AI is only as good as what it remembers.
RecontX exists to make LLM memory accurate, accountable, and secure.
Language models have become capable enough that the limiting factor is rarely the model. It is what the model is given: which facts, from which sources, how current, and whether anyone put them there on purpose.
Enterprises now run assistants and agents that read years of documents, remember conversations, and act on what they recall. That memory is written by many systems and people, it changes constantly, and it is an attack surface. Most of it was assembled quickly, to make a demo work.
We treat context and memory as infrastructure: designed, measured, secured, and owned by the teams who depend on it.
Principles
Measure before optimizing
Every Engagement starts from a baseline, and every change is judged against it. Opinions about what “feels better” are where evaluation begins, not where it ends.
Memory is a liability as well as an asset
What a system keeps, it has to protect, correct, and eventually delete. We design forgetting as deliberately as remembering.
Every fact has a source
Records carry provenance, so any answer can be traced back to where it came from and who said it.
Security lives in the architecture
Permissions and isolation are enforced by the system that retrieves, never by instructions in a prompt.
Vendor-neutral by default
We work with the models, databases, and clouds you already use, and recommend a change only when the evidence supports it.
You own what we build
Code, schemas, eval suites, and documentation stay with your team. No platform fees and no lock-in.
How we work
Senior engineers, small teams
The people who scope the work are the people who do it. No hand-off to a delivery bench.
Inside your environment
We build in your repositories, your cloud, and your CI, under your access controls and security review.
Built to be handed over
Every Engagement ends with documentation, runbooks, and an eval harness your team can extend without us.
What we don’t do
- We don’t sell a model or a proprietary memory platform.
- We don’t train anything on your data or reuse it across clients.
- We don’t recommend a vendor because of a partnership. We have none.
Working on memory for an LLM application?
Get in touch