Division 01
AI Research Lab
Applied science on real problems
Eight active programmes, each grounded in a system that is already live.
8 programmes active
Enter the lab
Applied Research - Enterprise Delivery
Research at the frontier. Engineering that ships.
AI Tech Partner Labs pairs an applied research lab with an enterprise delivery practice - agentic systems, retrieval, real-time voice and vision, built and operated in production.
Agentic systemsRetrieval & RAGReal-time voiceComputer visionMLOps
Healthcare · Insurance · Education · Horticulture · Manufacturing
08
Active research programmes tied to live systems
20+
Years of engineering and cloud architecture leadership
06
Industries where the stakes are real
1day
Typical turnaround on a new project brief
01 -Divisions
Two practices. One loop.
Research finds what works. Development ships it. Production problems set the next research cycle.
Division 02
AI Development
Production systems, owned end to end
Strategy through operations ??? evaluation harnesses, drift, latency budgets, audit trails, and on-call.
PoC ??? production
See how we deliver
02 -The loop
Research that compounds into delivery
Findings feed the curriculum and the systems we ship. Client problems set the next cycle.
-
01
Research finds the method
A constraint from a live system becomes a programme with a baseline and a harness.
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02
The Academy teaches what held up
Validated findings become curriculum, so every engineer starts from current practice.
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03
Development ships it
The method reaches clients as a production system, with the evaluation set attached.
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04
Production sets the next question
What breaks in the field is the next programme. The loop is the whole point.
03 -Capabilities
Eight service lines, combined to fit
Almost nothing real is a single model. These combine into whatever your problem actually needs.
AI chatbots
Conversational interfaces for support, internal helpdesks, and domain Q&A ??? grounded, so they decline rather than invent.
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.
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.
04 -Industries
Where the stakes are real
Regulated, physically constrained, or operationally messy environments - where a clever demo is not nearly enough.
Healthcare
Clinical intake, triage support, and diagnostics with privacy handled as a design constraint.
Education
Voice tutors, adaptive pacing, and multimodal learning tools that hold a student’s attention.
Manufacturing
Quality inspection, anomaly detection, and shop-floor data joined to planning systems.
Horticulture & AgTech
IoT-fed prediction for pests, irrigation, and yield across thousands of acres of live operations.
05 -Proof
Shipped, measured, still running
A representative selection of production deployments, with the numbers that mattered.
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
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 reduction06 -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
MLOps, monitoring, and security designed in from the first week.
Measured on business outcomes
Success is cost, speed, conversion, or revenue ??? not just model accuracy.
A real partnership
We embed with your team. Your engineers can run everything we build.
07 -How we work
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. Suited to proofs of concept, MVPs, and targeted initiatives.
Retainer partnership
A dedicated AI team embedded with yours across multiple initiatives over quarters.
Advisory & architecture
Roadmaps, architecture reviews, and technology selection for leadership building long-term capability.
08 -Insights
Notes from the lab and the delivery floor
What we measured, what broke, and what it changed.
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Let's talk
Tell us what you are trying to ship.
Describe the problem, the constraint, and what success would look like. You will get a considered response ??? not a generic proposal.
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