Lockmere AI Labs
Applied AI for work that must stay private.
We design and ship AI systems, large-scale data infrastructure, and end-to-end encrypted communications — for teams where correctness and confidentiality are not optional extras.
- Deployment
- Self-hosted or cloud
- Encryption
- End-to-end by default
- Protocol
- Open standards, no lock-in
- Handover
- Source and runbooks
Capabilities
Four practices, one engineering standard.
Most engagements draw on more than one. An AI system is only as good as the data platform under it, and neither is worth much if the material moving through them is not protected.
AI Engineering
Production LLM systems — retrieval, agents, and evaluation harnesses — built to survive contact with real users and real data.
- Retrieval pipelines over private corpora
- Tool-using agents with bounded permissions
- Evaluation and regression suites before rollout
- Self-hosted and API-backed model deployments
Data Platform
Ingestion, transformation, and serving layers that stay correct at volume — and stay auditable when someone asks how a number was produced.
- Batch and streaming ingestion
- Warehouse and lakehouse modelling
- Lineage, quality gates, and backfills
- Query layers for analytics and for models
Secure Messaging
A Matrix-based, end-to-end encrypted client and self-hosted homeserver — federated by standard, private by default, yours to operate.
- End-to-end encryption with cross-signed devices
- Self-hosted homeserver on your infrastructure
- Federation you control, or none at all
- Open protocol — no vendor lock-in
Custom Software
Domain platforms for document-heavy, high-consequence work — where the workflow is specific, the data is sensitive, and off-the-shelf does not fit.
- Discovery mapped to the actual workflow
- Systems designed around review and approval
- Access control and audit trails as primitives
- Handover with documentation and runbooks
How we work
Three commitments we do not trade away.
Confidentiality is a design input
Encryption, data residency, and access boundaries get decided in architecture, not retrofitted after a security review.
Evaluated, not demoed
Every AI system ships with a measurable baseline and a regression suite. If we cannot show it got better, we do not claim it did.
You keep the keys
Self-hosting is a first-class deployment target. Source, infrastructure, and operational knowledge transfer to your team.
In-house product
An encrypted messenger you actually own.
We build and operate a Matrix-based client and homeserver. Messages are end-to-end encrypted, devices are cross-signed, and the server runs on your infrastructure — so conversations never transit a vendor you did not choose.
Because it speaks an open protocol, you can federate with partners, keep it fully isolated, or migrate away entirely. That option is the point.
How it is builtFederation identity
User ID
@hello:lockmere.io
Homeserver
matrix.lockmere.io
Transport
Olm / Megolm · cross-signed devices
Delegated from the apex domain, so identities stay short and the homeserver stays movable.
Tell us what you are trying to build.
Send a short description of the problem and the constraints around it — data sensitivity, deadlines, what already exists. We reply with an honest read on scope and whether we are the right team.