop — dataset design, labelling, training, edge deployment, and drift monitoring — for products that need vision to actually work in the field.
What you get
- Image classification & detection (YOLO, EfficientNet, transformers)
- Semantic and instance segmentation
- Image enhancement, harmonisation, and quality inspection
- Ego-motion, optical flow, and perception pipelines
- Models optimised for edge (mobile, embedded, industrial)
Stack we reach for
02
Hyperspectral & Spectral Imaging
See what RGB can’t — for materials, defects, and quality.
Hyperspectral and multispectral imaging is rare, hard-won expertise. We apply it to material identification, defect detection, and quality inspection — combining spectral physics, sensor calibration, and deep learning into inline inspection systems.
What you get
- Spectral classification for material identification
- Defect, contamination, and decay detection
- Sensor calibration & cross-band data harmonisation
- Spectral + RGB + depth fusion into a single decision
- IoT-connected inline inspection systems
Stack we reach for
03
3D Reconstruction & Sensor Fusion
LiDAR + RGB + IMU, turned into decisions.
Visual odometry, point-cloud processing, 3D reconstruction from 2D imagery, and multi-sensor fusion. We use ICP, YOLO 7, NeRF, and EfficientNet to build indoor mapping, 3D segmentation, and object-reconstruction systems — with octree-optimised pipelines that run in real time.
What you get
- LiDAR + RGB + IMU sensor-fusion pipelines
- Visual odometry and SLAM-style indoor mapping
- 2D-to-3D reconstruction and NeRF-based scenes
- Point-cloud segmentation and object extraction
- Real-time performance tuning (octree, GPU)
Stack we reach for
04
Hardware & Edge Deployment
From board to production line.
Embedded ML, IoT automation, custom sensor rigs, and inference tuned for latency and power. We design the whole edge stack — from choosing sensors and boards to shipping models onto them and closing the loop with cloud analytics.
What you get
- Edge model optimisation (quantisation, distillation, ONNX / TensorRT)
- Custom sensor rigs and multi-sensor calibration
- IoT automation & control loops
- Embedded deployment (Jetson, Coral, ARM SoCs)
- Cloud/edge sync and fleet management
Stack we reach for
05
Applied AI & LLMs
LLMs, RAG, and vision-language into production.
Retrieval-augmented Q&A, vision-language models, LLM integrations inside existing products, and BERT-embedded categorical stacks. We handle the unsexy parts — evals, guardrails, prompt versioning, and cost.
What you get
- RAG pipelines with source-grounded answers
- Vision-language model integrations
- LLM features inside existing web and mobile products
- Categorical modelling with BERT + PCA + autoencoders
- Prompt versioning, evaluation, and cost controls
Stack we reach for
06
Agentic AI
Multi-agent systems that actually get work done.
Autonomous agents, tool-using assistants, planner–executor architectures, and multi-agent workflows — designed with the discipline that makes them survive production. Memory, state, human-in-the-loop, evals, and failure modes are first-class, not afterthoughts.
What you get
- Single-agent systems with tool use and memory
- Multi-agent orchestration (planner, worker, critic patterns)
- MCP servers and function-calling integrations
- Human-in-the-loop review and approval gates
- Agent evaluation harnesses and observability
Stack we reach for
07
Forecasting & Time-series
Turn history into decisions.
Demand, decay, throughput, and anomaly forecasting for CPG, industrial operations, and consumer platforms. We combine classical statistical methods with modern deep-learning approaches and wire the outputs into dashboards or automation.
What you get
- Demand and revenue forecasting
- Decay, spoilage, and quality forecasting
- Throughput and capacity forecasting for operations
- Anomaly detection on streaming data
- Executive dashboards driven by forecasts
Stack we reach for
08
Mobile Apps
Native-feeling apps on every platform your users are on.
Cross-platform Flutter when speed and reach matter; native Swift or Kotlin when the experience has to be flawless. We ship marketplaces, on-demand services, AI-powered consumer apps, and everyday utilities to real users on both stores.
What you get
- Flutter apps for iOS + Android from one codebase
- Native iOS (Swift / SwiftUI) apps
- Native Android (Kotlin / Jetpack Compose) apps
- App Store and Play Store launches
- Offline-first and sync architectures
Stack we reach for
09
Full-stack Web & Product Platforms
Fast, boring, reliable web software.
Modern Next.js on the front, Postgres and Supabase on the back, deployed on Vercel. We build marketing sites that convert, operations dashboards teams actually use, and full product platforms that combine web, mobile, and back end into one shipped system.
What you get
- Marketing sites with SEO and performance built in
- Internal operations and production dashboards
- Multi-platform product suites (web + mobile + back end)
- Custom CMS, admin panels, and authenticated portals
- Migration from legacy stacks (WordPress, Rails, etc.)
Stack we reach for
Also need the team to build it?
We also hire the engineers. See our Talent pillar, or get in touch and we’ll suggest the shape.
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