Adaptive coreAdaptive agents
Agents that turn histories and outcomes into evidence-backed proposals for better memory, context, skills, and workflows.
Adaptive systems · Open source at the core
Adaptive intelligence. Accountable by design.
Agents that learn from every outcome—and never make a change you can't see, undo, or measure.
- Gets smarter after every conversation
- Nothing ships without your review
- Runs in your own infrastructure
Self-improving · Human-governed
Learnsfrom every outcome
Reviewedbefore it ships
Deployedin your cloud
Adaptive systems
Intelligence shaped around the work.
Products and solutions that connect real-world workflows, operational knowledge, and measurable outcomes—then keep improving under explicit human control.
Memory, context, tools, policies, and operational state work as one connected system—not isolated AI features.
Context systems
Knowledge graphs, vector recall, and live data assembled into exactly what the current task needs.
Real-time voice
Low-latency conversations embedded in business workflows, with relevant history available as the call unfolds.
Private AI
Pseudonymization, selective erasure, local-first storage, deployment choice, and an auditable control surface.
Context engineering
The right context, assembled in real time.
Relationships from a knowledge graph, semantic matches from vector recall, and live operational signals become one compact, provenance-aware context block.
G
Knowledge graph
Relationships, current truth, and where each fact came from.
V
Vector recall
Semantically relevant evidence across large knowledge estates.
R
Real-time signals
What is happening now across workflows and business systems.
C
Context shaping
Budget-aware assembly: full, summarized, or omitted by priority.
Context assembly / live
“What does this customer need next—and why?”
CONTEXT
FOR NOW
FOR NOW
semantic matchMorning preference
connected factPlan requires review
similar outcomePrevious follow-up
live signalSlot opened today
Hybrid context Meaning plus relationships, with current operational truth.
Semantic evidence and graph-connected facts are assembled together, ranked for this moment, and kept within a defined context budget.
Embedded voice agents
Voice that understands more than the last sentence.
Relevant history and live business context arrive inside the conversation—fast enough to act, confirm, or hand off without breaking the flow.
ListenUnderstandRecallActConfirm
voice session / livelatency budget 50ms
“Could I move my appointment to a morning slot?”
“Yes. I found your preferred clinic and an opening tomorrow at 9:30.”
context recalled in-process4 grains
prefers morningsclinic: centralplan: activeconsent: verified
Appointment change ready to confirmhuman can intervene
Governed learning loop
It improves. A person decides how.
Work becomes history. History becomes evidence. Evidence becomes a proposed change. Every accepted change remains recorded, reversible, and re-measured.
evidence-citedreviewableundoablere-measured
01 / proposalrun history → evidence
A recurring failure becomes a concrete recommendation.
Four of the last eight runs failed at the same attachment step. The proposed change cites each run by hash and cannot execute its own advice.
Evidence4 / 8 related runs failed
Origindeterministic analyzer
Inverseprepared before apply
Waiting for a named reviewerNothing applies itself.
proposedTrust layer
Private by default. Accountable at every step.
Deployment boundary, model provider, retention, erasure, and approval policy remain explicit choices—not hidden assumptions.
Anonymize before the model.
Sensitive identity and quasi-identifiers can be removed or pseudonymized before context reaches a model, while the operational system keeps the authorized mapping.
Context payloadPseudonymized
namePriya Raman
phone+91 98••• 4312
locationChennai · Adyar
preferencemorning appointment
Direct identity and location are removed. Task-relevant preference remains available.
Authority stays visible.
Named approvals, written reasons, replayable execution, stored inverses, and regression checks make accountability part of the runtime.
01
Intent recordedBefore the external effect.
journaled02
Evidence attachedProvenance travels with the proposal.
verified03
Human approvalIdentity and written reason required.
waiting04
Apply and re-measureInverse retained; regression can propose revert.
controlledBuilt in the open
Explore open source Open foundations for serious AI.
Inspect the code. Extend it. Self-host it. Open-source foundations for governed adaptation, embedded voice, and deterministic agent work.
01 / SUBSTRATEMIT · APACHE-2.0
Areev
Governed adaptive-agent infrastructure: memory, context graph, execution history, and a human-gated improvement loop.
View on GitHub02 / VOICEAPACHE-2.0
Flowcat
A self-hosted, native-Rust runtime for real-time voice agents, with in-process SIP/RTP and provider choice.
View on GitHub03 / TOOLSOPEN SOURCE
Agent Tools
Deterministic tools for large files, binary formats, and strict transformations that agents cannot afford to guess.
View on GitHubIndustry solutions
Designed for the realities of the industry.
Specialized experiences preserve the workflows, terminology, handoffs, and trust requirements of each operating environment.
Adaptive AI that earns trust as it improves.
Bring the problem, workflow, or product idea. Shape it into an accountable system ready for the real world.