Continuous-learning AI for enterprise
Memory that only answers with evidence.
Models that learn without retraining.
Reasera gives your AI a memory it can be held to: every answer carries a receipt, unknowns are declined instead of invented, and a correction sticks. Behind it is a platform of models that learn from your team as they work — live, not next quarter.
Built on continuous-learning research · Patent pending · Per-client isolation · Every answer traceable, every change on the record
The problem
Today’s AI is eloquent and amnesiac.
Language models answer everything with the same confidence, whether they know or not. Retrieval hands them the right document and then lets them say anything anyway. Fine-tuning teaches one fact and damages others. And when something turns out to be wrong, nothing takes it back.
Receipts on every answer
which source, which passage, when it was added
Declines instead of guesses
a certified “no record” instead of an invented answer
Corrections that stick
retract a claim once and reworded copies are refused too
Why not just use RAG?
Same model. Same documents. Different discipline.
We put the same small language model and the same document set on both sides. One side uses standard RAG — retrieval-augmented generation, the usual way to connect a language model to your documents. The other answers through Reasera Memory. Retrieval found the right document almost every time on both sides.
Retrieval was not the problem. What the model is allowed to say next is.
When it answers, how often is it right?
Questions the model could not answer from its own knowledge, with the answer present in the documents. Retrieval found the right document 98% of the time for both sides. Reasera gives 53% of them a certain answer, flags another 32% as lower-confidence, and declines the rest with a reason. (9,792 questions)
Does a correction stick?
Withdrawn claims that stayed withdrawn after the same claim came back in different words and the question was asked again. Reasera retracted the claim; RAG deleted the document instead. Unrelated facts blocked: 12 of 39,270 checks. (6,545 claims, two rewordings each)
Does it admit when it has nothing?
Questions where the subject’s document was in memory but did not contain the answer. Reasera declined 159 of 176 at the certain level and answered 17 wrongly. RAG cannot decline: it answered all 176 from the model’s own recollection — unverifiable, and wrong 22% of the time. (176 questions)
Do you need the big model?
How often it is right when it answers, same questions. The comparison model is sixty times larger, and with RAG it is still bound by what free generation produces. (9,792 questions)
Panels drawn from all 21,919 CounterFact records, one per subject, with Wikipedia summaries as the documents: 9,792 facts the base model gets wrong with the answer present; 176 whose document lacks the answer; 6,545 retractions with two rewordings each. Both sides use the same open 124M-parameter model; the last card compares against an 8-billion-parameter model. Standard RAG = dense embedding retrieval, top document from a 10,650-document pool, free greedy decoding; the same word-level scoring for every arm. Excluding the 1,095 questions whose answer appears in the subject’s own name: 80% vs 22%. Answers flagged lower-confidence are right 53% of the time; your application decides whether to show, caveat, or verify them. On facts both sides already know, the two tie (90% vs 89%). A fixed sample of these panels gates every release.
Results are from Reasera’s fixed evaluation panels, described above, and are re-measured before each release. Performance on your data depends on your sources, configuration, and questions, and is not guaranteed.
How memory works
Three steps. Every one of them auditable.
Add your sources
Documents, transcripts, tickets, knowledge bases. Every addition is an event on a ledger, so the state of your memory at any moment can be rebuilt from it.
Ask, and get a receipt
Each answer carries a certainty level and a receipt: which source, which passage, when it was added. When the evidence isn’t there you get a reason instead of a guess — no source, insufficient evidence, conflicting sources, or retracted.
Curate as you learn
Retract a claim that turned out wrong and it stays retracted, even when the same claim shows up again in different words. Erase what must not exist and the content leaves the ledger too. Both take a person — never an API key.
The platform
Lifelong learning, on your data.
Reasera is the harness that keeps your AI learning after it goes live — from your documents, your team’s corrections, and every interaction — without retraining and without losing what it already knew. Memory holds what your organization knows and proves it. Models that route, classify, and rank learn from every correction as your team works. Everything they learn stays yours: on a ledger, isolated per client, replayable, erasable.
Recall and verify
Memory
Evidence-bound answers with receipts, certified absence, retraction that survives rewording, and erasure that is real.
Product page →Route and adapt
Classifiers
Ticket triage, document sorting, intent detection. Add a category with a single example and it is live in seconds. Every correction updates the live model — no retraining, no data-science team.
Select
Re-ranker
The step that decides what reaches the user or the LLM. Sharpens with every thumbs-up, with no fine-tuning and no retraining project.
How the pieces work together
When memory cannot certify an answer, that gap is a signal, not a dead end. Route it to a specialist model, a tool, or a person — with a classifier that learns new routes as you add them. New client, new product line, new tool: the platform adapts on the request path, not in a retraining project. The family keeps growing: more continuous-learning models from the same research, each held to the same evidence gates.
Built for regulated operations
Isolation, audit, and erasure are enforced — not promised.
Per-client isolation
Each customer gets its own isolated store for memory and models. Your clients' accounts are separated within it.
An auditable ledger
Every learning event — every addition, correction, retraction, and erasure — is recorded. Any past state can be rebuilt from the ledger, minus what you have erased.
Controlled model deployments
Every model deploys the way you choose. Hold new learning at a checkpoint for validation, or let it go live with every correction.
Curation needs a person
Retracting or erasing memory takes a person with the right role. API keys cannot do it, by design.
Your data, your AI.
Everything your AI learns, it learns from you and for you. Your documents and corrections never train a shared model.
Fits your stack
HTTP API, memory-scoped API keys, webhooks, and an MCP server so agents and copilots can use memory directly.
Where Reasera fits
When your AI needs to move as fast as your business.
AI agents and copilots
Give agents a memory that cites its sources and says so when it has none. Available as an MCP server and an HTTP API, so it works with the agent framework you already use.
Customer operations
One platform, many clients, each isolated. Ticket routing that learns from every correction, and a knowledge memory per client that never leaks across accounts.
Regulated knowledge
Finance, healthcare, legal, compliance: answers must be traceable and mistakes must be retractable. Cite-or-decline is the default, and the ledger is the audit trail.
Search and RAG pipelines
Keep your retrieval; add discipline. The re-ranker decides what reaches the model, and memory decides whether the model may answer at all.
See it on your own data.
A 30-minute walkthrough with the founder. Bring a question your AI gets wrong.