Deep learning & custom models
Neural networks designed, trained and optimised for your data — from new architectures to fine-tuning and distillation.
Now booking new builds
We engineer
deep learning
for the real world.
A deep-tech team of ML researchers, data scientists and software engineers. Deep learning, computer vision, analytics and LLMs — taken from research to production, and kept there.
0+AI systems delivered
0countries, 4 continents
0 wksto a working prototype
0hresponse to every request
PyTorchTensorFlowJAXOpenCVYOLOTransformersHugging Facescikit-learnXGBoostSparkAirflowdbtSnowflakeBigQueryPower BILangGraphvLLMONNXTensorRTNVIDIA JetsonKubernetesAWSGCPAzure
// 01 — Capabilities
Deep tech, end to end —
from raw data to production.
Not just LLM wrappers. We build across the full AI stack — deep learning, vision, analytics and language — and the software that puts it in front of users.
Computer vision
Detection, segmentation, tracking, OCR and video analytics — running in the cloud or on edge devices and cameras.
Data analytics & engineering
Pipelines, warehouses and dashboards that turn scattered data into one reliable source of truth and real insight.
Predictive ML & forecasting
Demand forecasting, churn and risk scoring, anomaly detection and recommendations — measured against real outcomes.
NLP, LLMs & RAG
Language systems that search, summarise, extract and answer over your documents — with evals and citations built in.
AI automations & chatbots
Chatbots, agents and workflow bots that handle bookings, enquiries, orders and tickets — wired into your CRM and systems.
MLOps & edge AI
Deployment, monitoring and retraining pipelines. GPU serving in the cloud or quantised models on-device.
AI product engineering
Full-stack web and mobile products built around your models — UX, APIs, auth, integrations and scale.
AI & data science
- Deep learning research
- Vision, NLP & speech
- Statistics & forecasting
- Experiments & evaluation
Software craft
- System architecture
- Backend, APIs & data
- Frontend & product UX
- Security, scale & DevOps
// 02 — Industries
Proven where the
stakes are real.
Hands-on delivery across eight industries — pick one to see what we build there.
Healthcare
Medical imaging algorithms built with clinicians — validated carefully and designed around patient privacy.
- Radiology AI — chest X-ray detection and triage
- Digital pathology — cell detection, tissue segmentation, TIL scoring
- Medical image annotation with active learning
- Microscopy and biomedical image analysis
Retail & E-commerce
From store floors to warehouses — turning retail data into decisions that move revenue and margin.
- Footfall, queue and customer-attention analytics
- Warehouse returns automation and damage detection
- Order, pricing and customer-engagement automation
- Recommendations, forecasting and sales dashboards
Hospitality & Real estate
AI assistants that answer, book and qualify around the clock — so teams focus on guests and clients.
- Booking chatbots for courts, rooms, tables and sessions
- Lead qualification and automatic viewing scheduling
- Multilingual, location-aware customer support
- Website revamps with CRM and calendar integration
Manufacturing
Vision and edge AI on the factory floor — real-time, reliable and running without the cloud.
- Real-time production logging at the line
- Visual inspection and defect detection
- Edge deployment on NVIDIA Jetson with TensorRT
- Production dashboards and traceability
Food & Supply chain
Turning packaging, documents and inventory into clean, structured, compliant data.
- OCR + NER extraction from product packaging
- Compliance data enrichment (e.g. FSSAI)
- Self-learning pipelines that improve from corrections
- Inventory and returns automation in warehouses
Security & Logistics
Identity, vehicle and crowd intelligence for ports, venues and high-security sites.
- Face recognition for driver and staff verification
- Number-plate, licence and document checks
- Offline-capable Android field apps
- Crowd counting and flow management for events
Advertising & Media
Measuring the offline world like the online one — who saw what, when and for how long.
- Real-time audience profiling on edge devices
- Plug-in hardware for existing ad screens
- Campaign analytics and ROI measurement
- Recommendation engines driven by live data
Customer support & BI
Smarter operations for any business — from ticket queues to board-level dashboards.
- Real-time ticket prioritisation and routing
- Support chatbots integrated with your systems
- 360° dashboards across all your data
- Revenue and demand forecasting
// 03 — Process
From request to production,
in four clear steps.
-
01
Discover
Week 1We dig into your goal, data and constraints, then send a written plan with scope, architecture and a fixed quote.
-
02
Prototype
Weeks 2–3A working prototype on your real data, with an evaluation set, so we can measure quality before scaling.
-
03
Build
Weeks 4+Production engineering: integrations, UI, guardrails, tests and deployment. Weekly demos, shared repo, no black boxes.
-
04
Scale & support
OngoingMonitoring, evals and cost tuning after launch — plus handover and training so your team owns it.
// 04 — Case studies
Shipped, deployed,
in use.
A selection of AI systems our team has built — from hospital labs to warehouse floors.
Built for
- BigBasket
- Nykaa
- Bata
- Airtel
- Transnet
- Pramana
- Hardcastle Restaurants
- Quinta de Monserrate
- BuyCondo
- Lushful
// 05 — Start a project
Tell us what you want built.
Two minutes. A senior engineer reads every request and replies within 48 hours.
// 06 — Talk to an engineer
Book a 30‑minute call.
No sales script. You'll talk directly to someone who would build it.
// 07 — FAQ
Good questions.
How much does a project cost?
It depends on scope. Prototypes are typically a few weeks of work; production systems longer. After the discovery call we send a fixed quote for each phase, so there are no surprises.
Do you only build LLM / chatbot projects?
No. LLMs are one tool among many. Much of our work is deep learning, computer vision, forecasting, classical ML and data engineering — we pick whatever solves the problem best.
What if we don't have much data yet?
We can help collect and label it, use pre-trained and foundation models, generate synthetic data, or start with a data audit to find out what's feasible before you invest.
Which technologies do you use?
Whatever fits your cost, latency and privacy needs — PyTorch and the modern deep learning stack, classical ML, commercial or open-weight LLMs, and your existing cloud and data tools. We are not tied to any vendor.
Is our data safe — including patient data?
We sign NDAs, work inside your cloud when needed, and can build fully self‑hosted systems so data never leaves your infrastructure. For healthcare work we design around de-identification, access controls and audit trails from day one.
Do we own the code?
Yes. All code, models and prompts we build for you are yours, in your repository, from day one.
Can you work with our existing team?
Absolutely. We often embed with in‑house engineers, pair on architecture and leave behind documentation and training.