Applied AI · Research to Production
AI systems that survive production.
Mindmatrix Holding Limited designs, builds and operates applied AI — language-model applications, tool-using agents, retrieval infrastructure and the evaluation discipline that keeps them honest.
Layer 01
LLM Applications
Product surfaces on frontier models
Layer 02
Agent Systems
Tool use, planning, guardrails
Layer 03
AI Infrastructure
Retrieval, serving, observability
Scope
End to end
Research, build, operate
Models
Multi-vendor
Frontier and open-weight
Delivery
Remote-first
Async, documented, reviewable
Focus
Production
Evaluated, monitored, maintained
The stack we build on
Capabilities
Four things we do well.
Most AI work fails somewhere between a convincing demo and a system people can rely on. These four capabilities are what closes that gap.
LLM Application Development
Product surfaces built on frontier language models.
Learn moreAI Agent Systems
Tool-using systems that plan, act and stay inside their limits.
Learn moreRAG & Knowledge Engineering
Retrieval that holds up against messy, real documents.
Learn moreModel Adaptation & Deployment
Fine-tuning, serving and the operations around them.
Learn moreHow we work
A short path from question to system.
Four stages, each with a decision point. If the evidence says stop, we stop — that is cheaper for everyone than shipping something that does not hold up.
01
Discover
We map the task, the data, the constraints and the failure cost. You get a written problem statement, a feasibility read and a recommendation — including when the honest answer is that AI is the wrong tool.
02
Prototype
A narrow, working slice of the real thing on real data, plus the first evaluation set. The goal is not a demo; it is evidence about whether the approach clears your quality bar.
03
Harden
Guardrails, retries, fallbacks, cost and latency budgets, red-team passes, structured logging. This is the stage most projects skip and most incidents come from.
04
Operate
Deployment, monitoring, regression tracking as models change, and a handover your own engineers can carry. We document ourselves out of a job.
Insights
Notes from the build.
Engineering write-ups on what actually holds up once an AI system meets real users.
Evaluation is the moat, not the model
Everyone can call the same API. What separates a demo from a product is the test suite nobody sees.
ReadRetrieval that survives real documents
Most disappointing RAG systems are retrieval failures wearing a model costume. Fix the retrieval.
ReadFrom prototype to production: an agent hardening checklist
The gap between an agent that works in a demo and one you can let near a customer, itemised.
ReadFAQ
Questions worth asking before we start.
Straight answers to what clients ask us first.
01 What kinds of engagements do you take on?
Three shapes: a scoped build (a defined AI feature or product, delivered and handed over), an embedded engagement (our engineers working inside your team for a period), and a review (an audit of an existing AI system with a written remediation plan). We will tell you which one fits after the first conversation.
02 Which models do you build with?
Whichever clears the bar for your task. We work across frontier APIs — Anthropic Claude, OpenAI and others — and open-weight models you can host yourself. We keep the model layer swappable on purpose, because the right choice moves every few months.
03 How do you handle our data and IP?
Client data stays in client-controlled environments wherever the architecture allows. We sign NDAs before technical discussions, work on your infrastructure or an isolated environment by agreement, and assign IP in the deliverables to you. Model providers are configured for zero data retention where the vendor supports it.
04 Do you work with early-stage teams?
Yes. Early-stage work usually starts as a short discovery plus a prototype, so you learn whether the idea is technically real before committing a budget to it.
05 How do you know an AI feature actually works?
We build the evaluation set before we build the feature: task-specific test cases with expected behaviour, scored automatically, tracked over time. Without it you are shipping on vibes, and vibes do not survive a model upgrade.
06 How do we start?
Email us with the problem you are trying to solve — even loosely defined. We will reply with an honest read on feasibility and what a first engagement would look like. No obligation, and no pitch deck.
Get in touch
Tell us what you are trying to build.
One email is enough to start. Describe the problem in your own words — we will come back with a technical read on whether it is worth building and how we would approach it.
[email protected]