AI chatbots
Conversational interfaces for support, internal helpdesks, and domain Q&A ??? grounded, so they decline rather than invent.
Production systems, owned end to end.
We take the whole path: strategy, architecture, build, deploy, operate ??? including evaluation harnesses, drift monitoring, latency budgets, inference cost, audit trails, and being on the call when it breaks at 2am.
AWS & AzureMLOps from day onePoC through productionResearch-backed
20+
Years of engineering and cloud architecture leadership
08
Service lines that combine into whatever the problem needs
06
Regulated and operationally complex industries
1day
Typical turnaround on a new project brief
The difference
Most shops ship a demo. The demo is the easy part.
A proof of concept can be built by anyone in a fortnight, because a proof of concept never has to survive a real user, a compliance review, or a Tuesday when the upstream API is slow.
We own that distance. Every build is informed by what the research lab has actually measured, and the same people who designed the system are the people who run it.
What ships with every system
- An evaluation harness you own, so quality is measurable after we leave
- Drift and accuracy monitoring wired into your existing observability
- A stated latency budget and inference cost per request
- Audit trails and provenance where the domain requires them
- A rehearsed rollback, not a hope that the deploy holds
01 ??? What we build
Eight service lines, combined to fit
Almost nothing real is a single model.
AI agents
Agents that plan, call tools, and recover from failure ??? with tracing, guardrails, and human checkpoints where they matter.
Voice agents
Real-time speech for clinical intake, tutoring, and contact centres, engineered around the latency budget rather than despite it.
AI copilots
Assistants embedded where your team already works, so adoption does not depend on anyone opening a new tab.
Intelligent automation
Document understanding and decision pipelines that remove queues and handoffs, not just individual clicks.
Retrieval & RAG
Grounded answers over your own corpora: hybrid retrieval, reranking, citations, and honest handling of what is not there.
Enterprise AI platforms
Custom platforms shaped to your industry, compliance posture, and scale rather than a vendor’s product roadmap.
AI operating systems
One governed layer across models, agents, data, policy, and observability ??? so AI scales past the pilot team.
02 ??? Industries
Where the stakes are real
We work best in regulated, physically constrained, or operationally messy environments.
Healthcare
Clinical intake, triage support, and diagnostics with privacy handled as a design constraint.
Insurance
Claims analysis, risk scoring, and document intelligence across policy and regulatory corpora.
Education
Voice tutors, adaptive pacing, and multimodal learning tools that hold a student’s attention.
Horticulture & AgTech
IoT-fed prediction for pests, irrigation, and yield across thousands of acres of live operations.
Manufacturing
Quality inspection, anomaly detection, and shop-floor data joined to planning systems.
Public infrastructure
Sensor networks and route optimisation for services that citizens notice when they fail.
03 ??? How we deliver
Five phases, one accountable team
No handoff gaps between strategy, build, and operations.
01
Discover
Opportunity, data readiness, and the honest business case.
02
Design
Architecture, model selection, and a delivery roadmap you can staff.
03
Build
Short iterations with working software and stakeholders in the room.
04
Deploy
Pipelines, cloud hardening, integration, and a rehearsed rollout.
05
Operate
Monitoring, drift response, tuning, and continuous cost control.
Project-based
Defined scope, timeline, and deliverables.
Retainer partnership
A dedicated AI team embedded with yours.
Advisory & architecture
Roadmaps, architecture reviews, and technology selection.
04 ??? Proof
Shipped, measured, still running
Representative outcomes from production deployments.
Healthcare ?? Voice
Medical diagnosis voice agent
HIPAA-aware intake that takes symptoms by voice or text, reasons across multiple models, and hands clinicians a structured triage summary instead of a transcript.
88% concordance
35% less manual triage
Horticulture ?? IoT + ML
Smart horticulture intelligence
Field sensors, weather feeds, and agronomy history combined into outbreak forecasting and irrigation scheduling across commercial growing operations.
12,000+ acres
87% prediction accuracy
30% water saved
Audio AI ?? Deep learning
Real-time voice energy model
A deep learning pipeline estimating voice signal energy live, powering emotion detection and speech quality scoring inside production audio products.
MAE 0.05
R?? 0.92
<100 ms inference
AI educational whiteboard
Multi-model collaborative learning surface joining conversational AI to visual explanation.
40% faster comprehension ?? 60% lower inference costEnterprise workforce AI
Four integrated modules: demand forecasting, anomaly detection, compliance scoring, live alerting.
90%+ forecast accuracy ?? 18-week rolloutSmart city waste management
Five thousand instrumented bins driving dynamic collection routes for a metro-scale population.
2.5M residents served ?? 30% fuel reduction05 ??? Why us
Why teams bring us the hard one
Research-backed, not recycled
Every build draws on what the lab has measured this year.
Production-grade engineering
Systems that hold under real load, with MLOps built in.
Measured on business outcomes
Success is cost, speed, conversion, or revenue.
A real partnership
No black boxes, no disappearing after handoff.
Let's talk
Tell us what you are trying to ship.
Describe the problem, the constraint, and what success would look like.
We reply within one business day